Round Bar End Of The Study Of Image Recognition And Processing

In this dissertation, includes the contents of three parts mainly: basic process of binary image, object segmentation and background cut apart of image, circle object detection.We are tying the question of counting of the round stock, Achieve the goal through recognizing the picture of round stock section. We use Otsu's automatic threshold selection method during the binarization in partitioned windows, a discriminating method based on the pyramid data structure of Lorentz information measure is put forward.we propose a method based on mathematical morphology for segmenting images of round stock section, so we have received the edge picture of the goal picture.In order to segment object and the background noise: First we separate the foreground speck noise and the noisy background in the image by means of the gray-scale skeleton transformation and the concept of maximal inscribed blocks. By removing the varying background and speck noise, the image is enhanced. To successfully recognize and separate the unwanted components, a size characterization algorithm is formulated based on the gray-scale morphological opening. Finally, a global thresholding can be applied to the enhanced image to obtain the object from the background.There are a large amount of useless accumulations yielded byrandom sampling when randomized Hough transform(RHT) is used to detect circles in complex images, so we proposes an improved RHT. It uses gradient direction information to determine whether the parameter based on the two sampled points should be accumulated or not. In comparison with the original RHT, the problem of useless accumulations is well solved and the method has higher speed, smaller storage. Direct against the object is not standard circle, we use the new algorithm combines two methods of cluster analysis and fuzzy recognition, recognizes step by step from coarse to fine, sets up classifier at every level. It solves the problem of uncertain objects classification successfully, implements recognition of irregular quasi-circular object accurately.Finally, we put forward the plan of design of the whole recognition system, lay the foundation for the system's research work afterwards.