图像科学综述外文翻译外文文献英文文献

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外文参考文献翻译-中文

外⽂参考⽂献翻译-中⽂基于4G LTE技术的⾼速铁路移动通信系统KS Solanki教授，Kratika ChouhanUjjain⼯程学院，印度Madhya Pradesh的Ujjain摘要：随着时间发展，⾼速铁路（HSR）要求可靠的，安全的列车运⾏和乘客通信。

为了实现这个⽬标，HSR的系统需要更⾼的带宽和更短的响应时间，⽽且HSR的旧技术需要进⾏发展，开发新技术，改进现有的架构和控制成本。

为了满⾜这⼀要求，HSR采⽤了GSM的演进GSM-R技术，但它并不能满⾜客户的需求。

因此采⽤了新技术LTE-R，它提供了更⾼的带宽，并且在⾼速下提供了更⾼的客户满意度。

本⽂介绍了LTE-R，给出GSM-R与LTE-R之间的⽐较结果，并描述了在⾼速下哪种铁路移动通信系统更好。

关键词：⾼速铁路，LTE，GSM，通信和信令系统⼀介绍⾼速铁路需要提⾼对移动通信系统的要求。

随着这种改进，其⽹络架构和硬件设备必须适应⾼达500公⾥/⼩时的列车速度。

HSR还需要快速切换功能。

因此，为了解决这些问题，HSR 需要⼀种名为LTE-R的新技术，基于LTE-R的HSR提供⾼数据传输速率，更⾼带宽和低延迟。

LTE-R能够处理⽇益增长的业务量，确保乘客安全并提供实时多媒体信息。

随着列车速度的不断提⾼，可靠的宽带通信系统对于⾼铁移动通信⾄关重要。

HSR的应⽤服务质量（QOS）测量，包括如数据速率，误码率（BER）和传输延迟。

为了实现HSR的运营需求，需要⼀个能够与 LTE保持⼀致的能⼒的新系统，提供新的业务，但仍能够与GSM-R长时间共存。

HSR系统选择合适的⽆线通信系统时，需要考虑性能，服务，属性，频段和⼯业⽀持等问题。

4G LTE系统与第三代（3G）系统相⽐，它具有简单的扁平架构，⾼数据速率和低延迟。

在LTE的性能和成熟度⽔平上，LTE- railway（LTE-R）将可能成为下⼀代HSR通信系统。

⼆ LTE-R系统描述考虑LTE-R的频率和频谱使⽤，对为⾼速铁路（HSR）通信提供更⾼效的数据传输⾮常重要。

图像识别中英文对照外文翻译文献

中英文对照外文翻译文献(文档含英文原文和中文翻译)Elastic image matchingAbstractOne fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NP-complete by reduction from 3-SAT, thus giving evidence that the known exponential time algorithms are justified, but approximation algorithms or simplifications are necessary.Keywords: Elastic image matching; Two-dimensional warping; NP-completeness 1. IntroductionIn image recognition, a common problem is to match two given images, e.g. when comparing an observed image to given references. In that pro-cess, elastic image matching, two-dimensional (2D-)warping (Uchida and Sakoe, 1998) or similar types of invariant methods (Keysers et al., 2000) can be used. For this purpose, we can define cost functions depending on the distortion introduced in the matching andsearch for the best matching with respect to a given cost function. In this paper, we show that it is an algorithmically hard problem to decide whether a matching between two images exists with costs below a given threshold. We show that the problem image matching is NP-complete by means of a reduction from 3-SAT, which is a common method of demonstrating a problem to be intrinsically hard (Garey and Johnson, 1979). This result shows the inherent computational difficulties in this type of image comparison, while interestingly the same problem is solvable for 1D sequences in polynomial time, e.g. the dynamic time warping problem in speech recognition (see e.g. Ney et al., 1992). This has the following implications: researchers who are interested in an exact solution to this problem cannot hope to find a polynomial time algorithm, unless P=NP. Furthermore, one can conclude that exponential time algorithms as presented and extended by Uchida and Sakoe (1998, 1999a,b, 2000a,b) may be justified for some image matching applications. On the other hand this shows that those interested in faster algorithms––e.g. for pattern recognition purposes––are right in searching for sub-optimal solutions. One method to do this is the restriction to local optimizations or linear approximations of global transformations as presented in (Keysers et al., 2000). Another possibility is to use heuristic approaches like simulated annealing or genetic algorithms to find an approximate solution. Furthermore, methods like beam search are promising candidates, as these are used successfully in speech recognition, although linguistic decoding is also an NP-complete problem (Casacuberta and de la Higuera, 1999). 2. Image matchingAmong the varieties of matching algorithms,we choose the one presented by Uchida and Sakoe(1998) as a starting point to formalize the problem image matching. Let the images be given as(without loss of generality) square grids of size M×M with gray values (respectively node labels)from a finite alphabet &={1，…，G}. To define thed：&×&→N , problem, two distance functions are needed,one acting on gray valuesg measuring the match in gray values, and one acting on displacement differences :Z×Z→N , measuring the distortion introduced by t he matching. For these distance ddfunctions we assume that they are monotonous functions (computable in polynomial time) of the commonly used squared Euclid-ean distance, i.ed g (g 1,g 2)=f 1(||g 1-g 2||²)and d d (z)=f 2(||z||²) monotonously increasing. Now we call the following optimization problem the image matching problem (let µ={1，…M} ).Instance: The pair( A ; B ) of two images A and B of size M×M .Solution: A mapping function f :µ×µ→µ×µ.Measure:c （A,B,f ）=),(),(j i f ij g B Ad ∑μμ⨯∈),(j i+∑⨯-⋅⋅⋅∈+-+μ}1,{1,),()))0,1(),(())0,1(),(((M j i d j i f j i f dμ⨯-⋅⋅⋅∈}1,{1,),(M j i +∑⋅⋅⋅⨯∈+-+1}-M ,{1,),()))1,0(),(())1,0(),(((μj i d j i f j i f d 1}-M ,{1,),(⋅⋅⋅⨯∈μj iGoal:min f c(A,B,f).In other words, the problem is to find the mapping from A onto B that minimizes the distance between the mapped gray values together with a measure for the distortion introduced by the mapping. Here, the distortion is measured by the deviation from the identity mapping in the two dimensions. The identity mapping fulfills f(i,j)=(i,j),and therefore ，f((i,j)+(x,y))=f(i,j)+(x,y)The corresponding decision problem is fixed by the followingQuestion:Given an instance of image matching and a cost c′, does there exist a ma pping f such that c(A,B,f)≤c′?In the definition of the problem some care must be taken concerning the distance functions. For example, if either one of the distance functions is a constant function, the problem is clearly in P (for d g constant, the minimum is given by the identity mapping and for d d constant, the minimum can be determined by sorting all possible matching for each pixel by gray value cost and mapping to one of the pixels with minimum cost). But these special cases are not those we are concerned with in image matching in general.We choose the matching problem of Uchida and Sakoe (1998) to complete the definition of the problem. Here, the mapping functions are restricted by continuity and monotonicity constraints: the deviations from the identity mapping may locally be at most one pixel (i.e. limited to the eight-neighborhood with squared Euclidean distance less than or equal to 2). This can be formalized in this approach bychoosing the functions f1,f2as e.g.f 1=id，f2(x)=step(x):=⎩⎨⎧.2,)10(,2,0>≤⋅xGxMM3. Reduction from 3-SAT3-SAT is a very well-known NP-complete problem (Garey and Johnson, 1979), where 3-SAT is defined as follows:Instance: Collection of clauses C={C1，···,CK} on a set of variables X={x1， (x)L}such that each ckconsists of 3 literals for k=1，···K .Each literal is a variable or the negation of a variable.Question:Is there a truth assignment for X which satisfies each clause ck, k=1，···K ?The dependency graph D（Ф）corresponding to an instance Ф of 3-SAT is defined to be the bipartite graph whose independent sets are formed by the set of clauses Cand the set of variables X .Two vert ices ck and x1are adjacent iff ckinvolvesx 1or-xL.Given any 3-SAT formula U, we show how to construct in polynomial time anequivalent image matching problem l（Ф）=（A(Ф),B(Ф)）; . The two images of l （Ф）are similar according to the cost function （i.e.f:c(A(Ф),B(Ф),f）≤0) iff the formulaФ is satisfiable. We perform the reduction from 3-SAT using the following steps:• From the formula Ф we construct the dependency graph D（Ф）.• The dependency graph D（Ф）is drawn in the plane.• The drawing of D（Ф）is refined to depict the logical behaviour of Ф , yielding two images（A(Ф),B(Ф)）.For this, we use three types of components: one component to represent variables of Ф , one component to represent clauses of Ф, and components which act as interfaces between the former two types. Before we give the formal reduction, we introduce these components.3.1. Basic componentsFor the reduction from 3-SAT we need five components from which we will construct the in-stances for image matching , given a Boolean formula in 3-DNF,respectively its graph. The five components are the building blocks needed for the graph drawing and will be introduced in the following, namely the representations of connectors,crossings, variables, and clauses. The connectors represent the edges and have two varieties, straight connectors and corner connectors. Each of the components consists of two parts, one for image A and one for image B , where blank pixels are considered to be of the‘background ’color.We will depict possible mappings in the following using arrows indicating the direction of displacement (where displacements within the eight-neighborhood of a pixel are the only cases considered). Blank squares represent mapping to the respective counterpart in the second image.For example, the following displacements of neighboring pixels can be used with zero cost:On the other hand, the following displacements result in costs greater than zero:Fig. 1 shows the first component, the straight connector component, which consists of a line of two different interchanging colors,here denoted by the two symbols◇and□. Given that the outside pixels are mapped to their respe ctive counterparts and the connector is continued infinitely, there are two possible ways in which the colored pixels can be mapped, namely to the left (i.e. f(2,j)=(2,j-1)) or to the right (i.e. f(2,j)=(2,j+1)),where the background pixels have different possibilities for the mapping, not