21-08-2014, 04:05 PM
The problem of determining the location and orientation of straight lines and circles in images is often encountered in the fields of computer vision and image processing. Traditionally the Hough transform has been widely used to solve this problem for binary images, due to its simplicity and effectiveness. In this report I discuss the problem of detecting straight lines in gray-scale images. I treat the input image as noisy observations, which are related to the underlying transform domain image through the inverse Hough transform operator. In this report Hough Transform and its algorithm has been discussed I present four different forms of such constraints and demonstrate their effectiveness. Finally I show how our scheme can be alternatively viewed as one of finding an optimal representation of the image in terms of elements chosen from a redundant dictionary of lines, and thus is a form of adaptive signal representation. Hough Transform. The advantages and disadvantages of the same have also been discussed here. Due the simplicity Hough Transform is used in many applications and I have presented some of then applications. Hough Transform can implemented using Matlab. First Edges has to found out in the image and then Hough Transform can be applied to the processed image. The results for the detection of line and circle using Matlab has been shown in this report.