COMPUTER VISION TECHNIQUESComputer vision is the science that develops the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from an observed image, from a set of images. , or sequence of images from calculations carried out by specialized or general purpose computers (notes). The main goal of computer vision applications is to produce automated recognition systems capable of matching or even surpassing human performance. Computer vision can be used to enable new relationship techniques and connect the physical and virtual worlds. Some computer vision techniques are used to produce better quality of computer vision application. Image Processing Image processing is a technique in which data from an image is digitized and various mathematical operations are applied to the data, usually with a digital computer. , in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the interpretation and recognition tasks usually performed by humans. Image processing is separated into two levels: lower level processing and higher level. treatment at the level. Lower level processing takes the image pixels as input and performs tasks such as image enhancement, feature extraction, and image segmentation. Higher level processing takes the output of lower level processing as input and generates system related output. An example of tasks performed in higher-level processing includes vehicle tracking (T., 1998). In general, image processing operations can be classified into four types (Y., 2010), namely:1. Pixel operations: the output on a pixel depends only on the input on that pixel, independent...... middle of paper ...... and calculate the M medians of the squares of the differences between the corresponding points and the transformed points. Then select the affine parameters for which the median of the squared difference is the minimum. According to the above procedure, the probability p that at least one set of background data and their corresponding points are obtained is derived from the following equation.〖p(ε ,q,M)=1-(1 -((1-ε)q)〗^3) ͫWhere ε(<0.5) is the ratio of moving object regions to the entire image and q is the probability that the corresponding points are correctly found. This method will provide an accurate and reliable model. REFERENCES Behard, A., Shahrokni, A. and Motanedi, S, A. (2002). A robust vision-based moving target detection and tracking system. Kaukorata, T. and Smed, J. (2006). Role of the pattern recognition platform. T., LC (1998). Image processing and pattern recognition. Elsevier.