influencing the main property of the connector. This property, which justifies the name ‘connector ’, is the following: It is not possible to find a mapping, which yields zero cost where the relative displacements of the connector pixels are not equal, i.e. one always has f(2,j)-(2,j)=f(2,j')-(2,j'),which can easily be observed by induction over j'.That is, given an initial displacement of one pixel (which will be ±1 in this context), the remaining end of the connector has the same displacement if overall costs of the mapping are zero. Given this property and the direction of a connector, which we define to be directed from variable to clause, wecan define the state of the connector as carrying the‘true’truth value, if the displacement is 1 pixel in the direction of the connector and as carrying the‘false’ truth value, if the displacement is -1 pixel in the direction of the connector. This property then ensures that the truth value transmitted by the connector cannot change at mappings of zero cost.Image A image Bmapping 1 mapping 2Fig. 1. The straight connector component with two possible zero cost mappings.For drawing of arbitrary graphs, clearly one also needs corners,which are represented in Fig. 2.By considering all possible displacements which guarantee overall cost zero, one can observe that the corner component also ensures the basic connector property. For example, consider the first depicted mapping, which has zero cost. On the other hand, the second mapping shows, that it is not possible to construct a zero cost mapping with both connectors‘leaving’the component. In that case, the pixel at the position marked‘? ’either has a conflict (that i s, introduces a cost greater than zero in the criterion function because of mapping mismatch) with the pixel above or to the right of it,if the same color is to be met and otherwise, a cost in the gray value mismatch term is introduced.image A image Bmapping 1 mapping 2Fig. 2. The corner connector component and two example mappings.Fig. 3 shows the variable component, in this case with two positive (to the left) and one negated output (to the right) leaving the component as connectors. Here, a fourth color is used, denoted by ·.This component has two possible mappings for thecolored pixels with zero cost, which map the vertical component of the source image to the left or the right vertical component in the target image, respectively. (In both cases the second vertical element in the target image is not a target of the mapping.) This ensures±1 pixel relative displacements at the entry to the connectors. This property again can be deducted by regarding all possible mappings of the two images.The property that follows (which is necessary for the use as variable) is that all zero cost mappings ensure that all positive connectors carry the same truth value,which is the opposite of the truth value for all the negated connectors. It is easy to see from this example how variable components for arbitrary numbers of positive and negated outputs can be constructed.image A image BImage C image DFig. 3. The variable component with two positive and one negated output and two possible mappings (for true and false truth value).Fig. 4 shows the most complex of the components, the clause component. This component consists of two parts. The first part is the horizontal connector with a 'bend' in it to the right.This part has the property that cost zero mappings are possible for all truth values of x and y with the exception of two 'false' values. This two input disjunction，can be extended to a three input dis-junction using the part in the lower left. If the z connector carries a 'false' truth value, this part can only be mapped one pixel downwards at zero cost.In that case the junction pixel (the fourth pixel in the third row) cannot be mapped upwards at zero cost and the 'two input clause' behaves as de-scribed above. On the other hand, if the z connector carries a 'true' truth value, this part can only be mapped one pixel upwards at zero cost,and the junction pixel can be mapped upwards,thus allowing both x and y to carry a 'false' truth value in a zero cost mapping. Thus there exists a zero cost mapping of the clause component iff at least one of the input connectors carries a truth value.image Aimage B mapping 1(true,true,false)mapping 2 (false,false,true,)Fig. 4. The clause component with three incoming connectors x, y , z and zero cost mappings forthe two cases(true,true,false)and (false, false, true).The described components are already sufficient to prove NP-completeness by reduction from planar 3-SAT (which is an NP-complete sub-problem of 3-SAT where the additional constraints on the instances is that the dependency graph is planar),but in order to derive a reduction from 3-SAT, we also include the possibility of crossing connectors.Fig. 5 shows the connector crossing, whose basic property is to allow zero cost mappings if the truth–values are consistently propagated. This is assured by a color change of the vertical connector and a 'flexible' middle part, which can be mapped to four different positions depending on the truth value distribution.image Aimage Bzero cost mappingFig. 5. The connector crossing component and one zero cost mapping.3.2. ReductionUsing the previously introduced components, we can now perform the reduction from 3-SAT to image matching .Proof of the claim that the image matching problem is NP-complete:Clearly, the image matching problem is in NP since, given a mapping f and two images A and B ,the computation of c（A,B,f）can be done in polynomial time. To prove NP-hardness, we construct a reduction from the 3-SAT problem. Given an instance of 3-SAT we construct two images A and B , for which a mapping of cost zero exists iff all the clauses can be satisfied.Given the dependency graph D ,we construct an embedding of the graph into a 2D pixel grid, placing the vertices on a large enough distance from each other (say100（K+L）² ).This can be done using well-known methods from graph drawing (see e.g.di Battista et al.,1999).From this image of the graph D we construct the two images A and B , using the components described above.Each vertex belonging to a variable is replaced with the respective parts of the variable component, having a number of leaving connectors equal to the number of incident edges under consideration of the positive or negative use in the respective clause. Each vertex belonging to a clause is replaced by the respective clause component,and each crossing of edges is replaced by the respective crossing component. Finally, all the edges are replaced with connectors and corner connectors, and the remaining pixels inside the rectangular hull of the construction are set to the background gray value. Clearly, the placement of the components can be done in such a way that all the components are at a large enough distance from each other, where the background pixels act as an 'insulation' against mapping of pixels, which do not belong to the same component. It can be easily seen, that the size of the constructed images is polynomial with respect to the number of vertices and edges of D and thus polynomial in the size of the instance of 3-SAT, at most in the order （K+L）².Furthermore, it can obviously be constructed in polynomial time, as the corresponding graph drawing algorithms are polynomial.Let there exist a truth assignment to the variables x1，…，xL, which satisfies allthe clauses c1，…，cK. We construct a mapping f , that satisfies c（f,A,B）=0 asfollows.For all pixels (i, j ) belonging to variable component l with A（i，j）not of the background color,set f（i，j）=（i，j-1）if xlis assigned the truth value 'true' , set f（i，j）=（i，j+1）, otherwise. For the remaining pixels of the variable component set A（i，j）=B（i，j），if f（i，j）=（i，j）, otherwise choose f（i，j）from{(i,j+1),(i+1,j+1),(i-1,j+1)}for xl'false' respectively from {(i,j-1),(i+1,j-1),(i-1,j-1)}for xl'true ',such that A（i，j）=B（f（i，j））. This assignment is always possible and has zero cost, as can be easily verified.For the pixels（i，j）belonging to (corner) connector components,the mapping function can only be extended in one way without the introduction of nonzero cost,starting from the connection with the variable component. This is ensured by thebasic connector property. By choosing f （i ，j ）=（i,j ）for all pixels of background color, we obtain a valid extension for the connectors. For the connector crossing components the extension is straight forward, although here ––as in the variable mapping ––some care must be taken with the assign ment of the background value pixels, but a zero cost assignment is always possible using the same scheme as presented for the variable mapping.It remains to be shown that the clause components can be mapped at zero cost, if at least one of the input connectors x , y , z carries a ' true' truth value.For a proof we regard alls even possibilities and construct a mapping for each case. In thedescription of the clause component it was already argued that this is possible,and due to space limitations we omit the formalization of the argument here.Finally, for all the pixels （i ，j ）not belonging to any of the components, we set f （i ，j ）=（i ，j ）thus arriving at a mapping function which has c （f ，A ，B ）=0。

