Fourier_Slice_Photography

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全景图像拼接与漫游讲义林晓泽

加到100%。
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阴影部分为重叠区域
线性融合
线性融合方法简单，但存在问题。
重影现象
解决办法
使用多段融合方法
多段融合
• 多段融合算法是一种多尺度、多分辨率的图像融合方法。在不同尺度和空间分辨率上进行融合。
➢ 步骤1：建立图像的高斯金字塔 ➢ 步骤2：由高斯金字塔建立图像的拉普拉斯金字塔 ➢ 步骤3：对拉普拉斯金字塔的每层进行融合 ➢ 步骤4：组合融合后的拉普拉斯金字塔
谢
谢
观
多段融合
2.第0层减去图像A，得到拉普拉斯金字塔第0层。
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1.第1层放大一倍得到图像A 0
高斯金字塔
多段融合
取亮度大的点，保持轮廓
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亮度取平均
0 拉普拉斯金字塔
多段融合
消除重影现象
线性融合
多段融合
全景图像漫游
• 虎溪虚拟校园系统（前台Flash+后台JSP） • 全景浏览 • 建筑物（单位，办事指南） • 导航（路线导航，新生导航） • 测距 • 鹰眼
• 相比三维建模，制作流程快，更真实，速度更快； • 现有的全景拼接软件：版权，部分需要手工设置。
全景图像拼接
➢ 全景图像拼接的定义：全景图像拼接是一种将一组相互间有重叠部分的图
像序列进行空间匹配对准，经重采样合成后形成一幅包含各图像序列信息的360°水平视角的、完整的、清晰的新图像的技术。
• Step 3:将所有内点代入公式，采用最小二乘法解出H，如果 Step3中没有任何点改变，则进入Step4；否则进入Step3；

