No abstract

Block matching and optical flow algorithms are the two major motion estimation techniques that are widely employed today. The main aim of this paper is to compare the above two algorithms in terms of processing time, Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Mean Opinion Score (MOS). An exhaustive search block matching algorithm which has the maximum efficiency compared to any other block matching algorithm as well as the Brox's optical flow estimation algorithm are implemented. The algorithms are optimized by selecting appropriate parameter values that gives the best result. Then the algorithms are compared based on their motion estimated image for the same input image sequence and finally the results obtained are analyzed.

No abstract

Tuberculosis is a health threatening disease with high mortality and morbidity rates. So proper tools are required to diagnose the disease at the right time. To address this issue, we propose a novel scheme for detecting tuberculosis in chest X-ray images. The method detects tuberculosis in a three stage process namely segmentation, feature extraction and classification. The lung region is segmented using adaptive thresholding. Then feature extraction extracts information contained in the image. These feature set is given to support vector machine to distinguish between normal and abnormal chest image. The algorithm is evaluated using four performance measuring criteria : accuracy, sensitivity, specificity and area under the ROC curve (AUC). Simulation results reveal the efficiency of the method in detecting tuberculosis.