关于机器视觉的外文文献

关于机器视觉的外文文献Title: Overview of Machine Vision: Technologies and ApplicationsAbstract:Machine vision is an important technology that enables computers to interpret visual information from images or videos. This paper provides an overview of the technologies behind machine vision and their various applications. Thefirst section covers the basics of machine vision, including image acquisition, processing, and analysis. The secondsection discusses some common machine vision techniques suchas pattern recognition, object detection and classification. The third section gives examples of machine vision applications, including manufacturing, medical diagnosis, and surveillance.Introduction:Machine vision has become increasingly important in many fields, from manufacturing to medical diagnosis to robotics.It is a technology that allows computers to interpret visual information from images or videos, enabling them to make automated decisions based on that information. This paperwill provide an overview of machine vision technologies and their applications.Section 1: Basics of Machine Vision1.1 Image Acquisition: This section discusses the different ways in which images or videos can be captured and stored for machine vision applications. It covers topics such as cameras, sensors, and storage devices.1.2 Image Processing: This section explains the different methods used to clean up and enhance images before analysis. Techniques such as filtering and image segmentation are discussed.1.3 Image Analysis: This section discusses the different algorithms used to analyze images and extract important features such as edges, corners, and textures.Section 2: Machine Vision Techniques2.1 Pattern Recognition: This section covers the basics of pattern recognition and its importance in machine vision. Methods such as template matching and machine learning are discussed.2.2 Object Detection: This section explains the different ways in which objects can be detected in a scene, including feature-based methods and deep learning.2.3 Object Classification: This section covers the different algorithms used to classify objects based on their features or attributes. Methods such as decision trees and support vector machines are discussed.Section 3: Applications of Machine Vision3.1 Manufacturing: This section discusses the various applications of machine vision in manufacturing, including quality control, inspection, and assembly.3.2 Medical Diagnosis: This section covers the different ways in which machine vision can be used for medical diagnosis, including pathology, radiology, and ophthalmology.3.3 Surveillance: This section explains the different ways in which machine vision can be used for surveillance, including face recognition and crowd monitoring.Conclusion:Machine vision is a powerful technology that is becomingincreasingly important in many fields. With its ability to interpret visual information and make automated decisions based on that data, it has the potential to revolutionize the way we live and work. As the technology continues to evolve and improve, we can expect to see even more exciting applications of machine vision in the future.。

【图像复原技术研究文献综述2000字】

图像复原技术研究国内外文献综述作为日常生活中广泛使用的技术，图像修复技术汇集了国内外许多重要技术。

实际上，图像复原分为三种标准：首先是搭建其劣化图像的图像模型；其次去研究和筛选最佳的图像复原方法；最后进行图像复原。

所有类型的成像模型和优化规则都会导致应用于不同领域的不同图像恢复算法。

我们对现有的图像复原方法大致做了总结，如利用线性代数知识的线性代数复原技术、搭建图像退化模型的去卷积图像恢复技术以及不考虑PSF的图像盲解卷积算法等。

其中，去卷积方法主要包括功率谱均衡、Wiener滤波和几何平均滤波等，然而这些方法需要许多预信息和噪声稳定假设，这在现实当中我们不可能利用计算机去做到的的事情，因此它们只适用于线性空间不变的理想系统，仅当噪声与信号无关时才能达到很好的效果。

但是在一些条件恶化的情况下，就不能满足图像修复的效果了。

在图像恢复领域当中，另一个重要且常见的方法是盲去卷积复原法。

它的优势是在没有预先了解退化函数和实际信号的知识前提下，可以根据劣化图像直接估计劣化函数和初始信号。

实际上，现在有几个算法通过不充分的预测信息来恢复劣化图像。

由于我们需要对图像和点扩展函数的一些相关信息进行假设和推导，所以这就导致图像恢复的解通常并不存在唯一性，并且我们已知的初始条件和对附加图像假设的选择也会对解的优劣产生很大的关系。

与此同时，由于信道中不可避免的加性噪声的影响，会进一步导致盲图像的复原变差，给图像复原造成许多困难。

也就是说，如果我们直接利用点扩展函数进行去卷积再现初始图像，则一般会导致高频噪声放大，导致算法的性能恶化，恢复不出清晰的图像。

因此，我们要尽可能的提高图像的信噪比，从而提高图像复原的效果。

基于已知的降质算子和加性噪声的某些统计性质从而恢复清晰的图像，我们将这种方法叫做线性代数恢复方法，并且这种线性代数恢复方法在一定程度上提出了用于恢复滤波器的数值计算从而使得模糊图像恢复到与清晰图像一致的新的设计思想。

医学影像学英文文献

医学影像学英文文献英文回答：Within the realm of medical imaging, sophisticated imaging techniques empower healthcare professionals with the ability to visualize and comprehend anatomical structures and physiological processes in the human body. These techniques are instrumental in diagnosing diseases, guiding therapeutic interventions, and monitoring treatment outcomes.Computed tomography (CT) and magnetic resonance imaging (MRI) are two cornerstone imaging modalities widely employed in medical practice. CT utilizes X-rays and advanced computational algorithms to generate cross-sectional images of the body, providing detailed depictions of bones, soft tissues, and blood vessels. MRI, on the other hand, harnesses the power of powerful magnets and radiofrequency waves to create intricate images that excel in showcasing soft tissue structures, including the brain,spinal cord, and internal organs.Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are nuclear medicine imaging techniques that involve the administration of radioactive tracers into the body. These tracers accumulate in specific organs or tissues, enabling the visualization and assessment of metabolic processes and disease activity. PET is particularly valuable in oncology, as it can detect the presence and extent of cancerous lesions.Ultrasound, also known as sonography, utilizes high-frequency sound waves to produce images of internal structures. It is a versatile technique commonly employed in obstetrics, cardiology, and abdominal imaging. Ultrasound offers real-time visualization, making it ideal for guiding procedures such as biopsies and injections.Interventional radiology is a specialized field that combines imaging guidance with minimally invasive procedures. Interventional radiologists utilize imaging techniques to precisely navigate catheters and otherinstruments through the body, enabling the diagnosis and treatment of conditions without the need for open surgery. This approach offers reduced invasiveness and faster recovery times compared to traditional surgical interventions.Medical imaging has revolutionized healthcare by providing invaluable insights into the human body. The ability to visualize anatomical structures andphysiological processes in exquisite detail has transformed the practice of medicine, leading to more accurate diagnoses, targeted treatments, and improved patient outcomes.中文回答：医学影像学是现代医学不可或缺的一部分，它利用各种成像技术对人体的解剖结构和生理过程进行可视化和理解，在疾病诊断、治疗方案制定和治疗效果评估中发挥着至关重要的作用。