Wide-field Fourier transform spectral imaging

a r X i v :0802.3773v 1 [p h y s i c s .o p t i c s ] 26 F eb 2008Wide-ﬁeld Fourier transform spectral imagingMichael Atlan and Michel GrossLaboratoire Kastler Brossel,´Ecole Normale Sup´e rieure,Universit´e Pierre et Marie-Curie -Paris 6,Centre National de la Recherche Scientiﬁque,UMR 8552;24rue Lhomond,75005Paris,France(Dated:February 27,2008)We report experimental results of parallel measurement of spectral components of the light.The temporal ﬂuctuations of an optical ﬁeld mixed with a separate reference are recorded with a high throughput complementary metal oxide semi-conductor camera (1Megapixel at 2kHz framerate).A numerical Fourier transform of the time-domain recording enables wide-ﬁeld coherent spectral imaging.Qualitative comparisons with frequency-domain wide-ﬁeld laser Doppler imaging are pro-vided.Many coherent spectral detection schemes using a sin-gle detector (or balanced detection)to detect tempo-ral ﬂuctuation spectra in an optical mixing conﬁgura-tion rely on Fourier Transform spectroscopy (FTS)for signal measurement [1,2].They provide a high spec-tral resolution and shot-noise sensitivity.They allow to shift away the 1/f noise of laser intensity ﬂuctuations since the measurement is done with GHz-bandwidth de-tectors.Most imaging conﬁgurations require a spatial scanning of the beam,but two approaches to parallel co-herent spectral imaging with a solid-state array detector were presented recently :full-ﬁeld laser Doppler imag-ing (LDI)[3,4,5]and frequency-domain wide-ﬁeld LDI (FDLDI)[6,7,8].In the former approach,the tempo-ral ﬂuctuations of an optical object ﬁeld impinging on a complementary metal oxide semi-conductor (CMOS)camera are recorded.Spectral imaging is done by cal-culating the intensity-ﬂuctuation spectrum by a Fourier transform (FT).One major weakness of this approach lies in its inapplicability in low-light conditions.The lat-ter approach uses a spatiotemporal heterodyne detection,which consists in recording an optical mix of the object ﬁeld with an angularly tilted and frequency-shifted lo-cal oscillator (LO).It enables to measure spectral maps with a high sensitivity but requires to acquire the spectral components sequentially by sweeping the LO frequency.We present an alternative approach,designed to combine the advantages of both methods.It uses the properties of digital oﬀ-axis holography and FTS to enable exploring of the temporal frequency spectrum of the object ﬁeld.Basically,the parallel spectral imaging instrument pre-sented here uses a CMOS camera to record the intensity ﬂuctuations of an object ﬁeld mixed with a separate ref-erence (LO);the ﬁeld spectral components are calculated by FTS.The experimental setup is based on an optical interfer-ometer sketched in Fig.1.A CW,80mW,λ=658nm diode (Mitsubishi ML120G21)provides the main laser beam (ﬁeld E L ,angular frequency ωL ).A small part of this beam is split by a prism to form a reference (LO)beam,while the remaining part is expanded and illu-minates an object in reﬂection with an averageincidenceFIG.1:Setup.LD :single mode laser diode.M :mirror.BE :beam expander.BS :beam splitter.E :object ﬁeld.E LO :local oscillator ﬁeld.angle α≈45◦.The object is made of a USAF 1951target set in front of a 4mm-thick transparent tank,ﬁlled with a non dilute intralipid (TM)10%emulsion.To beneﬁt from heterodyne gain,the ﬁeld scattered by the object,E ,is mixed with the LO ﬁeld E LO (|E LO |2/|E |2∼103),and is detected by a CMOS camera (LaVision HighSpeedStar 4,10bit,1024×1024pixels at ωS /(2π)=2.0kHz frame rate,pixel area d pix 2with d pix =17.5µm,set at a dis-tance d =50cm from the object.A 10mm focal length lens is placed in the reference arm in order to create an oﬀ-axis (θ≈1◦tilt angle)virtual point source in the object plane.This conﬁguration constitutes a lensless Fourier holographic setup [9].In the detector plane,the LO and object ﬁelds are:E LO (t )=E LO e iωL t +c.c.E (x,y,t )=E (x,y,t )e iωL t +c.c.(1)where c .c .is the complex conjugate term.The LO beam is a spherical wave propagating along z ,and thus the LO ﬁeld envelope E LO does not depend on x,y,t .The object ﬁeld envelope E ,which contains information on the ob-ject shape,and which may exhibit speckle,depends on position x,y .It also depends on time t because of dy-namic scattering.The intensity I recorded by the camera can be expressed as a function of the complex ﬁelds :I (x,y,t )=+E(x,y,t)E∗LO+E∗(x,y,t)E LO whereFIG.4:Spectrum of theﬁeld dynamically backscattered by a non dilute suspension of intralipid10%obtained by averaging the objectﬁeld intensity over50×50pixels.FTS(solid gray curve)and FDLDI(points)spectra.Horizontal axis is the frequencyω/(2π)in kHz,vertical axis is signal in linear arbitrary units.2048frequency pointsωare linearly spaced between theNyquist frequencies±1.0kHz.The measurement time of the1024×1024×2048data cube is≃1s,and the FT3D calculation time on a personal computer is about 1hour nowadays.Fig.3(a)to3(d)show the images of the objectﬁeld intensity| E|2in the target plane for the fre-quency componentsω/(2π)=0(a),187.5(b),500.0(c)and937.5Hz(d)(128×256pixels crops of the total holo-gram,displayed in logarithmic scale).Forω=0(a),the LO beam noise is dominant and the target is not visible. Forω=0,the USAF target is visible but the bright-ness and SNR of the image will decrease with frequency ((b)to(d)).Fig.3(e)shows the image obtained by aver-aging over all frequencies.We have compared these re-sults with wide-ﬁeld FDLDI images[6,7]obtained with a charge-coupled device(CCD)camera(PCO Pixelﬂy: 1280×1024pixels,framerate:8Hz)with four-phase de-modulation over32images per spectral point,in the same experiment.Fig.3shows the128×256pixels FDLDI im-ages at0(f),187.5(g),500.0(h)and937.5Hz(i),while image(j)corresponds to the average over all frequen-cies.The USAF target is seen on all the images.For ω=0,the target appears as a contrast-reversed image [6].Since the pixel size of the CCD camera(6.7×6.7µm) is smaller than its CMOS counterpart(17.5µm×17.5µm), the extension of FDLDI image is larger(the acceptance angle of the receiver is proportional to the inverse of the pixel size).The white dashed rectangle of Fig.3j corre-sponds to the CMOS-imagerﬁeld of view.Although the number of recorded spectral points was kept low for the FDLDI measurement compared to the FTS scheme(64 vs.2048),the total measurement time was much greater (256seconds vs.1second).This diﬀerence is due to the throughput discrepancy between the CCD(1.3Mpixel@ 8Hz)and the CMOS(1.0Mpixel@2kHz)receivers. We have computed the frequency spectrum of the light diﬀused by the intralipid emulsion with FTS.This spec-trum is obtained by averaging the objectﬁeld intensity over a50×50pixels region of the reconstructed image. The lineshape is plotted on Fig.4as a solid gray curve. We have compared its shape with the one obtained with the FDLDI technique(Fig.4points).The agreement is good except in the tails of the spectrum.The FTS fre-quency response is imperfectlyﬂat,because of the CCD ﬁnite exposure time(1/ωS)that yields signal low pass ﬁltering[15,16].Moreover,because the signal temporal evolution is sampled at2kHz,temporal sampling aliases and spectrum overlap are expected around the Nyquist frequencies±1kHz.In this Letter,we have shown that the spatiotemporal heterodyne detection recently introduced[6,7,8]can be adapted to a wide-ﬁeld Fourier transform spectral imag-ing scheme with a high throughput array detector.By using an oﬀ-axis optical mixing conﬁguration,the object-LOﬁelds cross terms are shifted away from center of the detector reciprocal plane(k-space),contrarily to the ob-ject and LO self-beating contributions,which remain un-shifted.It is then possible to reject the local oscillator and the objectﬁeld self-beating contributions accounting for noise.The heterodyne gain provided by optical am-pliﬁcation of the objectﬁeld by the LOﬁeld is essential for a high frame rate camera measurement in low-light conditions,since the objectﬁeld intensity decreases with the camera exposure time.The ability toﬁlter-oﬀthe LO beam noise,yields an optimal sensitivity of1photo-electron of noise per pixel.This limit has been reached with4-phase detection[17],which consists of a discrete Fourier transform on4data points to calculate a sin-gle frequency component of the objectﬁeld.Here,the expected noise limit is the same for each frequency com-ponent of the objectﬁeld obtained by discrete Fourier transform.This method mightﬁnd applications in dy-namic light scattering analysis of colloidal suspensions and microﬂuidic systems.[1]E.Pike,Review of Physics in Technology1,180(1970).[2]D.Chung,K.Lee,and E.Mazur,Applied physics.B,Lasers and optics64,1(1997).[3]A.Serov,W.Steenbergen,and F.de Mul,Optics Letters27,300(2002).[4]A.Serov and sser,Opt.Express13,6416(2005).[5]A.Serov,B.Steinacher,and sser,Opt.Ex.13,3681(2005).[6]M.Atlan,M.Gross,T.Vitalis,A.Rancillac,B.C.For-get,and A.K.Dunn,Optics Letters31(2006).[7]M.Atlan and M.Gross,Review of Scientiﬁc Instruments77,1161031(2006).[8]M.Atlan and M.Gross,Journal of the Optical Societyof America A24,2701(2007).[9]G.W.Stroke,Applied Physics Letters6,201(1965).[10]U.Schnars and W.Juptner,Appl.Opt.33,179(1994).[11]C.Wagner,S.Seebacher,W.Osten,and W.Juptner,Applied Optics38,4812(1999).[12]U.Schnars and W.P.O.Juptner,Meas.Sci.Technol.13,R85(2002).[13]T.M.Kreis,Optical Engineering41,771(2002).[14]E.Cuche,P.Marquet,and C.Depeursinge,Applied Op-tics39,4070(2000).[15]P.Picart,J.Leval,D.Mounier,and S.Gougeon,Opt.Lett.28,1900(2003).[16]M.Atlan,M.Gross,and E.Absil,Optics Letters32,1456(2007).[17]M.Gross and M.Atlan,Optics Letters32,909(2007).。

