国际会议英文主持词

国际会议英文主持词

篇一：英文国际会议讲稿

PPT(1)

大家上午好！今天我汇报的主题是：基于改进型LBP算法的运动目标检测系统。运动目标检测技术能降低视频监控的人力成本，提高监控效率，同时也是运动目标提取、跟踪及识别算法的基础。图像信号具有数据量大，实时性要求高等特征。随着算法的复杂度和图像清晰度的提高，需要的处理速度也越来越高。幸运的是，图像处理的固有特性是并行的，尤其是低层和中间层算法。这一特性使这些算法，比较容易在FPGA等并行运算器件上实现，今天汇报的主题就是关于改进型LBP算法在硬件上的实现。

good morning everyone.

My report is about a Motion Detection System Based on Improved LBP Operator.

Automatic motion detection can reduce the human cost of video surveillance and improve efficiency [? f??(?)ns?]，it is also the fundament of object extraction, tracking and recognition

[rek?g n??(?)n]. In this work, efforts [ ef?ts] were made to establish the background model which is resistance to the variation of illumination. And our video surveillance system was realized on a FPGA based platform. PPT(2)

目前，常用的运动目标检测算法有背景差分法、帧间差分法等。帧间差分法的基本原理是将相邻两帧图像的对应像素点的灰度值进行减法运算，若得到的差值的绝对值大于阈值，则将该点判定为运动点。但是帧间差分检测的结果往往是运动物体的轮廓，无法获得目标的完整形态。

Currently, Optic Flow, Background Subtraction and Inter-frame difference are regard as the three mainstream algorithms to detect moving object.

Inter-frame difference based method need not model [ m?dl] the background. It detects moving objects based on the frame difference between two continuous frames. The method is easy to be implemented and can realize real-time detection, but it cannot extract the full shape of the moving objects [6].

PPT(3)

在摄像头固定的情况下，背景差分法较为简单，且易于实现。若背景已知，并能提供完整的特征数据，该方法能较准确地检测出运动目标。但在实际的应用中，准确的背景模型很难建立。如果背景模型如果没有很好地适应场景的变化，将大大影响目标检测结果的准确性。像这副图中，背景模型没有及时更新，导致了检测的错误。

The basic principle of background removal method is building a background model and providing a classification of the pixels into either foreground or background [3-5]. In a complex and dynamic environment, it is difficult to build a robust [r?(?) b?st] background model.

PPT(4)

上述的帧间差分法和背景差分法都是基于灰度的。基于灰度的算法在光照条件改变的情况下，性能会大大地降低，甚至失去作用。

The algorithms we have discussed above are all based on grayscale. In practical applications especially outdoor environment, the grayscales of each pixel are unpredictably shifty because of the variations in the intensity and angle of illumination.

PPT(5)

为了解决光照改变带来的基于灰度的算法失效的问题，我们考虑用纹理特征来检测运动目标。而LBP算法是目前最常用的表征纹理特征的算法之一。首先在图像中提取相邻9个像素点的灰度值。然后对9个像素中除中心像素以外的其他8个像素做二值化处理。大于

等于中心点像素的，标记为1，小于的则标记为0。最后将中心像素点周围的标记值按统一的顺序排列，得到LBP值，图中计算出的LBP值为10001111。当某区域内所有像素的灰度都同时增大或减小一定的数值时，该区域内的LBP值是不会改变的，这就是LBP对灰度的平移不变特性。它能够很好地解决灰度受光照影响的问题。

In order to solve the above problems, we proposed an improved LBP algorithm which is resistance to the variations of illumination.

Local binary pattern (LBP) is widely used in machine vision applications such as face detection, face recognition and moving object detection [9-11]. LBP represents a relatively simple yet powerful texture descriptor which can describe the relationship of a pixel with its immediate neighborhood. The fundamental of LBP operator is showed in Fig 1. The basic version of LBP produces 256 texture patterns based on a 9 pixels neighborhood. The neighboring pixel is set to 1 or 0 according to the grayscale value of the pixel is larger than the value of centric pixel or not. For example, in Fig1 7 is larger than 6, so the pixel in first row first column is set to 1. Arranging the 8 binary numbers in certain order, we get an 8 bits binary number, which is the LBP pattern we need. For example in Fig.1, the LBP is 10001111. LBP is tolerant [ t?l(?)r(?)nt] against illumination changing. When the grayscales of pixels in a 9 pixels window are shifted due to illumination changing, the LBP value will keep unchanged.