电气工程及其自动化专业外文文献英文文献外文翻译 plc方面

1、外文原文（复印件）A: Fundamentals of Single-chip MicrocomputerTh e si ng le-ch i p mi cr oc om pu ter is t he c ul mi nat i on o f bo th t h e d ev el op me nt o f th e d ig it al com p ut er an d t he int e gr at ed ci rc ui ta r gu ab ly th e t ow m os t s i gn if ic ant i nv en ti on s o f t h e 20t h c en tu ry[1].Th es e to w typ e s of a rc hi te ctu r e ar e fo un d i n s in gl e-ch ip m i cr oc om pu te r. So m e em pl oy t he sp l it p ro gr am/d ata me mo ry o f th e H a rv ar d ar ch it ect u re, sh ow n i n -5A, ot he rs fo ll ow th e ph i lo so ph y, w i de ly a da pt ed fo r g en er al-p ur pos e c om pu te rs an d m i cr op ro ce ss or s, o f m a ki ng no lo gi c al di st in ct io n b e tw ee n p ro gr am a n d da t a m em ory a s i n th e Pr in cet o n ar ch it ec tu re,sh ow n in-5A.In g en er al te r ms a s in gl e-chi p m ic ro co mp ut er i sc h ar ac te ri zed b y the i nc or po ra tio n of al l t he uni t s o f a co mp ut er i n to a s in gl e dev i ce, as s ho wn in Fi g3-5A-3.-5A-1 A Harvard type-5A. A conventional Princeton computerFig3-5A-3. Principal features of a microcomputerRead only memory (ROM).R OM i s u su al ly f or th e p er ma ne nt, n o n-vo la ti le s tor a ge o f an a pp lic a ti on s pr og ra m .M an ym i cr oc om pu te rs an d mi cr oc on tr ol le r s a re in t en de d fo r h ig h-v ol ume a p pl ic at io ns a nd h en ce t he e co nom i ca l ma nu fa ct ure of t he d ev ic es r e qu ir es t ha t the co nt en ts o f the pr og ra m me mo ry b e co mm it te dp e rm an en tl y d ur in g th e m an uf ac tu re o f c hi ps . Cl ear l y, th is im pl ie sa ri g or ou s a pp roa c h t o R OM co de d e ve lo pm en t s in ce c ha ng es ca nn otb e m ad e af te r man u fa ct ur e .T hi s d e ve lo pm en t pr oce s s ma y in vo lv e e m ul at io n us in g a s op hi st ic at ed deve lo pm en t sy st em w i th a ha rd wa re e m ul at io n ca pa bil i ty a s we ll a s th e u se of po we rf ul so ft wa re t oo ls.So me m an uf act u re rs p ro vi de ad d it io na l RO M opt i on s byi n cl ud in g i n th ei r ra ng e de vi ce s wi th (or i nt en de d fo r us e wi th) u s er pr og ra mm ab le m em or y. Th e s im p le st of th es e i s us ua ll y d ev ice w h ic h ca n op er ate in a m ic ro pr oce s so r mo de b y usi n g so me o f th e i n pu t/ou tp ut li ne s as a n ad dr es s an d da ta b us f or acc e ss in g e xt er na l m e mo ry. T hi s t ype o f d ev ic e c an b e ha ve fu nc ti on al l y a s t he si ng le c h ip mi cr oc om pu te r fr om wh ic h i t i s de ri ve d a lb eit w it h r es tr ic ted I/O an d a mo di fie d e xt er na l ci rcu i t. T he u se o f t h es e RO Ml es sd e vi ce s is c om mo n e ve n in p ro du ct io n c ir cu it s wh er e t he v ol um e do es n o t ju st if y th e d e ve lo pm en t co sts of c us to m on-ch i p RO M[2];t he re c a n st il l b e a si g ni fi ca nt s a vi ng in I/O a nd ot he r c hi ps co mp ar ed t o a c on ve nt io nal mi cr op ro ce ss or b as ed c ir cu it. M o re e xa ctr e pl ac em en t fo r RO M d ev ic es c an b e o bt ai ne d in t he f o rm o f va ri an ts w i th 'pi gg y-ba ck'EP RO M(Er as ab le p ro gr am ma bl e ROM)s oc ke ts o rd e vi ce s w it h EP ROM i ns te ad o f R OM 。

自动化专业-外文文献-英文文献-外文翻译-plc方面

1、外文原文（复印件)A: Fundamentals of Single-chip MicrocomputerTh e si ng le-ch i p mi cr oc om pu ter is t he c ul mi nat i on o f bo th t h e d ev el op me nt o f th e d ig it al com p ut er an d t he int e gr at ed ci rc ui ta r gu ab ly th e t ow m os t s i gn if ic ant i nv en ti on s o f t h e 20t h c en tu ry[1].Th es e to w t ype s o f a rc hi te ct ur e a re fo un d i n s i ng le—ch ip m i cr oc om pu te r。

S o me em pl oy th e s p li t p ro gr am/d at a me mo ry of t he H a rv ar d ar ch it ect u re, sh ow n in Fi g.3-5A—1，ot he r s fo ll ow t hep h il os op hy, wi del y a da pt ed f or ge n er al—pu rp os e c o mp ut er s an dm i cr op ro ce ss or s, of ma ki ng no lo gi c al di st in ct io n be tw ee n p ro gr am a n d da ta m em or y a s i n th e Pr in cet o n ar ch it ec tu re,sh ow n in F ig。

3-5A-2.In g en er al te r ms a s in gl e—ch i p mi cr oc om pu ter isc h ar ac te ri zed b y the i nc or po ra tio n of al l t he uni t s o f a co mp ut er i n to a s in gl e de v i ce，as s ho wn i n F ig3—5A—3。

图像融合技术外文翻译-中英对照

***大学毕业设计（英文翻译）原文题目：Automatic Panoramic Image Stitching using Invariant Features 译文题目：使用不变特征的全景图像自动拼接学院：电子与信息工程学院专业： ********姓名： ******学号：**********使用不变特征的全景图像自动拼接马修·布朗和戴维•洛{mbrown|lowe}@cs.ubc.ca计算机科学系英国哥伦比亚大学加拿大温哥华摘要本文研究全自动全景图像的拼接问题，尽管一维问题（单一旋转轴）很好研究，但二维或多行拼接却比较困难。

以前的方法使用人工输入或限制图像序列，以建立匹配的图像，在这篇文章中，我们假定拼接是一个多图像匹配问题，并使用不变的局部特征来找到所有图像的匹配特征。

由于以上这些，该方法对输入图像的顺序、方向、尺度和亮度变化都不敏感；它也对不属于全景图一部分的噪声图像不敏感，并可以在一个无序的图像数据集中识别多个全景图。

此外，为了提供更多有关的细节，本文通过引入增益补偿和自动校直步骤延伸了我们以前在该领域的工作。

1. 简介全景图像拼接已经有了大量的研究文献和一些商业应用。

这个问题的基本几何学很好理解，对于每个图像由一个估计的3×3的摄像机矩阵或对应矩阵组成。

估计处理通常由用户输入近似的校直图像或者一个固定的图像序列来初始化，例如，佳能数码相机内的图像拼接软件需要水平或垂直扫描，或图像的方阵。

在自动定位进行前，第4版的REALVIZ拼接软件有一个用户界面，用鼠标在图像大致定位，而我们的研究是有新意的，因为不需要提供这样的初始化。

根据研究文献，图像自动对齐和拼接的方法大致可分为两类——直接的和基于特征的。

直接的方法有这样的优点，它们使用所有可利用的图像数据，因此可以提供非常准确的定位，但是需要一个只有细微差别的初始化处理。

基于特征的配准不需要初始化，但是缺少不变性的传统的特征匹配方法（例如，Harris角点图像修补的相关性）需要实现任意全景图像序列的可靠匹配。

matlab图像处理外文翻译外文文献

matlab图像处理外文翻译外文文献附录A 英文原文Scene recognition for mine rescue robotlocalization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)Abstract：A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landmarks are organized by using HMM to represent the scene where the robot is, and fuzzy logic strategy is used to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluation problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model1 IntroductionSearch and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hybrid ones[2]. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization.Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online.。

外文翻译----数字图像处理方法的研究(中英文)(1)