85 mm Planar T f 1.4 全景镜头说明书

Planar® T*f/1.4 - 85 mmThe 85 mm Planar® T* f/1.4 lens ranks amongstthe best high-speed lenses presently available for 35mm reflex cameras. An outstanding feature is theexcellent image quality even at full aperture.This feature results in special applications of the 85mm Planar® T* f/1.4 lens for discerning amateurand professional photographers. Owing to the fullyutilizable speed, this lens is especially suitable forstage photography. Sporting events, outdoors andindoors, in twilight and floodlights are tasks forwhich the press reporter requires or desires a focallength which is slightly longer than that of standardlenses due to the object distance or for reasons ofperspective. As regards portraits, anotheradvantage is that the depth of field is low withphotographs taken with the diaphragm fully open.Thus an unsteady and disturbing background isavoided and the portrait stands out clearly againstthe surroundings.Cat. No. of lens:10 21 45Weight:approx. 595 gNumber of elements:6Focusing range:∞ to 1 mNumber of groups:5Entrance pupil:Max. aperture:f/1.4Position:68.7 mm behind the first lens vertex Focal length:84.8 mm Diameter:58.4 mmNegative size:24 x 36 mm Exit pupil:Angular field 2w:28º 30' diagonal Position:23.1 mm in front of the last lens vertex Mount:focusing mount with bayonet;Diameter:46.1 mmTTL metering either at full aperture Position of principal planes:or in stopped-down position.H:39.1 mm behind the first lens vertexAperture priority/Shutter priority/H':45.0 mm in front of the last lens vertexAutomatic programs Back focal distance:39.3 mm(Multi-Mode Operation)Distance between first andAperture scale: 1.4 - 2 - 2.8 - 4 - 5.6 - 8 - 11 - 16last lens vertex:65.8 mmFilter connection:clip-on-filter, diameter 70 mmscrew-in type, thread M 67 x 0.75Performance data:Planar® T* f/1.4 - 85 mmCat. No. 10 21 451. MTF DiagramsThe image height u - calculated from theimage center - is entered in mm on thehorizontal axis of the graph. Themodulation transfer T (MTF = Modulation Transfer Factor) is entered on the verticalaxis. Parameters of the graph are thespatial frequencies R in cycles (line pairs)per mm given at the top of this page.The lowest spatial frequency correspondsto the upper pair of curves, the highestspatial frequency to the lower pair. Aboveeach graph, the f-number k is given forwhich the measurement was made."White" light means that themeasurement was made with a subject illumination having the approximatespectral distribution of daylight.Unless otherwise indicated, theperformance data refer to large object distances, for which normal photographiclenses are primarily used.2. Relative illuminanceIn this diagram the horizontal axis givesthe image height u in mm and the verticalaxis the relative illuminance E, both for full aperture and a moderately stopped-downlens. The values for E are determinedtaking into account vignetting and naturallight decrease.3. DistortionHere again the image height u is enteredon the horizontal axis in mm. The verticalaxis gives the distortion V in % of therelevant image height. A positive value forV means that the actual image point isfurther from the image center than withperfectly distortion-free imaging(pincushion distortion); a negative Vindicates barrel distortion.Carl ZeissPhotoobjektiveD-73446 OberkochenTelephone (07364) 20-6175Fax (07364) 20-4045eMail:**************http://www.zeiss.de Subject to change.。

小视野Focus-DWI技术对乳腺癌应用价值的评价

小视野Focus-DWI 技术对乳腺癌应用价值的评价王志远1，俞茜21.阳新县人民医院医学影像科，湖北阳新435200；2.延安大学附属医院心脑血管病区，陕西延安716000【摘要】目的探讨小视野高清扩散加权成像(Focus DWI)在乳腺癌诊断中的应用价值。

方法回顾性收集延安大学附属医院经手术病理诊断的乳腺癌患者51例，术前均行乳腺MRI 检查，包含Focus-DWI 和常规DWI (C-DWI)序列。

由两名阅片者采用双盲法对Focus-DWI 和C-DWI 图像的变形程度、伪影、清晰度以及整体图像质量进行主观评分，测量乳腺癌及正常乳腺腺体的ADC 值，比较两种DWI 序列主观评分、定量参数间的差异。

结果Focus-DWI 序列整体图像质量、清晰度的主观评分分别为(3.71±0.70)分、(3.65±0.66)分，明显高于C-DWI 序列的(2.71±0.64)分、(2.69±0.71)分，差异均有统计学意义(P <0.05)；Focus-DWI 序列图像变形和伪影严重程度的主观评分分别为(2.51±0.83)分、(2.61±0.63)分，明显低于C-DWI 序列的(3.41±0.80)分、(3.57±0.61)分，差异均有统计学意义(P <0.05)；乳腺癌Focus-DWI 测得ADC 值为(1.12±0.12)×10-3mm 2/s ，明显低于C-DWI 的(1.22±0.13)×10-3mm 2/s ，差异具有统计学意义(P <0.05)；正常腺体Focus-DWI 和C-DWI 测得ADC 值分别为(2.03±0.89)×10-3mm 2/s 、(2.13±0.75)×10-3mm 2/s ，差异无统计学意义(P >0.05)。

