iet computer vision,Computer,vision_,Matlab,,presentation_11.6,computer vision,computer vision 讲义

发布时间:2012-05-25 来源: matlab vision

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Matlab presentation Computer Vision 220111XX 刘 XX 2014/10/27 Today I will do a presentation about Computer Vision. This is the outline of my presentation. First, I will do a brief introduction about Computer Vision and its applications. Then I will show you something about OpenCV. Finally, we will talk something about the development of Computer Vision. Now I will do a brief introduction about Computer Vision. So, what is Computer vision? There are some pictures about the applications of Computer Vision, for example, robotics[r ? ? 'b ? t ? ks] 机 器 人 学 and verify faces. Part 1 A brief introduction about Computer Vision Computer Vision, also known as ‘Machine Vision’, is a science about studying how to make a machine to ‘see’. Furthermore, that is, instead of the human eyes, we can use cameras and computers to track, measure and identify the target and further to do graphics['gr? f? ks] with computer processing to make the target more suitable for the human eye to detect or transmit images to the instrument. As a scientific discipline, computer vision researches relevant theories and techniques and tries to build an artificial intelligence systems which can get 'information'

from the image or multidimensional image data. As for the perception [p?'sep?(?)n] 感知 can be seen as a subject of extracting information from the senses, so the computer vision can be seen as a science of how to make a manual ['m?nj?(?)l] system to ‘sense’ from images or multidimensional image data. All in all, the purpose of Computer Vision is to mimic ['m?m?k] 模仿 the functions of human vision system as more as possible . And so the guiding principle of Computer Vision is that ‘If the eye can do it, so should the machine’. Finally, let us talk about the key components of Computer Vision. Computer Vision consists of Digital Camera, Pre-procesing,Feature Extraction and Object Recognition. Furthermore, they can also be classified as Low-Level Vision, Mid-Level Vision and High-Level Vision. The structure and algorithms do not ['?lg?r??(?)mz] 算 法 necessarily mimic the actual process of human visual perception, but may utilise some results of biological and cognitive ['k?ɡn?t?v] 认知的 studies. Next stage, let us talk about the applications of Computer Vision. Part 2 Applications of Computer Vision , There are many applications of Computer Vision. Such as, robotics, video surveillance[s?'ve?l(?)ns;

intelligent cruise control automatic assembly lines 智能巡航体系 -'ve??ns] 监 视 , automatic parking, , automatic visual 自动装配生产线 inspection, target detection, human-computer interface and so on. Next stage, I will give you some examples of its applications. First, let us pay attention to Automatic Visual Inspection 自动视觉检测 or verify faces. Just as the picture says, when we open or log in the computer, the computer can recognize and detect the face of users. And the computer just use our face to be a password, so different users have different accounts correspondingly. There are also many software or apps which use this function to protect our accounts and private information. In the following stage, our topic is about Intelligent Cruise Control. In the Computer Vision, with the camera and image processing, out target can cruise automatically and intelligently. Now, please watch a short video about Intelligent Cruise Control. Finally, we will do some research about Human-Computer Interface. People can interact [?nt?r'?kt] with computer with the function of Computer Vision. Here, I will give you a video about Using Kinect for Windows with MATLAB. Next stage, I will introduce a new theory or a new subject to you which is called OpenCV. Part 3 OpenCV First I will do a brief introduction about OpenCV and then we will pay much attention to Image Processing and Face Detection based on OpenCV. OpenCV is a cross-platform computer vision library which is based on the open source Released. And OpenCV can run on Linux, Windows and Mac OS operating systems. It is lightweight but with high efficient. OpenCV consists of a series of C functions and a few C ++ classes. What is more, OpenCV also provides an interface of Python, Ruby['ru?b?], MATLAB and other languages. Furthermore, many common algorithms for image processing and computer vision are also realized on the platform of OpenCV. In the following stage, we will pay attention to Image Processing. First, let us talk something about smoothing of image. Smoothing, also called blurring[bl?]模糊, is a simple and frequently used image processing operation. There are many reasons for smoothing, but it is usually done to reduce noise or camera artifacts. Smoothing is also important when we wish to reduce the resolution [rez?'lu??(?)n] 分辨率 有原则的 of an image in a principled ['pr?ns?p(?)ld] way. OpenCV offers five different smoothing operations at this time. All of them are supported through one function, cvSmooth(), which takes our desired form of smoothing as an argument. void cvSmooth( const CvArr* src, CvArr* dst, int smoothtype = CV_GAUSSIAN, int param1 = 3, int param2 = 0, double param3 = 0, double param4 = 0 ); The src and dst arguments are the usual source and destination for the smooth operation. This pictures can reflect the function of smoothing. And here is an example of smoothing. Second, let us talk something about resize of image. We often encounter an image of some size that we would like to convert to an image of some other size. We may want to upsize (zoom in) or downsize (zoom out) the image;

we can accomplish either task by using cvResize(). This function will fit the source image exactly to the destination image size. void cvResize( const CvArr* src, CvArr* dst, int interpolation = CV_INTER_LINEAR ); If the ROI is set in the source image then that ROI will be resized to fit in the destination image. Likewise, if an ROI is set in the destination image then the source will be resized to fit into the ROI. And here is an example of resize. Third, let us talk something about threshold of image. Frequently we have done many layers of processing steps and want either to make a final decision about the pixels['piks?l]像素 in an image or to categorically [,k?ti'g?rikli] 绝对地 reject those pixels below or above some value while keeping the others. The OpenCV function cvThreshold() accomplishes these tasks. double cvThreshold( CvArr* src, CvArr* dst, double threshold, double max_value, int threshold_type );

The basic idea is that an array is given, along with a threshold, and then something happens to every element of the array depending on whether it is below or above the threshold. And here is an example of threshold. Finally, I will do some introduction about Face Detection. Face Detection can divide into four parts: Face detection, Face preprocessing, Collect and learn faces and Face recognition. In the stage of face detection, the computer shall detect whether the target is a face or not. Then, face preprocessing, with the functions of OpenCV,the computer can extract the feature of the face. In the next stage---collect and learn faces, the computer shall collect and learn lots of features of face. Finally, with the previous preparation, the computer can detect or recognize most of faces in a new image. Here are the source code of Face Detection based on OpenCV which used the language of Java. Main Function Test For example, if I just stand in front of a camera and with the function of OpenCV, the computer can detect that this is a face and a person’s face, and not a book. And this is a result of Face Detection.The computer detects that there are eight faces in the picture. However, in fact there are seven faces in the picture. It is obviously that the computer also detects the book as a person’s face and make a mistake. At the end of my presentation, we will talk about the development of Computer Vision for a few minutes. Part 4 Development of Computer Vision As a science about studying how to make a machine to ‘see’, Computer Vison is becoming a more and more important role in our life. As my opinion, 3D reconstruction, especially the application of Kinect, Recognition and Tracking will develop fast and become more and more important in the future. Ok. That is all. Thank you for your attention. Any question?

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Representation for puter Vision — Theory, Algorithms, and Applications Additive Kernels and Explicit Embeddings for Large Scale puter Vision Problems Using MATLAB ...

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iet computer vision,Computer,vision_,Matlab,,presentation_11.6,computer vision,computer vision 讲义