Ultrasonography is non-invasive and painless. In Ultrasonography the images are often affected with Speckle noise. It is a multiplicative noise. To help the doctors to identify the abnormalities properly there are several methods to diagnose as speckle is a major problem. This paper gives details about popular spatial domain, transform domain, CNN techniques for despeckling in ultrasound images. Transform domain methods like Wavelet methods, Curvelet methods, Bayes Shrink methods are prominent among many researches. Deep learning based methods are evolving like DnCNN, ECNDNet etc. for efficient despeckling. An overview of the methods is given here with certain measurement parameters like PSNR, MSE.

Fingerprint matching algorithm is a key step in fingerprint recognition system. Though there are many existing matching algorithms, there has been inability to match fingerprints in linear time. In this paper we present a novel biometric approach to match fingerprints that run in linear time. We match the minutiae in the fingerprint by constructing a Nearest Neighbor Vector (NNV) considering its k-nearest neighbors. The consolidation of these matched minutiae points is done by incorporating them in binary tree that propagates simultaneously in both fingerprints. This helps our algorithm to run in 0(n) time in contrast to many existing algorithms when reference core point is available. We analyze the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database.

The paper proposes a parametric approach for color based tracking. The method fragments a multimodal color object into multiple homogeneous, unimodal, fragments. The fragmentation process consists of multi level thresholding of the object color space followed by an assembling. Each homogeneous region is then modelled using a single parametric distribution and the tracking is achieved by fusing the results of the multiple parametric distributions. The advantage of the method lies in tracking complex objects with partial occlusions and various deformations like non-rigid, orientation and scale changes. We evaluate the performance of the proposed approach on standard and challenging real world datasets.

The paper proposes a novel approach for classification of sports images based on the geometric information encoded in the image of a sport's field. The proposed approach uses invariant nature of a crossratio under projective transformation to develop a robust classifier. For a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using Hough transform. These cross-ratios are represented by a histogram which forms a feature vector for the image. An SVM classifier trained on aprior model histograms of crossratios for sports fields is used to decide the most likely sport's field in the image. Experimental validation shows robust classification using the proposed approach for images of Tennis, Football, Badminton, Basketball taken from dissimilar view points.

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