摄影视觉技巧介绍

我的摄影老师老浦拍的作品超级稀饭啊~ 老人家不玩网络。

哈哈。

不然就爆他博客了 @东川泥石流汽车拉力赛我的摄影老师老浦拍的作品超级稀饭啊~ 老人家不玩博客。

哈哈。

不然就爆他博客了 @东川泥石流汽车拉力赛相機: Nikon D700 曝光: 8 Aperture: f/2.8 焦距: 17 mm ISO 速度: 1600 曝光偏向: 0 EV 閃光: No Flash相機: Canon EOS 1000D 曝光: 1/4000 sec Aperture: f/1.8 焦距: 50 mm ISO 速度: 100 曝光偏向: 0 EV 閃光: Off, Did not fireCamera:Canon EOS 5D Mark II Exposure:0.005 sec (1/200) Aperture:f/3.2 Focal Length:95 mm ISO Speed:800 Exposure Bia s:0 EV Flash:Off, Did not fireCamera:Canon EOS 5D Mark II Exposure:0.003 sec (1/400) Aperture:f/2.0 Focal Length:85 mm ISO Speed:100 Exposure Bia s:0 EV Flash:Off, Did not fireCamera:Canon EOS 5D Mark II Exposure:0.008 sec (1/125) Aperture:f/1.6 Focal Length:85 mm ISO Speed:500 Exposure Bia s:0 EV Flash:On, FiredCamera:Canon EOS 5D Mark II Exposure:0.001 sec (1/1000) Aperture:f/2.5 Focal Length:85 mm ISO Speed:100 Exposure Bi as:0 EV Flash:On, FiredCamera:Canon EOS 5D Mark II Exposure:0.006 sec (1/160) Aperture:f/9.0 Focal Length:85 mm ISO Speed:500 Exposure Bia s:0 EV Flash:Off, Did not fireCamera:Canon EOS 5D Mark II Exposure:0.002 sec (1/500) Aperture:f/3.2 Focal Length:85 mm ISO Speed:100 Exposure Bia s:0 EV Flash:On, FiredCamera:Canon EOS 5D Mark II Exposure:0.002 sec (1/500) Aperture:f/2.2 Focal Length:85 mm ISO Speed:50 Exposure Bias: 0 EV Flash:On, FiredCamera:Canon EOS 5D Mark II Exposure:0.002 sec (1/500) Aperture:f/2.2 Focal Length:85 mm ISO Speed:50 Exposure Bias: 0 EV Flash:On, FiredCamera:Canon EOS 5D Mark II Exposure:0.002 sec (1/640) Aperture:f/2.0 Focal Length:85 mm ISO Speed:100 Exposure Bia s:0 EV Flash:On, Fired相機: Nikon D3 曝光: 15 Aperture: f/5.6 焦距: 120 mm ISO 速度: 800 曝光偏向: -1/3 EV 閃光: No Flash相機: Canon EOS 5D Mark II 曝光: 0.25 sec (1/4) Aperture: f/6.3 焦距: 24 mm ISO 速度: 1000 曝光偏向: 0 EV 閃光: Off, Did not fire /photos/floriopics/逆光就是很神圣嘛：） /photos/elisabethdunker/相機: Nikon D40 曝光: 0.01 sec (1/100) Aperture: f/22.0 焦距: 55 mm 焦距: 55.0 mm ISO 速度: 800 曝光偏向: 0 EV 閃光: No Flash /photos/bethannny//photos/made_in_england/相機: Canon EOS-1Ds Mark III 曝光: 0.017 sec (1/60) Aperture: f/5.6 焦距: 24 mm ISO 速度: 100 曝光偏向: 0 EV 閃光: Off, Did not fire/photos/sheldonnalos/ photographer：Sheldon Nalosphotographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/photographer：Dustin Diaz flickr：/photos/polvero/。