The research of digital image processing technique1IntroductionInterest in digital image processing methods stems from two principal application areas:improvement of pictorial information for human interpretation;and processing of image data for storage,transmission,and representation for autonomous machine perception.1.1What Is Digital Image Processing?An image may be defined as a two-dimensional function,f(x,y),where x and y are spatial(plane)coordinates,and the amplitude of f at any pair of coordinates(x,y)is called the intensity or gray level of the image at that point.When x,y,and digital image.The field of digital image processing refers to processing digital images by means of a digital computer.Note that a digital image is composed of a finite number of elements,each of which has a particular location and value.These elements are referred to as picture elements,image elements,pels,and pixels.Pixel is the term most widely used to denote the elements of a digital image.We consider these definitions in more formal terms in Chapter2.Vision is the most advanced of our senses,so it is not surprising that images play the single most important role in human perception.However,unlike human who are limited to the visual band of the electromagnetic(EM)spectrum,imaging machines cover almost the entire EM spectrum,ranging from gamma to radio waves.They can operate on images generated by sources that human are not accustomed to associating with image.These include ultrasound,electron microscopy,and computer-generated images.Thus,digital image processing encompasses a wide and varied field of application.There is no general agreement among authors regarding where image processing stops and other related areas,such as image analysis and computer vision,start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images.We believe this to be a limiting and somewhat artificial boundary.For example,under this definition,even the trivial task of computing the average intensity of an image(which yields a single number)would not be considered an image processing operation.On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to emulate human vision,including learning and being able to make inferences and take actions based on visual inputs.This area itself is a branch of artificial intelligence(AI) whose objective is to emulate human intelligence.This field of AI is in its earliest stages of infancy in terms of development,with progress having been much slower than originally anticipated.The area of image analysis(also called image understanding)is in between image processing and computer vision.There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other.However,one useful paradigm is to consider three types of computerized processes is this continuum:low-,mid-,and high-ever processes.Low-level processes involve primitive operation such as image preprocessing to reduce noise,contrast enhancement,and image sharpening.A low-level process is characterized by the fact that both its input and output are images. Mid-level processing on images involves tasks such as segmentation(partitioning an image into regions or objects),description of those objects to reduce them to a form suitable for computer processing,and classification(recognition)of individual object. Amid-level process is characterized by the fact that its inputs generally are images, but its output is attributes extracted from those images(e.g.,edges contours,and the identity of individual object).Finally,higher-level processing involves“making sense”of an ensemble of recognized objects,as in image analysis,and,at the far end of the continuum,performing the cognitive function normally associated with vision. Based on the preceding comments,we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image.Thus,what we call in this book digital image processing encompasses processes whose inputs and outputs are images and,in addition, encompasses processes that extract attributes from images,up to and including the recognition of individual objects.As a simple illustration to clarify these concepts, consider the area of automated analysis of text.The processes of acquiring an image of the area containing the text.Preprocessing that images,extracting(segmenting)the individual characters,describing the characters in a form suitable for computer processing,and recognizing those individual characters are in the scope of what we call digital image processing in this book.Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement“making cense.”As will become evident shortly,digital image processing,as we have defined it,is used successfully in a broad rang of areas of exceptional social and economic value.The concepts developed in the following chapters are the foundation for the methods used in those application areas.1.2The Origins of Digital Image ProcessingOne of the first applications of digital images was in the newspaper industry,when pictures were first sent by submarine cable between London and NewYork. Introduction of the Bartlane cable picture transmission system in the early1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three hours.Specialized printing equipment coded pictures for cable transmission and then reconstructed them at the receiving end.Figure 1.1was transmitted in this way and reproduced on a telegraph printer fitted with typefaces simulating a halftone pattern.Some of the initial problems in improving the visual quality of these early digital pictures were related to the selection of printing procedures and the distribution ofintensity levels.The printing method used to obtain Fig.1.1was abandoned toward the end of1921in favor of a technique based on photographic reproduction made from tapes perforated at the telegraph receiving terminal.Figure1.2shows an images obtained using this method.The improvements over Fig.1.1are evident,both in tonal quality and in resolution.FIGURE1.1A digital picture produced in FIGURE1.2A digital picture 1921from a coded tape by a telegraph printer made in1922from a tape punched With special type faces(McFarlane)after the signals had crossed theAtlantic twice.Some errors areVisible.(McFarlane)The early Bartlane systems were capable of coding images in five distinct level of gray.This capability was increased to15levels in1929.Figure1.3is typical of the images that could be obtained using the15-tone equipment.During this period, introduction of a system for developing a film plate via light beams that were modulated by the coded picture tape improved the reproduction process considerably. Although the examples just cited involve digital images,they are not considered digital image processing results in the context of our definition because computer were not involved in their creation.Thus,the history of digital processing is intimately tied to the development of the digital computer.In fact digital images require so much storage and computational power that progress in the field of digital image processing has been dependent on the development of digital computers of supporting technologies that include data storage,display,and transmission.The idea of a computer goes back to the invention of the abacus in Asia Minor, more than5000years ago.More recently,there were developments in the past two centuries that are the foundation of what we call computer today.However,the basis for what we call a modern digital computer dates back to only the1940s with the introduction by John von Neumann of two key concepts:(1)a memory to hold a stored program and data,and(2)conditional branching.There two ideas are the foundation of a central processing unit(CPU),which is at the heart of computer today. Starting with von Neumann,there were a series of advances that led to computers powerful enough to be used for digital image processing.Briefly,these advances maybe summarized as follow:(1)the invention of the transistor by Bell Laboratories in1948;(2)the development in the1950s and1960s of the high-level programminglanguages COBOL(Common Business-Oriented Language)and FORTRAN (Formula Translator);(3)the invention of the integrated circuit(IC)at Texas Instruments in1958;(4)the development of operating system in the early1960s;(5)the development of the microprocessor(a single chip consisting of the centralprocessing unit,memory,and input and output controls)by Inter in the early 1970s;(6)introduction by IBM of the personal computer in1981;(7)progressive miniaturization of components,starting with large scale integration(LI)in the late1970s,then very large scale integration(VLSI)in the1980s,to the present use of ultra large scale integration(ULSI).Figure1.3In1929from London to Cenerale Pershingthat New York delivers with15level tone equipmentsthrough cable with Foch do not the photograph by decorationConcurrent with these advances were development in the areas of mass storage and display systems,both of which are fundamental requirements for digital image processing.The first computers powerful enough to carry out meaningful image processing tasks appeared in the early1960s.The birth of what we call digital image processing today can be traced to the availability of those machines and the onset of the apace program during that period.It took the combination of those two developments to bring into focus the potential of digital image processing concepts.Work on using computer techniques for improving images from a space probe began at the Jet Propulsion Laboratory(Pasadena,California)in1964when pictures of the moontransmitted by Ranger7were processed by a computer to correct various types of image distortion inherent in the on-board television camera.Figure1.4shows the first image of the moon taken by Ranger7on July31,1964at9:09A.M.Eastern Daylight Time(EDT),about17minutes before impacting the lunar surface(the markers,called reseau mark,are used for geometric corrections,as discussed in Chapter5).This also is the first image of the moon taken by a U.S.spacecraft.The imaging lessons learned with ranger7served as the basis for improved methods used to enhance and restore images from the Surveyor missions to the moon,the Mariner series of flyby mission to Mars,the Apollo manned flights to the moon,and others.In parallel with space application,digital image processing techniques began in the late1960s and early1970s to be used in medical imaging,remote Earth resources observations,and astronomy.The invention in the early1970s of computerized axial tomography(CAT),also called computerized tomography(CT)for short,is one of the most important events in the application of image processing in medical diagnosis. Computerized axial tomography is a process in which a ring of detectors encircles an object(or patient)and an X-ray source,concentric with the detector ring,rotates about the object.The X-rays pass through the object and are collected at the opposite end by the corresponding detectors in the ring.As the source rotates,this procedure is repeated.Tomography consists of algorithms that use the sensed data to construct an image that represents a“slice”through the object.Motion of the object in a direction perpendicular to the ring of detectors produces a set of such slices,which constitute a three-dimensional(3-D)rendition of the inside of the object.Tomography was invented independently by Sir Godfrey N.Hounsfield and Professor Allan M. Cormack,who shared the X-rays were discovered in1895by Wilhelm Conrad Roentgen,for which he received the1901Nobel Prize for Physics.These two inventions,nearly100years apart,led to some of the most active application areas of image processing today.Figure1.4The first picture of the moon by a U.S.Spacecraft.Ranger7took this image on July31,1964at9:09A.M.EDT,about17minutes beforeImpacting the lunar surface.(Courtesy of NASA.)中文翻译数字图像处理方法的研究1绪论数字图像处理方法的研究源于两个主要应用领域:其一是为了便于人们分析而对图像信息进行改进；其二是为了使机器自动理解而对图像数据进行存储、传输及显示。

图像处理文献综述

文献综述近年来，随着计算机视觉技术的日益发展，图像处理作为该领域的关键方向受到越来越多研究人员的关注与思考。

在现在的日常生活中，由于通信设备低廉的价格和便捷的操作，人们越来越喜欢用图像和视频来进行交流和分享，消费性的电子产品在消费者中已经非常普遍，例如移动手机和数码相机等等。

