Shai Avidan

Bio: Shai Avidan is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Pixel & Template matching. The author has an hindex of 50, co-authored 138 publications receiving 15378 citations. Previous affiliations of Shai Avidan include Mitsubishi Electric Research Laboratories & Mitsubishi.

Threading fundamental matrices

Abstract: We present a new function that operates on fundamental matrices across a sequence of views. The operation, we call "threading", connects two consecutive fundamental matrices using the trifocal tensor as the connecting thread. The threading operation guarantees that consecutive camera matrices are consistent with a unique 3D model, without ever recovering a 3D model. Applications include recovery of camera ego-motion from a sequence of views, image stabilization across a sequence, and multi-view image based rendering.

Semi-Global Matching: A Principled Derivation in Terms of Message Passing

Abstract: Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.

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

Abstract: 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.

TreeCANN - k-d tree coherence approximate nearest neighbor algorithm

Abstract: TreeCANN is a fast algorithm for approximately matching all patches between two images It does so by following the established convention of finding an initial set of matching patch candidates between the two images and then propagating good matches to neighboring patches in the image plane TreeCANN accelerates each of these components substantially leading to an algorithm that is ×3 to ×5 faster than existing methods Seed matching is achieved using a properly tuned k-d tree on a sparse grid of patches In particular, we show that a sequence of key design decisions can make k-d trees run as fast as recently proposed state-of-the-art methods, and because of image coherency it is enough to consider only a sparse grid of patches across the image plane We then develop a novel propagation step that is based on the integral image, which drastically reduces the computational load that is dominated by the need to repeatedly measure similarity between pairs of patches As a by-product we give an optimal algorithm for exact matching that is based on the integral image The proposed exact algorithm is faster than previously reported results and depends only on the size of the images and not on the size of the patches We report results on large and varied data sets and show that TreeCANN is orders of magnitude faster than exact NN search yet produces matches that are within 1% error, compared to the exact NN search

Multiple histogram matching

Abstract: Histogram Matching (HM) is a common technique for finding a monotonic map between two histograms. However, HM cannot deal with cases where a single mapping is sought between two sets of histograms. This paper presents a novel technique that finds such a mapping in an optimal manner under various histograms distance measures.

Multiple View Geometry in Computer Vision.

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Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

Abstract: Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Object tracking: A survey

Abstract: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.