This paper describes a new shape matching using image projection features. The corners are located in the binary image using the radius vector function. The imaginary line joining any two corners is called the baseline, if the distance between those two corners is the maximum. The image is rotated to align the baseline with the reference axis. Horizontal and vertical projections of the rotated image are drawn. The projections are matched with the projections of the database images using the sorted normalized matching algorithm. The algorithm is tested on various test images.
In this paper, we proposed a method, which is very fast and gives better moving information of the objects in the image sequences. The possible locations of moving objects are found first, and then we apply the Hough Transform only on the detected moving regions to find the optical flow vectors for those regions only. So we save lot of time for not finding optical flow for the still or background parts in the image sequences. The new Boolean based edge detection is applied on the two consecutive input images, and then the differential edge image of the resulting two edge maps is found. A mask for detecting the moving regions is made by dilating the differential edge image. After getting the moving regions in the image sequence with the help of the mask obtained already, we use the Hough Transform and voting accumulation methods for solving optical flow constraint equations. The voting based Hough transform avoids the errors associated with least squares techniques. Calculation of a large number of points along the constraint line is also avoided by using the transformed slope-intercept parameter domain. The simulation results show that the proposed method is very effective for extracting optical flow vectors and hence tracking moving objects in the images.
Tracking of moving objects is one of the application techniques with complex processing for understanding input images. In this paper, we have considered optical flow which is one of moving object tracking algorithms. We proposed a new method using the Combinatorial Hough Transform (CHT) and Voting Accumulation in order to find optimal constraint lines. Also, we used the logical operation in order to release the operation time. The proposed method can extract the optical flow of the moving object. Then, the moving information was computed using the extracted optical flow. We have simulated the proposed method using test images including the noise.