在这个纷繁多变的世界，每天都有数以万计的图像产生，同时信息冗余问题也随之而来。

尽管在一定的程度上，内存技术的增加和网络带宽的提高解决了图像的压缩和传输问题，但是智能的图像检索和有效的数据存储，以及图像内容的提取依然没有能很好的解决。

视觉注意机制可以被看做是人类对视觉信息的一个筛选过程，也就是说只有一小部分重要的信息能够被大脑进行处理。

人类在观察一个场景时，他们往往会将他们的注意力集中在他们感兴趣的区域，例如拥有鲜艳的颜色，光滑的亮度，特殊的形状以及有趣的方位的区域。

传统的图像处理方法是将整幅图像统一的处理，均匀的分配计算机资源；然而许多的视觉任务仅仅只关系图像中的一个或几个区域，统一的处理整幅图像很明显会浪费过多的计算机资源，减少处理的效率[1,2]。

因此，在计算机视觉领域，建立具有人类视觉系统独特数据筛选能力的数学模型显得至关重要。

受高效的视觉信息处理机制的启发，计算机视觉领域的显著性检测应运而生。

图像显著性检测是通过建立一定的数学模型，让计算机来模拟人类的视觉系统，使得计算机能够准确高效的定位到感兴趣的区域。

一般来说，一个信号的显著性可以表示为其和周围环境的差异性。

正是因为这个信号和周围的其他信号的迥异性，使得视觉系统不需要对环境中的所有感兴趣的区域进行逐个的扫描，显著的目标会自动从环境中凸显出来。

另外，一些心理学研究表明人类的视觉机制不仅仅是由低级的视觉信号来驱动的，基于记忆、经验等的先验知识同样能够决定场景中的不同信号的显著性，而这些先验知识往往是和一些高层次的事件以及视觉任务联系在一起的。

基于当前场景的视觉显著性机制是低级的，慢速的。

数字图像处理论文中英文对照资料外文翻译文献

第 1 页中英文对照资料外文翻译文献原文To image edge examination algorithm researchAbstract ：Digital image processing took a relative quite young discipline,is following the computer technology rapid development, day by day obtains th widespread application.The edge took the image one kind of basic characteristic,in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widesp application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develo the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainlyhas Robert, Laplacian, Sobel, Canny, operators and so on LOG 。

数字图像处理文献综述

医学图像增强处理与分析【摘要】医学图像处理技术作为医学成像技术的发展基础，带动着现代医学诊断产生着深刻的变革。

图像增强技术在医学数字图像的定量、定性分析中扮演着重要的角色，它直接影响到后续的处理与分析工作。

本文以医学图像(主要为X光、CT、B超等医用透视图像)为主要的研究对象，研究图像增强技术在医学图像处理领域中的应用。

本文通过对多种图像增强方法的图像处理效果进行了比较和验证，最后总结出了针对医学图像的各项特点最有效的图像增强处理方法。

关键词：医学图像处理；图像增强；有效方法；Medical Image has been an important supplementary measure of the doctor's diagnosis and treatment. As the developmental foundation of these imaging technology, Medical Image Processing leads to profoundly changes of modern medical diagnosis. Image enhancement technology plays an important role in quantitative and qualitative analysis of medical imaging .It has affected the following treatment and analysis directly. The thesis chooses medical images (including X-ray, CT, B ultrasonic image) as the main research object, studies the application of image enhancement technology in the field of medical images processing. and then we sum up the most effective processing method for image enhancement according to the characteristics of image.Key words:Medical Image ；Medical image enhancement ；effective method11 引言近年来，随着信息时代特别是数字时代的来临数字医学影像成为医生诊断和治疗的重要辅助手段。

数字图像处理英文原版及翻译

数字图象处理英文原版及翻译Digital Image Processing: English Original Version and TranslationIntroduction:Digital Image Processing is a field of study that focuses on the analysis and manipulation of digital images using computer algorithms. It involves various techniques and methods to enhance, modify, and extract information from images. In this document, we will provide an overview of the English original version and translation of digital image processing materials.English Original Version:The English original version of digital image processing is a comprehensive textbook written by Richard E. Woods and Rafael C. Gonzalez. It covers the fundamental concepts and principles of image processing, including image formation, image enhancement, image restoration, image segmentation, and image compression. The book also explores advanced topics such as image recognition, image understanding, and computer vision.The English original version consists of 14 chapters, each focusing on different aspects of digital image processing. It starts with an introduction to the field, explaining the basic concepts and terminology. The subsequent chapters delve into topics such as image transforms, image enhancement in the spatial domain, image enhancement in the frequency domain, image restoration, color image processing, and image compression.The book provides a theoretical foundation for digital image processing and is accompanied by numerous examples and illustrations to aid understanding. It also includes MATLAB codes and exercises to reinforce the concepts discussed in each chapter. The English original version is widely regarded as a comprehensive and authoritative reference in the field of digital image processing.Translation:The translation of the digital image processing textbook into another language is an essential task to make the knowledge and concepts accessible to a wider audience. The translation process involves converting the English original version into the target language while maintaining the accuracy and clarity of the content.To ensure a high-quality translation, it is crucial to select a professional translator with expertise in both the source language (English) and the target language. The translator should have a solid understanding of the subject matter and possess excellent language skills to convey the concepts accurately.During the translation process, the translator carefully reads and comprehends the English original version. They then analyze the text and identify any cultural or linguistic nuances that need to be considered while translating. The translator may consult subject matter experts or reference materials to ensure the accuracy of technical terms and concepts.The translation process involves several stages, including translation, editing, and proofreading. After the initial translation, the editor reviews the translated text to ensure its coherence, accuracy, and adherence to the target language's grammar and style. The proofreader then performs a final check to eliminate any errors or inconsistencies.It is important to note that the translation may require adapting certain examples, illustrations, or exercises to suit the target language and culture. This adaptation ensures that the translated version resonates with the local audience and facilitates better understanding of the concepts.Conclusion:Digital Image Processing: English Original Version and Translation provides a comprehensive overview of the field of digital image processing. The English original version, authored by Richard E. Woods and Rafael C. Gonzalez, serves as a valuable reference for understanding the fundamental concepts and techniques in image processing.The translation process plays a crucial role in making this knowledge accessible to non-English speakers. It involves careful selection of a professional translator, thoroughunderstanding of the subject matter, and meticulous translation, editing, and proofreading stages. The translated version aims to accurately convey the concepts while adapting to the target language and culture.By providing both the English original version and its translation, individuals from different linguistic backgrounds can benefit from the knowledge and advancements in digital image processing, fostering international collaboration and innovation in this field.。

3-电气工程及其自动化专业外文文献英文文献外文翻译

3-电气工程及其自动化专业外文文献英文文献外文翻译1、外文原文(复印件)A: Fundamentals of Single-chip MicrocomputerThe single-chip microcomputer is the culmination of both the development of the digital computer and the integrated circuit arguably the tow most significant inventions of the 20th century [1].These tow types of architecture are found in single-chip microcomputer. Some employ the split program/data memory of the Harvard architecture, shown in Fig.3-5A-1, others follow the philosophy, widely adapted for general-purpose computers and microprocessors, of making no logical distinction between program and data memory as in the Princeton architecture, shown in Fig.3-5A-2.In general terms a single-chip microcomputer is characterized by the incorporation of all the units of a computer into a single device, as shown in Fig3-5A-3.ProgramInput& memoryOutputCPU unitDatamemoryFig.3-5A-1 A Harvard typeInput&Output CPU memoryunitFig.3-5A-2. A conventional Princeton computerExternal Timer/ System Timing Counter clock componentsSerial I/OReset ROMPrarallelI/OInterrupts RAMCPUPowerFig3-5A-3. Principal features of a microcomputerRead only memory (ROM).ROM is usually for the permanent,non-volatile storage of an applications program .Many microcomputers and microcontrollers are intended for high-volume applications and hence the economical manufacture of the devices requires that the contents of the program memory be committed permanently during the manufacture of chips . Clearly, this implies a rigorous approach to ROM code development since changes cannot be made after manufacture .This development process may involve emulation using a sophisticated development system with a hardware emulation capability as well as the use of powerful software tools.Some manufacturers provide additional ROM options by including in their range devices with (or intended for use with) user programmablememory. The simplest of these is usually device which can operate in a microprocessor mode by using some of the input/output lines as an address and data bus for accessing external memory. This type of device can behave functionally as the single chip microcomputer from which itis derived albeit with restricted I/O and a modified external circuit. The use of these ROMlessdevices is common even in production circuits where the volume does not justify the development costs of custom on-chip ROM[2];there canstill be a significant saving in I/O and other chips compared to a conventional microprocessor based circuit. More exact replacement for ROM devices can be obtained in the form of variants with 'piggy-back' EPROM(Erasable programmable ROM )sockets or devices with EPROM instead of ROM 。