新英汉摄影技术词典

新英汉摄影技术词典前言随着摄影技术的不断发展，摄影领域的专业术语也日新月异。

本词典旨在帮助读者更好地了解摄影技术术语，并在实践中运用。

无论是新手摄影爱好者，还是专业摄影师，都可以在本词典中找到所需的信息和参考资料。

A1. Aperture (光圈)光圈是调节镜头光线的大小，通常用f值表示。

较小的f值意味着更大的光圈，更多的光线进入镜头；而较大的f值意味着较小的光圈，限制光线进入镜头。

2. Auto Focus (自动对焦)自动对焦是指相机能够自动识别并对焦于所选择的目标，而不需要手动调整镜头。

B1. Bokeh (虚化)虚化是指在摄影中通过控制焦距和光圈大小，使得背景模糊，从而突出主体目标。

2. Bracketing (曝光补偿)曝光补偿是指通过连续拍摄同一景物的多张照片，每张照片的曝光参数有所不同，以确保至少有一张符合要求的曝光。

C1. Depth of Field (景深)景深是指摄影作品中清晰焦点与模糊焦点之间的范围，主要受光圈大小、镜头焦距和拍摄距离等因素的影响。

2. Cropping (裁剪)裁剪是指在后期处理中对照片进行裁剪或修剪，从而改变画面的构图和内容。

D1. Dynamic Range (动态范围)动态范围是指相机传感器能够捕捉的亮度范围，通常以光线最暗部分和最亮部分之间的差异来衡量。

2. Double Exposure (双重曝光)双重曝光是指在同一张底片或传感器上连续曝光两次或以上，从而叠加多个场景，创造出独特的效果。

E1. Exposure (曝光)曝光是指相机传感器或胶卷对光线的暴露时间，通过控制快门速度、光圈和ISO等参数来调节曝光量。

2. Electronic Viewfinder (电子取景器)电子取景器是指使用数字信息显示相机镜头捕捉画面的一种取景方式，通常在无反光镜相机中使用。

F1. Fisheye Lens (鱼眼镜头)鱼眼镜头是一种超广角镜头，其视角非常广阔，画面呈现出凸起状，造成鱼眼效果。

数字图像处理(冈萨雷斯)-4_fourier变换和频域介绍(dip3e)经典案例幻灯片PPT

F (u,v)
F *(u, v)
f ( x ,y ) ☆ h ( x ,y ) i f f t c o n j F ( u , v ) H ( u , v )
h(x,y):CD 周期延拓
PAC1
h:
PQ
QBD1
DFT
H (u,v)
F*(u,v)H(u,v)
IDFT
R(x,y):PQ
✓ 使用这组基函数的线性组合得到任意函数f，每个基函数的系数就是f与该基函数的内积
图像变换的目的
✓ 使图像处理问题简化； ✓ 有利于图像特征提取； ✓ 有助于从概念上增强对图像信息的理解；
图像变换通常是一种二维正交变换。
一般要求： 1. 正交变换必须是可逆的； 2. 正变换和反变换的算法不能太复杂； 3. 正交变换的特点是在变换域中图像能量将集中分布在低频率成分上，边缘、线状信息反映在高频率成分上，有利于图像处理
4.11 二维DFT的实现
沿着f(x,y)的一行所进行的傅里叶变换。
F (u ,v ) F ( u , v ) (4 .6 1 9 )
复习：当两个复数实部相等,虚部互为相反数时,这两个复数叫做互为共轭复数.
4.6
二维离散傅里叶变换的性质
其他性质：
✓尺度变换〔缩放〕及线性性
a f( x ,y ) a F ( u ,v ) f( a x ,b y ) 1 F ( u a ,v b ) |a b |
域表述困难的增强任务，在频率域中变得非常普通
✓ 滤波在频率域更为直观，它可以解释空间域滤波的某些性质
✓ 给出一个问题，寻找某个滤波器解决该问题，频率域处理对于试验、迅速而全面地控制滤波器参数是一个理想工具
✓ 一旦找到一个特殊应用的滤波器，通常在空间域用硬件实现

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检测技术相比，自动化的视觉检测系统更加经济、快捷、高效与安全。纹理物体在工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代化电子系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化检测提供高效而可靠的检测算法。纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体的检测也可取得不错的效果。在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列具有实际应用价值的图像。实验结果表明本文提出的纹理物体缺陷检测算法具有高效性和易于实现性。关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

normalized cuts and image segmentation翻译

规范化切割和图像分割摘要：为解决在视觉上的感知分组的问题，我们提出了一个新的方法。

我们目的是提取图像的总体印象，而不是只集中于局部特征和图像数据的一致性。

我们把图像分割看成一个图形的划分问题，并且提出一个新的分割图形的全球标准,规范化切割。

这一标准衡量了不同组之间的总差异和总相似。

我们发现基于广义特征值问题的一个高效计算技术可以用于优化标准。

我们已经将这种方法应用于静态图像和运动序列，发现结果是令人鼓舞的。

1简介近75年前，韦特海默推出的“格式塔”的方法奠定了感知分组和视觉感知组织的重要性。

我的目的是，分组问题可以通过考虑图（1）所示点的集合而更加明确。

Figure1:H<iw m.3Uiyps?通常人类观察者在这个图中会看到四个对象，一个圆环和内部的一团点以及右侧两个松散的点团。

然而这并不是唯一的分割情况。

有些人认为有三个对象，可以将右侧的两个认为是一个哑铃状的物体。

或者只有两个对象，右侧是一个哑铃状的物体，左侧是一个类似结构的圆形星系。

如果一个人倒行逆施，他可以认为事实上每一个点是一个不同的对象。

这似乎是一个人为的例子，但每一次图像分割都会面临一个相似的问题一将一个图像的区域D划分成子集Di会有许多可能的划分方式（包括极端的将每一个像素认为是一个单独的实体）。

我们怎样挑选“最正确”的呢？我们相信贝叶斯的观点是合适的，即一个人想要在较早的世界知识背景下找到最合理的解释。

当然，困难在于具体说明较早的世界知识一一些低层次的，例如亮度，颜色，质地或运行的一致性，但是关于物体对称或对象模型的中高层次的知识是同等重要的。

这些表明基于低层次线索的图像分割不能够也不应该旨在产生一个完整的最终的正确的分割。

目标应该是利用低层次的亮度，颜色，质地，或运动属性的一致性继续的提出分层分区。

中高层次的知识可以用于确认这些分组或者选择更深的关注。

这种关注可能会导致更进一步的再分割或分组。

关键点是图像分割是从大的图像向下进行，而不是像画家首先标示出主要区域，然后再填充细节。

基于形状特征的图像检索

题目：基于形状特征的图像检索系统的设计与实现基于形状特征的图像检索系统的设计与实现摘要近年来，随着多媒体和计算机互联网技术的快速发展，数字图像的数量正以惊人的速度增长。

面对日益丰富的图像信息海洋，人们需要有效地从中获取所期望得到多媒体信息。

因此，在大规模的图像数据库中进行快速、准确的检索成为人们研究的热点。

为了实现快速而准确地检索图像，利用图像的视觉特征，如颜色、纹理、形状等来进行图像检索的技术，也就是基于内容的图像检索技术（CBIR）应运而生[6]。

本文主要研究基于形状特征的图像检索，边缘检测是基于形状特征的一种检索方法，边缘是图像最基本的特性。

在图像边缘检测中，微分算子可以提取出图像的细节信息，景物边缘是细节信息中最具有描述景物特征的部分,也是图像分析中的一个不可或缺的部分。

本文详细地分析了一种边缘检测方法Canny算子，用C++编程实现各算子的边缘检测，并根据边缘检测的有效性和定位的可靠性，得出Canny算子具备有最优边缘检测所需的特性。

