Research On Removal Algorithm Of Shadows In Image Segmentation

With the rapid development of computer, the technology of digital image processing is developing fast. And people have a growing pursuit of higher image processing effect. However, the effect of digital images is degraded more or less by many inescapability factors during the imaging process. Image shadows is typical phenomenon of the drop of image quality. It will directly affect the precision of image matching, the accuracy of pattern recognition and the automation of objects extraction, which will cause more errors in tracking and recognition of object at post-treatment of intelligent video monitoring. Therefore, it is very important to wipe off shadows to improve work quality of intelligent video monitoring.Against the problem that many methods of image segmentation often bring shadow, this thesis analyzed the principle and optical properties of shadow whose illumination model is also made. Based on the theories of shadow, three effectively algorithms to remove shadow were designed by theories of digital image processing and information. Because the algorithms have different features and one algorithm is adapted to a kind of image, automation algorithm of removement shadows was designed by the algorithms.This thesis explains background, significance, source and present situations of shadow removing in domestic and foreign to clear the purpose of this research. And Chapter One studied the theory of shadow which is an important basis for the removal of the shadow, according to it, do the jobs as follows.Firstly, based on histogram and clustering techniques, a shadow eliminating method which depended on those techniques was presented. It can remove multi-object shadows. The quantity, probably width, borderline of objects, direction and approximately area of shadows could be confirmed by theory of histogram. And this method combined with clustering techniques of gray image, this method could obtain exact area of shadows in order to eliminate shadow of one-object or multi-objects.Secondly, by the use of the optical characteristic of shadow (such as color, veins), a shadow removing algorithm based on chrominance distortion and local Cross Entropy was set up. In the RGB space, we use color vector to calculate the angle between shadow and corresponding background. And based on the limit we can confirm the area of shadows. By the use of Kullback-Leibler divergence in information theory, we can gain further distinction between objects and shadows. The combine of chrominance distortion and Cross Entropy can eliminate shadow of segmentation object in the effect.Thirdly, the shadow removing algorithms based on multi-gradient analysis and line scan were designed according to the continuum and smooth of shadow area gray. Gradient represented the variety of gray and gradient is also much bigger at gray acute variety area. Line scan can search the lines of the image element point width. Gradient can find the continuum area of shadow and line scan can remove little area of non- area of shadow so as to realize purpose of eliminating shadow.Finally, based on the theory and application of three algorithms, the judgment process was designed and the choice of adaptive algorithms of shadows removing was also realized. It reduced human judgments and improves the universality of algorithm. A quantitative method is introduced to evaluate the algorithms on a benchmark.