A Robust Registration Method for Multi-view SAR Images based on Best Buddy Similarity

Due to the influence of imaging angle and terrain undulation, multi-view synthetic aperture radar (SAR) images are difficult to be directly registered by traditional methods. Although feature matching solves the issue of image rotation and maintains scale invariance, these methods often lead to non-uniformity of interest points and may not achieve subpixel accuracy. The traditional template matching method makes it difficult to generate correct matches for multi-view SAR oblique images. In this paper, a multi-view SAR image template matching method based on Best Buddy Similarity (BBS) is proposed to solve the traditional methods' problem. Firstly, the initial correspondences between images are established according to the Range-Doppler model of SAR images. Secondly, a sliding window search is performed on the established correspondence, the BBS is calculated, and the subpixel locations of the peaks on the similarity map are estimated to achieve a fine match. In the calculation process of BBS, the SAR-ROEWA operator is used to suppress the speckle noise of SAR images. The experiment demonstrated that SAR-BBS can accurately match SAR images with large rotation angle. The peak value on the search window is significant. The registration accuracy of SAR-BBS outperforms the other state-of-the-art methods

Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs) - pairs of points in source and target sets that are mutual nearest neighbours, i.e., each point is the nearest neighbour of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features.Israel Science Foundation (Grant 1917/2015)National Science Foundation (U.S.) (1212849)Shell Researc

The Method of Automatic Knuckle Image Acquisition for Continuous Verification Systems

The paper proposes a method of automatic knuckle image acquisition for continuous verification systems. The developed acquisition method is dedicated for verification systems in which the person being verified uses a computer keyboard. This manner of acquisition enables registration of the knuckle image without interrupting the user’s work for the time of acquisition. This is an important advantage, unprecedented in the currently known methods. The process of the automatic location of the finger knuckle can be considered as a pattern recognition approach and is based on the analysis of symmetry and similarity between the reference knuckle patterns and live camera image. The effectiveness of the aforesaid approach has been tested experimentally. The test results confirmed its high effectiveness. The effectiveness of the proposed method was also determined in a case where it is a part of a multi-biometric method

Exploiting Intrinsic Clustering Structure in Discrete-Valued Data Sets for Efficient Knowledge Discovery in the Presence of Missing Data

Scalable algorithm design has become central in the era of large-scale data analysis. The vast amounts of data pouring in from a diverse set of application domains, such as bioinformatics, recommender systems, sensor systems, and social networks, cannot be analyzed efficiently using many data mining and statistical tools that were designed for a small scale setting. It is an ongoing challenge to the data mining, machine learning, and statistics communities to design new methods for efficient data analysis. Confounding this challenge is the noisy and incomplete nature of real-world data sets. Research scientists as well as practitioners in industry need to find meaningful patterns in data with missing value rates often as high as 99%, in addition to errors in the data that can obstruct accurate analyses. My contribution to this line of research is the design of new algorithms for scalable clustering, data reduction, and similarity evaluation by exploiting inherent clustering structure in the input data to overcome the challenges of significant amounts of missing entries. I demonstrate that, by focusing on underlying clustering properties of the data, we can improve the efficiency of several data analysis methods on sparse, discrete-valued data sets. I highlight new methods that I have developed with my collaborators for three diverse knowledge discovery tasks: (1) clustering genetic markers into linkage groups, (2) reducing large-scale genetic data to a much smaller, more accurate representative data set, and (3) computing similarity between users in recommender systems. In each case, I point out how the underlying clustering structure can be used to design more efficient algorithms, even when high missing value rates are present