并通过基于轮廓的描述方法，傅里叶描述符对图像的形状特征进行描述并存入数据库中。

对行相应的检索功能。

关键词：图像检索；形状特征；Canny算子；边缘检测；傅里叶描述符Design and Implementation of Image Retrieval System Based onShape FeaturesABSTRACTWith the rapid development of multimedia and computer network technique, the quantity of digital image and video is going up fabulously. Facing the vast ocean of information of image, it has a good sense to obtain the desired multimedia information. Currently, rapid and effective searching for desired image from large-scale image databases becomes an hot research topic.In order to retrieve image quickly and accurately using image visual features such as color, texture, shape, which named content-based image retrieval (CBIR) came into being. This paper introduces the principle of wavelet transform applying to image edge detection. Edge detection is based on the shape of the characteristics of a retrieval method, and the edge is the most basic characteristics of the image. In the image edge detection ,differential operator can be used to extract the details of the images, features’ edge is the most detailed information describing the characteristics of the features of the image analysis, and is also an integral part of the image. This paper analyzes a Canny operator edge detection method, and we complete with the C++ language procedure to come ture edge detection. According to the effectiveness of the image detection and the reliability of the orientation, we can deduced that the Canny operator have the characteristics which the image edge has. And contour-based method for describing the image Fourier descriptors to describe the shape feature and stored in the database. Align the corresponding search function.Key words：image retrieval；sharp feature；Canny operator；edge detection；Fourier shape descriptors目录1 前言 (1)1.1 课题背景及研究意义 (1)1.2 国内外发展状况 (1)1.3 课题研究的主要内容 (2)2 基于形状特征的图像检索 (3)2.1 图像检索技术的发展过程 (3)2.1.1 基于内容的图像检索技术 (3)2.1.2 基于形状特征的图像检索 (3)2.2 边缘检测 (4)2.3 Canny边缘检测 (4)2.3.1 Canny指标 (4)2.3.2 Canny算子的实现 (5)2.4 基于轮廓的描述方法 (7)2.4.1 傅立叶形状描述符 (7)2.5 图像的相似性度量 (9)3 基于形状特征的图像检索系统的设计 (10)3.1 Canny算子的程序设计 (10)3.2 图像特征数据库设计 (11)3.3 实验结果 (12)4 基于形状特征的图像检索系统实现 (13)4.1 系统框架 (13)4.2 编程环境 (14)4.3 程序结果 (14)5 总结 (15)参考文献 (16)致谢 (17)附录 (18)1前言1.1课题背景及研究意义随着多媒体技术、计算机技术、通信技术及Intemet网络的迅速发展，人们正在快速地进入一个信息化社会。

光场成像技术进展

光场成像技术进展作者：聂云峰，相里斌，周志良， NIE Yun-Feng， XIANGLI Bin， ZHOU Zhi-Liang作者单位：聂云峰,NIE Yun-Feng(中国科学院光电研究院,北京 100094;中国科学院研究生院,北京 100049)，相里斌,周志良,XIANGLI Bin,ZHOU Zhi-Liang(中国科学院光电研究院,北京,100094)刊名：中国科学院研究生院学报英文刊名：Journal of the Graduate School of the Chinese Academy of Sciences年，卷(期)：2011,28(5)1.肖相国;王忠厚;孙传东基于光场摄像技术的对焦测距方法的研究[期刊论文]-光子学报 2008(12)2.Castleman K R Digital image processing 19793.Levoy M;Zhang Z;Mcdowall I Recording and controlling the 4D light field in a microscope using microlens arrays[外文期刊] 20094.Vaish V;Garg G;Talvala g V Synthetic aperture focusing using a shear-warp factorization of the viewing transform 20055.Yang J C Design and analysis of a two-dimensional camera array 20056.Ng R Fourier slice photography 20057.袁艳;周宇;胡煌华光场相机中微透镜阵列与探测器配准误差分析[期刊论文]-光子学报 2010(01)8.Fife K;Gamal A A 3D multi-aperture image sensor architecture 20069.Fife K;Gamal A Integrated multi-aperture imaging 200810.Chamgoulov R O;Lane P M Optical computed-tomography microscope using digital spatial light modulation 200411.Kak A C;Slaney M Principles of computerized tomographic imaging 200112.Levoy M;Ng R;Adams A Light field microscopy[外文期刊] 200613.Ng R;Levoy M;Bredif M Light field photography with a hand-held plenoptic camera 200514.Levoy M;Hanrahan P Light field rendering 199615.Adelson E;Wang Y A Single lens stereo with a plenoptic camera[外文期刊] 1992(02)16.Adams A;Talvala E V;Park S H The Frankencamera:An experimental platform for computational photography 201017.Story D;Whitmire E;Georgiev T The future of photography:adobe technical report 200818.Lumsdaine A;Georgiev T Full resolution lightfield rendering:adobe technical report 200819.Georgiev T Spatioangular resolution trade-off in integral photography 201020.Ng R;Hanrahan P Digital correction of lens aberrations in light field photography 200621.Levoy M Light fields and computational imaging[外文期刊] 2006(08)22.Bass M Handbook of optics 199523.Pawley J B Handbook of biological confocal microscopy 199524.Swedlow J R;Sedat J W;Agard A Deconvolution in optical microscopy 1997(11)25.Green P;Sun W;Matusik W Multi-aperture photography 200726.Liang C K;Lin T H;Wong B Y Programmable aperture photography:multiplexed light field acquisition[外文期刊] 200827.Levin A;Fergus R;Durand F Image and depth from a conventional camera with a coded aperture[外文期刊] 200728.Liang C K;Liu G;Chen H Light field acquisition using programmable aperture camera 200729.Veeraraghavan A;Raslar R;Agraval A Dappled photography:Mask enhanced cameras for heterodyned light fields and coded aperture refocusing 200730.Kanade T;Saito H;Vedula S The 3d room:digitizing time-varying 3d events by synchronized multiple video streams:technical report CMURITR-98-34 199831.Yang J C;Everett M;Buchler C A real-time distributed light field camera 200232.Isaksen A;McMillan L;Gortler S Dynamically reparameterized light fields 200033.Wilburn B;Joshi N;Vaish V High performance imaging using large camera arrays 200534.Georgiev T;Intwala C;Goma S Light field camera design for integral view photography:Adobe tech report 200635.Georgiev T;Lumsdaine A High dynamic range image capture with plenoptic 2.0 camera 200936.Georgiev T;Lumsdaine A Resolution in plenoptic cameras 200937.Montebello de Integral photography 201038.Dudley L Stereoscopic photography 201039.Dudley L Methods of integral photography 201040.Dudnikov Y A;Rozhkov B K;Antipova E N Obtaining a portrait of a person by the integral photography method 1980(09)41.Dudnikov Y A;Rozhkov B K Selecting the parameters of the lens-army photographing system inintegral photography 1978(06)42.Dudnikov Y A Elimination of image pseudoscopy in integnd photography 1971(03)43.Dudnikov Y A Autostereoscopy and integral photogaphy 1970(07)44.Okoshi T Optimum pmameters and depth resolution of lens-sheet and projection-type three-dimensional displays[外文期刊] 1971(10)45.Okoshi T Three-dimensional imaging and television 1968(10)46.Gabor D A new microscopic principle[外文期刊] 1948(161)47.Gershun A The light field 1939(18)48.Lippmann G Reversible prints integral photographs 1908(07)49.Ives F Parallax stereogram and process of making same 2010本文链接：/Periodical_zgkxyyjsyxb201105001.aspx。

