Study And Implement On Computer Vision Detection Algorithm

Vision is an important media for people to observe and realize the world. People feel the world through vision, feeling, hearing and smelling, however, 80 percents of the information is gotten through vision. Computer vision is the way that people use computer to realize their vision, which is the feeling, recognition and comprehension of objective in the real world. Computer vision is a new and rapidly growing research field, and it is widely used in many areas. Computer vision techniques will show their predominance in some situation that can't be felt by people, such as the detection of danger and so on.This paper is about the computer vision, which is part of the national creative fund project "Automatic Anti-explosive and Volume Measurable Pot Feeding System ". We applied the algorithm of the computer vision and achieved the hardware design and it solved the problems as follows: 1. Effective extraction and accurate recognition of the video signal in industry spot.2. The rapid calibration of the camera parameters and it includes digital camera model and on-line debug of each parameter.3. Complement the automatic pot feeding process. 4. The design of the interface between people and computer.The system algorithm includes the calibration of the camera, the enhancement of the image, the extraction of the image feature and the control of the bi-axis step motor.Firstly, because of the request for high precision of the pot feeding process and the application of the single camera in this system, we must allow for the distortion of the shot. So we calibrated the parameters of the camera through the general camera model. 11 parameters are necessary for the transmission from the 3-D world coordinate to the computer image coordinate, among which 5 are inner parameters. They are focus , shot distortion parameter, undetermined image scale parameter and the computer image coordinate and of the image origin. The other external parameters are : three Oula angles(,,)in and three parameters(,,)in . In this system we applied template based calibration: Put an object with the known shape and size in front of the camera, which is called the calibrated object. Specify the calibrating parameters through online software and with the least mean square error theory we have the optimum accuracy for the system.Secondly, we must do filter process to the input image because of the noise from industry spot so as to prepare for the subsequent process. There aren't too much edge information in input gray image while the pulse noise and pepper and salt noise in the spot have great influence on it, it will lead to bad quality. So we think of blending the average filter and median filter into a blend filter and design a blend filter. Average filter is used widely in image processing for its simpleness and high speed. However it has its own shortness such as its blur effects and blurring the edge and details of the image. While some nonlinear filters such as median filter can suppress the random noise especially pulse noise and pepper and salt noise without blurring the image edge. But it's not as good as the average filter when used to little detail image with gauss noise. In blend filter, we select square error of the image to compare with a threshold because it reflects the abundance of image detail, more detail, bigger . Selecting as control parameter can make average filter and median filter exert their own advantage to get better filter result.Thirdly, for the captured image, we should analyze it with some algorithm to get the necessary information. In this system, the position of the pot and the information of the poison of the feeding pipe should be gotten from the input image, so as to drive the feeding pipes to feed the pot.First we compared several general edge detection operators and chose the Sobel operator because it can not only detect the clear boundary but also provide the grads information.Then we detect the center of the pot and the position of the feeding pot through...