数字图像处理外文翻译外文文献英文文献数字图像处理

数字图像处理外文翻译外文文献英文文献数字图像处理Digital Image Processing1 IntroductionMany operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it isinteresting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report.1.1 Categories of MethodsOne class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digitalobject in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points.The third class of operators is characterized by iterative thinning. Historically, Listing [10] used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points remains. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (for digitizing this set) or the shape complexity of the given set. In the literature the term \thinning" is not used - 1 -in a unique interpretation besides that it always denotes a connectivity preserving reduction operation applied to digital images, involving iterations of transformations of speci_ed contour points into background points. A subset Q _ I of object points is reduced by ade_ned set D in one iteration, and the result Q0 = Q n D becomes Q for the next iteration. Topology-preserving skeletonization is a special case of thinning resulting in a connected set of digital arcs or curves.A digital curve is a path p =p0; p1; p2; :::; pn = q such that pi is a neighbor of pi?1, 1 _ i _ n, and p = q. A digital curve is called simpleif each point pi has exactly two neighbors in this curve. A digital arc is a subset of a digital curve such that p 6= q. A point of a digital arc which has exactly one neighbor is called an end point of this arc. Within this third class of operators (thinning algorithms) we may classify with respect to algorithmic strategies: individual pixels are either removed in a sequential order or in parallel. For example, the often cited algorithm by Hilditch [5] is an iterative process of testing and deleting contour pixels sequentially in standard raster scan order. Another sequential algorithm by Pavlidis [12] uses the de_nition of multiple points and proceeds by contour following. Examples of parallel algorithms in this third class are reduction operators which transform contour points into background points. Di_erences between these parallel algorithms are typically de_ned by tests implemented to ensure connectedness in a local neighborhood. The notion of a simple point is of basic importance for thinning and it will be shown in this reportthat di_erent de_nitions of simple points are actually equivalent. Several publications characterize properties of a set D of points (to be turned from object points to background points) to ensure that connectivity of object and background remain unchanged. The report discusses some of these properties in order to justify parallel thinning algorithms.1.2 BasicsThe used notation follows [17]. A digital image I is a functionde_ned on a discrete set C , which is called the carrier of the image.The elements of C are grid points or grid cells, and the elements (p;I(p)) of an image are pixels (2D case) or voxels (3D case). The range of a (scalar) image is f0; :::Gmaxg with Gmax _ 1. The range of a binary image is f0; 1g. We only use binary images I in this report. Let hIi be the set of all pixel locations with value 1, i.e. hIi = I?1(1). The image carrier is de_ned on an orthogonal grid in 2D or 3D - 2 -space. There are two options: using the grid cell model a 2D pixel location p is a closed square (2-cell) in the Euclidean plane and a 3D pixel location is a closed cube (3-cell) in the Euclidean space, where edges are of length 1 and parallel to the coordinate axes, and centers have integer coordinates. As a second option, using the grid point model a 2D or 3D pixel location is a grid point.Two pixel locations p and q in the grid cell model are called 0-adjacent i_ p 6= q and they share at least one vertex (which is a 0-cell). Note that this speci_es 8-adjacency in 2D or 26-adjacency in 3D if the grid point model is used. Two pixel locations p and q in the grid cell model are called 1- adjacent i_ p 6= q and they share at least one edge (which is a 1-cell). Note that this speci_es 4-adjacency in 2D or 18-adjacency in 3D if the grid point model is used. Finally, two 3Dpixel locations p and q in the grid cell model are called 2-adjacent i_ p 6= q and they share at least one face (which is a 2-cell). Note that this speci_es 6-adjacency if the grid point model is used. Any of these adjacency relations A_, _ 2 f0; 1; 2; 4; 6; 18; 26g, is irreexive andsymmetric on an image carrier C. The _-neighborhood N_(p) of a pixel location p includes p and its _-adjacent pixel locations. Coordinates of 2D grid points are denoted by (i; j), with 1 _ i _ n and 1 _ j _ m; i; j are integers and n;m are the numbers of rows and columns of C. In 3Dwe use integer coordinates (i; j; k). Based on neighborhood relations wede_ne connectedness as usual: two points p; q 2 C are _-connected with respect to M _ C and neighborhood relation N_ i_ there is a sequence of points p = p0; p1; p2; :::; pn = q such that pi is an _-neighbor of pi?1, for 1 _ i _ n, and all points on this sequence are either in M or all in the complement of M. A subset M _ C of an image carrier is called _-connected i_ M is not empty and all points in M are pairwise _-connected with respect to set M. An _-component of a subset S of C is a maximal _-connected subset of S. The study of connectivity in digital images has been introduced in [15]. It follows that any set hIi consists of a number of _-components. In case of the grid cell model, a component is the union of closed squares (2D case) or closed cubes (3D case). The boundary of a 2-cell is the union of its four edges and the boundary of a 3-cell is the union of its six faces. For practical purposes it iseasy to use neighborhood operations (called local operations) on adigital image I which de_ne a value at p 2 C in the transformed image based on pixel- 3 -values in I at p 2 C and its immediate neighbors in N_(p).2 Non-iterative AlgorithmsNon-iterative algorithms deliver subsets of components in specied scan orders without testing connectivity preservation in a number of iterations. In this section we only use the grid point model.2.1 \Distance Skeleton" AlgorithmsBlum [3] suggested a skeleton representation by a set of symmetric points.In a closed subset of the Euclidean plane a point p is called symmetric i_ at least 2 points exist on the boundary with equal distances to p. For every symmetric point, the associated maximal discis the largest disc in this set. The set of symmetric points, each labeled with the radius of the associated maximal disc, constitutes the skeleton of the set. This idea of presenting a component of a digital image as a \distance skeleton" is based on the calculation of a speci_ed distance from each point in a connected subset M _ C to the complement of the subset. The local maxima of the subset represent a \distance skeleton". In [15] the d4-distance is specied as follows. De_nition 1 The distance d4(p; q) from point p to point q, p 6= q, is the smallest positive integer n such that there exists a sequence of distinct grid points p = p0,p1; p2; :::; pn = q with pi is a 4-neighbor of pi?1, 1 _ i _ n.If p = q the distance between them is de_ned to be zero. Thedistance d4(p; q) has all properties of a metric. Given a binary digital image. We transform this image into a new one which represents at each point p 2 hIi the d4-distance to pixels having value zero. The transformation includes two steps. We apply functions f1 to the image Iin standard scan order, producing I_(i; j) = f1(i; j; I(i; j)), and f2in reverse standard scan order, producing T(i; j) = f2(i; j; I_(i; j)), as follows:f1(i; j; I(i; j)) =8><>>:0 if I(i; j) = 0minfI_(i ? 1; j)+ 1; I_(i; j ? 1) + 1gif I(i; j) = 1 and i 6= 1 or j 6= 1- 4 -m+ n otherwisef2(i; j; I_(i; j)) = minfI_(i; j); T(i+ 1; j)+ 1; T(i; j + 1) + 1g The resulting image T is the distance transform image of I. Notethat T is a set f[(i; j); T(i; j)] : 1 _ i _ n ^ 1 _ j _ mg, and let T_ _ T such that [(i; j); T(i; j)] 2 T_ i_ none of the four points in A4((i; j)) has a value in T equal to T(i; j)+1. For all remaining points (i; j) let T_(i; j) = 0. This image T_ is called distance skeleton. Now weapply functions g1 to the distance skeleton T_ in standard scan order, producing T__(i; j) = g1(i; j; T_(i; j)), and g2 to the result of g1 in reverse standard scan order, producing T___(i; j) = g2(i; j; T__(i; j)), as follows:g1(i; j; T_(i; j)) = maxfT_(i; j); T__(i ? 1; j)? 1; T__(i; j ? 1) ? 1gg2(i; j; T__(i; j)) = maxfT__(i; j); T___(i + 1; j)? 1; T___(i; j + 1) ? 1gThe result T___ is equal to the distance transform image T. Both functions g1 and g2 de_ne an operator G, with G(T_) = g2(g1(T_)) = T___, and we have [15]: Theorem 1 G(T_) = T, and if T0 is any subset of image T (extended to an image by having value 0 in all remaining positions) such that G(T0) = T, then T0(i; j) = T_(i; j) at all positions of T_with non-zero values. Informally, the theorem says that the distance transform image is reconstructible from the distance skeleton, and it is the smallest data set needed for such a reconstruction. The useddistance d4 di_ers from the Euclidean metric. For instance, this d4-distance skeleton is not invariant under rotation. For an approximation of the Euclidean distance, some authors suggested the use of di_erent weights for grid point neighborhoods [4]. Montanari [11] introduced a quasi-Euclidean distance. In general, the d4-distance skeleton is a subset of pixels (p; T(p)) of the transformed image, and it is not necessarily connected.2.2 \Critical Points" AlgorithmsThe simplest category of these algorithms determines the midpointsof subsets of connected components in standard scan order for each row. Let l be an index for the number of connected components in one row of the original image. We de_ne the following functions for 1 _ i _ n: ei(l) = _ j if this is the lth case I(i; j) = 1 ^ I(i; j ? 1) = 0 in row i, counting from the left, with I(i;?1) = 0 ，oi(l) = _ j if this is the lth case I(i; j) = 1- 5 -^ I(i; j+ 1) = 0 ，in row i, counting from the left, with I(i;m+ 1)= 0 ，mi(l) = int((oi(l) ?ei(l)=2)+ oi(l) ，The result of scanning row i is a set ofcoordinates (i;mi(l)) ofof the connected components in row i. The set of midpoints of all rows midpoints ，constitutes a critical point skeleton of an image I. This method is computationally eÆcient.The results are subsets of pixels of the original objects, and these subsets are not necessarily connected. They can form \noisy branches" when object components are nearly parallel to image rows. They may be useful for special applications where the scanning direction is approximately perpendicular to main orientations of object components.References[1] C. Arcelli, L. Cordella, S. Levialdi: Parallel thinning ofbinary pictures. Electron. Lett. 11:148{149, 1975}.[2] C. Arcelli, G. Sanniti di Baja: Skeletons of planar patterns. in: Topolog- ical Algorithms for Digital Image Processing (T. Y. Kong, A. Rosenfeld, eds.), North-Holland, 99{143, 1996.}[3] H. Blum: A transformation for extracting new descriptors of shape. in: Models for the Perception of Speech and Visual Form (W. Wathen- Dunn, ed.), MIT Press, Cambridge, Mass., 362{380, 1967.19} - 6 -数字图像处理1引言许多研究者已提议提出了在数字图像里的连接组件是由一个减少的数据量或简化的形状。