图像处理专业英语词汇

图像处理专业英语词汇Introduction:As a professional in the field of image processing, it is important to have a strong command of the relevant technical terminology in English. This will enable effective communication with colleagues, clients, and stakeholders in the industry. In this document, we will provide a comprehensive list of commonly used English vocabulary related to image processing, along with their definitions and usage examples.1. Image Processing:Image processing refers to the manipulation and analysis of digital images using computer algorithms. It involves various techniques such as image enhancement, restoration, segmentation, and recognition.2. Pixel:A pixel, short for picture element, is the smallest unit of a digital image. It represents a single point in an image and contains information about its color and intensity.Example: The resolution of a digital camera is determined by the number of pixels it can capture in an image.3. Resolution:Resolution refers to the level of detail that can be captured or displayed in an image. It is typically measured in pixels per inch (PPI) or dots per inch (DPI).Example: Higher resolution images provide sharper and more detailed visuals.4. Image Enhancement:Image enhancement involves improving the quality of an image by adjusting its brightness, contrast, sharpness, and color balance.Example: The image processing software offers a range of tools for enhancing photographs.5. Image Restoration:Image restoration techniques are used to remove noise, blur, or other distortions from an image and restore it to its original quality.Example: The image restoration algorithm successfully eliminated the noise in the scanned document.6. Image Segmentation:Image segmentation is the process of dividing an image into multiple regions or objects based on their characteristics, such as color, texture, or intensity.Example: The image segmentation algorithm accurately separated the foreground and background objects.7. Image Recognition:Image recognition involves identifying and classifying objects or patterns in an image using machine learning and computer vision techniques.Example: The image recognition system can accurately recognize and classify different species of flowers.8. Histogram:A histogram is a graphical representation of the distribution of pixel intensities in an image. It shows the frequency of occurrence of different intensity levels.Example: The histogram analysis revealed a high concentration of dark pixels in the image.9. Edge Detection:Edge detection is a technique used to identify and highlight the boundaries between different objects or regions in an image.Example: The edge detection algorithm accurately detected the edges of the objects in the image.10. Image Compression:Image compression is the process of reducing the file size of an image without significant loss of quality. It is achieved by removing redundant or irrelevant information from the image.Example: The image compression algorithm reduced the file size by 50% without noticeable loss of image quality.11. Morphological Operations:Morphological operations are a set of image processing techniques used to analyze and manipulate the shape and structure of objects in an image.Example: The morphological operations successfully removed small noise particles from the image.12. Feature Extraction:Feature extraction involves identifying and extracting relevant features or characteristics from an image for further analysis or classification.Example: The feature extraction algorithm extracted texture features from the image for cancer detection.Conclusion:This comprehensive list of English vocabulary related to image processing provides a solid foundation for effective communication in the field. By familiarizing yourself with these terms and their usage, you will be better equipped to collaborate, discuss, andpresent ideas in the context of image processing. Remember to continuously update your knowledge as the field evolves and new techniques emerge.。

数字图像

In this Chapter we will look at image enhancement in the frequency domain在这一章里,我们将看看在频域图像增强Jean Baptiste Joseph FourierJean Baptiste约瑟夫傅里叶The Fourier series & the Fourier transform傅里叶级数和傅里叶变换Image Processing in the frequency domain图像处理在频域Image smoothing图像的平滑Image sharpening图像锐化Fast Fourier Transform快速傅里叶变换Any function that periodically repeats itself can be expressed as a sum of sines and cosines of different frequencies each multiplied by a different coefficient – a Fourier series任何函数周期性的重复可以表示为不同频率的正弦和余弦的总和乘以一个不同系数——一个傅里叶级数Fourier transform:傅里叶变换:Even functions that are not periodic (but whose area under the curve is finite) can be expressed as the integral of sines and/or cosines multiplied by a weighing function. The formulation in this case is the Fourier transform偶函数不定期(但其曲线下的面积是有限的)可以表示为正弦和/或余弦的积分乘以一个重功能。

扭曲的时间

扭曲的时间作者：孙略来源：《中国摄影》2016年第09期从时间纬度上，我们可以将摄影划分为常见的三种：瞬间摄影、长时间曝光摄影和连续（动态）摄影。

另有一种摄影是极其鲜见的，但似乎能够与以上三种并列讨论。

此种摄影英文称之为Slit-Scan，英文直译为“狭缝扫描”，笔者称之为“扫描摄影”。

这里我们以焦平面快门（Focal-Plane Shutter）为例解释扫描摄影的原理。

焦平面快门位于片窗与镜头之间紧贴着胶片一侧，快门分为前帘与后帘两个部分，由阻光材料制成。

在曝光时间相对较长的情况下，比如1/50秒，前帘全部打开后，胶片曝光，然后后帘闭合，完成一次曝光过程。

如果曝光时间很短，比如1/2000秒，由于快门帘幕本身的运动速度限制，为了达到1/2000秒的曝光时间，后帘必须在前帘尚未全部打开的情况下就开始运动，胶片在实际曝光过程中并不是整个画面同时曝光，而是通过前后帘形成的“狭缝”进行“扫描”曝光，在这种情况下，胶片上每一点的曝光时间都非常短暂（1/2000秒），而且胶片上不同部分的曝光时刻也不一样，如果被摄物体快速运动，会造成一定程度的变形。