图像处理方面的参考文献

添加标题
图像处理技术的分类：按照处理方法的不同，图像处理技术可以分为图像增强、图像恢复、图像分析、图像理解等。其中，图像增强主要关注改善图像的视觉效果，提高图像的清晰度和对比度；图像恢复主要关注修复退化或损坏的图像，恢复其原始状态；图像分析主要关注从图像中提取有用的信息，如目标检测、特征提取等；图像理解则关注对图像内容的认知和解释，实现更高层次的理解和交互。
实时处理需求：随着图像采集设备的普及，实时处理成为一大挑战。
高分辨率处理：高分辨率图像的处理需要更高的计算资源和算法优化。
深度学习算法的可解释性：深度学习在图像处理中广泛应用，但其黑箱性质使得结果难以解释。
数据隐私与安全：图像处理涉及大量个人数据，如何在处理过程中保护隐私和数据安全是一大挑战。
未来图像处理技术的发展趋势和方向
深度学习在图像增强中的应用实例
图像超分辨率：使用深度学习技术将低分辨率图像转换为高分辨率图像，提高图像的清晰度和细节表现。
图像去噪：利用深度学习技术对图像中的噪声进行去除，提高图像的质量和可用性。
图像风格转换：通过深度学习技术实现将一种风格的图像转换为另一种风格，如将手绘风格的图像转换为写实风格的图像。
实例3：深度学习在图像分割中取得了许多成功的应用，如医学图像分割、遥感图像分割、人脸识别等，为相关领域的发展提供了有力支持。
实例4：深度学习在图像分割中面临的挑战包括计算量大、模型泛化能力不足等，未来需要进一步研究和改进。
深度学习在图像识别中的应用实例
图像分类：利用深度学习技术对图像进行分类，例如在人脸识别、物体识别等领域的应用。
图像增强技术分类：按照处理方法的不同，可以分为空域增强和频域增强两
02 类。其中，空域增强是在图像的像素域上直接进行操作，而频域增强则是在

医疗影像分析的深度学习模型研究(英文中文双语版优质文档)

医疗影像分析的深度学习模型研究（英文中文双语版优质文档）With the continuous development of medical technology, medical imaging is more and more widely used in clinical practice. However, the analysis and diagnosis of medical images usually takes a lot of time and manpower, and the accuracy is also affected by the doctor's personal experience. In recent years, the emergence of deep learning technology has brought new opportunities and challenges for medical image analysis. This article will deeply discuss the research on deep learning models for medical image analysis, including the application and research progress of models such as convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN).1. Application of convolutional neural network (CNN) model in medical image analysisConvolutional neural network is a state-of-the-art deep learning model, which has a wide range of applications in the field of medical image analysis. Convolutional neural networks can automatically extract features from medical images and classify them as normal and abnormal. For example, in medical image analysis, convolutional neural networks can be used to analyze lung X-rays to identify lung diseases such as pneumonia, tuberculosis, and lung cancer. In addition, convolutional neural networks can also be used for segmentation and registration of medical images for more precise localization and identification of lesions.2. Application of Recurrent Neural Network (RNN) Model in Medical Image AnalysisA recurrent neural network is a deep learning model capable of processing sequential data. In medical image analysis, recurrent neural networks are often used to analyze time series data, such as electrocardiograms and electroencephalograms in medical images. The cyclic neural network can automatically extract the features in the time series data, so as to realize the classification and recognition of medical images. For example, in the diagnosis of heart disease, a recurrent neural network can identify heart diseases such as arrhythmia and myocardial infarction by analyzing ECG data.3. Application of Generative Adversarial Network (GAN) Model in Medical Image AnalysisGenerative adversarial networks, a deep learning model capable of generating realistic images, have also been widely used in medical image analysis in recent years. Generative adversarial networks usually consist of two neural networks, one is a generative network, which is responsible for generating realistic images; the other is a discriminative network, which is used to judge whether the images generated by the generative network are consistent with real images. In medical image analysis, GAN can be used to generate realistic medical images, such as MRI images, CT images, etc., to help doctors better diagnose and treat. In addition, the generative confrontation network can also be used for denoising and enhancement of medical images to improve the clarity and accuracy of medical images.In conclusion, the application of deep learning models in medical image analysis has broad prospects and challenges. With the continuous development of technology and the deepening of research, the application of deep learning models in medical image analysis will become more and more extensive and in-depth, making greater contributions to the progress and development of clinical medicine.随着医疗技术的不断发展，医疗影像在临床中的应用越来越广泛。

眼视光学英文文献

眼视光学英文文献Optometry, the study of vision and the correction of visual disorders, plays a crucial role in modern healthcare. It encompasses a wide range of practices, from diagnosing eye diseases to prescribing corrective lenses.The field has seen significant advancements in recent years, with new technologies like digital eye exams andlaser-assisted surgeries improving patient outcomes. These innovations have made it easier to detect and treat various eye conditions, such as myopia and astigmatism.Education in optometry is rigorous, requiring a deep understanding of the eye's anatomy and physiology. Students must also be adept at using specialized equipment and interpreting complex visual data.Research in optometry is ongoing, with a focus on improving diagnostic tools and developing new treatments for eye diseases. Scientists are also exploring the relationship between vision and overall health, recognizing the eye as a window into the body's wellbeing.The role of an optometrist extends beyond the clinic. They are often involved in public health initiatives, educating communities about the importance of regular eye exams and promoting eye safety.As the population ages, the demand for optometry services is expected to grow. This presents an opportunity for optometrists to expand their practices and contribute to the health and well-being of society.In conclusion, the field of optometry is dynamic and essential, offering a blend of science, technology, and patient care. It is a profession that not only enhancesvision but also contributes to the broader understanding of health and wellness.。