雅克·亨利·拉蒂格拍摄的《德拉奇跑车》，这张照片可能是扫描摄影中最著名的案例，此照片拍摄时间为1912年。

照片中背景人物、电线杆以及前景的汽车都发生了严重的变形，而且前景、后景的变形方向正好相反。

这张照片是位于地面上用固定机位以旋转照相机的方式来拍摄的，相机的旋转方向是从画左摇向画右，快门的运动方向相对于画面是自下而上（由于胶片上呈现的是景物的倒像，快门的实际运动方向是自上而下），同时由于汽车向前行驶的速度大于照相机跟拍的速度，所以背景人物、电线杆与前景的汽车相对于照相机正好是反方向运动，从而造成了不同方向的变形。

如果继续深入研究，不难发现赛车上半部分变形没有下半部分明显，可能是由于在拍摄过程中赛车突然加速造成的。

也有其他很多摄影师利用扫描摄影拍摄出了众多优秀的作品，比如著名的法国摄影大师罗伯特·杜瓦诺（Robert Doisneau， 1912 -1994），美国摄影师安森·西尔（Ansen Seale）等。

影像专业英语词汇汇总

Image Professional English VocabularyCompilationAccuracy: 精确度Ensuring the precision of your images is crucial in the field of imaging.Algorithm: 算法The algorithms used in image processing are the backbone of modern imaging technology.Analog Signal: 模拟信号In contrast to digital signals, analog signals are continuous and vary in a smooth manner.Aperture: 光圈Controlling the aperture size helps to regulate the amount of light entering the camera lens.Aspect Ratio: 宽高比The aspect ratio of an image defines its proportional dimensions, typically expressed as width to height.Bandwidth: 带宽In digital imaging, bandwidth refers to the range of frequencies that can be transmitted without significant loss of quality.Bitmap: 位图A bitmap is a type of image file format that uses pixels to represent images.Brightness: 亮度Adjusting the brightness of an image can significantly impact its visual appeal.CCD (ChargeCoupled Device): CCD（电荷耦合器件）CMOS (Complementary MetalOxideSemiconductor): CMOS（互补金属氧化物半导体）CMOS technology is an alternative to CCD sensors and is widely used in digital imaging devices.Compression: 压缩Contrast: 对比度Enhancing contrast can make an image more dynamic and visually appealing.Depth of Field: 景深The depth of field determines the range of distance that appears acceptably sharp in an image.DPI (Dots Per Inch): DPI（每英寸点数）DPI is a measure of print resolution, indicating the number of ink dots per inch on a printed document.Dynamic Range: 动态范围The dynamic range of an image sensor refers to the range of light intensities it can capture accurately.Exposure: 曝光Proper exposure is key to capturing a wellbalanced image with good detail and color.Expanding the Image Professional English Vocabulary CompilationFile Format: 文件格式Gamma Correction: 伽马校正Gamma correction adjusts the midtone brightness of an image, affecting its overall appearance on different display devices.Histogram: 直方图A histogram is a graphical representation of the distribution of tones in an image, useful for adjusting exposure and contrast.ISO (International Organization for Standardization): ISO （国际标准化组织）In photography, ISO refers to the sensitivity of the camera's sensor to light.JPEG (Joint Photographic Experts Group): JPEG（联合图像专家组）Latitude: 宽容度Latitude in photography refers to the range of over or underexposure that a camera sensor can tolerate without losing image quality.Luminance: 亮度Luminance is a term used to describe the brightness perception of a color, separate from its hue and saturation.Metadata: 元数据Metadata includes information about an image, such asthe date taken, camera settings, and GPS coordinates, embedded within the file.Noise: 噪声Unwanted random variation of brightness or color in an image, often introduced during the capture or processing stages.Pixel: 像素Resolution: 分辨率Image resolution refers to the number of pixels in an image, typically expressed in terms of width and height (e.g., 1920x1080).Sharpening: 锐化Sharpening is a postprocessing technique that enhancesthe edges within an image, making it appear more clear and detailed.White Balance: 白平衡White balance adjustment ensures that the colors in an image appear natural under different lighting conditions.Zoom: 变焦Augmenting the Image Professional English Vocabulary CompilationDynamic Range Compression: 动态范围压缩This technique is used to reduce the contrast between the lightest and darkest areas of an image, bringing out details that might otherwise be lost.HDR (High Dynamic Range): HDR（高动态范围）Interpolation: 插值Interpolation is the process of estimating new data points within the range of known data, often used to increase the size of an image without significant loss of quality.Latitude: 宽容度In the context of film photography, latitude refers to the amount of over or underexposure that can be tolerated the film stock without a loss of image quality.Macro: 微距Macro photography involves capturing extreme closeup images of small subjects, often with a 1:1 or greater magnification ratio.Megapixel: 百万像素A megapixel is a million pixels, and it's used to describe the resolution of digital cameras and the detail level of images they produce.Montage: 蒙太奇Panorama: 全景A panorama is a wideangle view or representation of a physical space, whether in photography, painting, or mapping.Pixelation: 像素化RAW: 原始格式RAW is an image file format that contains minimally processed data from the image sensor, offering the most flexibility during postprocessing.Rule of Thirds: 三分法Saturation: 饱和度Saturation refers to the intensity or purity of a color, with high saturation being vivid and low saturation being more muted or grayish.Silhouette: 剪影A silhouette is a representation of the outline of an object, filled in with a solid color, typically black, emphasizing the shape rather than the details.。