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Author

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.


Papers
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Journal ArticleDOI
TL;DR: Display Omitted Semi Global Boundary Detection (SGBD) breaks the image into scan lines in multiple orientations, segments each one independently, and combines the results into a final probabilistic 2D boundary map.

5 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a method to detect dynamic regions in CrowdCam images based on the observation that matching static points must satisfy the epipolar geometry constraints, but computing exact matches is challenging, so they compute the probability that a pixel has a match along the corresponding epipolar line.

5 citations

Posted Content
TL;DR: In this paper, a family of novel frequency-domain utilization networks is presented, which utilize the inherent efficiency of the frequency domain by working directly in that domain, represented with the Discrete Cosine Transform.
Abstract: The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and model size. Here, we present FUN, a family of novel Frequency-domain Utilization Networks. These networks utilize the inherent efficiency of the frequency-domain by working directly in that domain, represented with the Discrete Cosine Transform. Using modern techniques and building blocks such as compound-scaling and inverted-residual layers we generate a set of such networks allowing one to balance between size, latency and accuracy while outperforming competing RGB-based models. Extensive evaluations verifies that our networks present strong alternatives to previous approaches. Moreover, we show that working in frequency domain allows for dynamic compression of the input at inference time without any explicit change to the architecture.

5 citations

Posted Content
TL;DR: This work addresses the novel problem of detecting dynamic regions in CrowdCam images – a set of still images captured by a group of people and calculates the probability that a pixel has a match, not necessarily the correct one, along the corresponding epipolar line.
Abstract: We address the novel problem of detecting dynamic regions in CrowdCam images, a set of still images captured by a group of people. These regions capture the most interesting parts of the scene, and detecting them plays an important role in the analysis of visual data. Our method is based on the observation that matching static points must satisfy the epipolar geometry constraints, but computing exact matches is challenging. Instead, we compute the probability that a pixel has a match, not necessarily the correct one, along the corresponding epipolar line. The complement of this probability is not necessarily the probability of a dynamic point because of occlusions, noise, and matching errors. Therefore, information from all pairs of images is aggregated to obtain a high quality dynamic probability map, per image. Experiments on challenging datasets demonstrate the effectiveness of the algorithm on a broad range of settings; no prior knowledge about the scene, the camera characteristics or the camera locations is required.

5 citations

Book ChapterDOI
01 Jan 2009
TL;DR: The fusion of a novel cryptographic protocol and recent advances in computer vision results in a secure and efficient protocol for image matching, which uses a secure fuzzy match of string and sets as its building block.
Abstract: Video surveillance is an intrusive operation that violates privacy. It is therefore desirable to devise surveillance protocols that minimize or even eliminate privacy intrusion. A principled way of doing so is to resort to Secure Multi-Party methods, that are provably secure, and adapt them to various vision algorithms. In this chapter, we describe an Oblivious Image Matching protocol which is a secure protocol for image matching. Image matching is a generalization of detection and recognition tasks since detection can be viewed as matching a particular image to a given object class (i.e., does this image contain a face?) while recognition can be viewed as matching an image of a particular instance of a class to another image of the same instance (i.e., does this image contain a particular car?). And instead of applying the Oblivious Image Matching to the entire image one can apply it to various sub-images, thus solving the localization problem (i.e., where is the gun in the image?). A leading approach to object detection and recognition is the bag-offeatures approach, where each object is reduced to a set of features and matching objects is reduced to matching their corresponding sets of features. Oblivious Image Matching uses a secure fuzzy match of string and sets as its building block. In the proposed protocol, two parties, Alice and Bob, wish to match their images, without leaking additional information. We use a novel cryptographic protocol for fuzzy matching and adopt it to the bag-of-features approach. Fuzzy matching compares two sets (or strings) and declares them to match if a certain percentage of their elements match. To apply fuzzy matching to images, we represent images as a set of visual words that can be fed to the secure fuzzy matching protocol. The fusion of a novel cryptographic protocol and recent advances in computer vision results in a secure and efficient protocol for image matching. Experiments on real images are presented.

5 citations


Cited by
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01 Jan 2001
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

14,282 citations

Book
24 Aug 2012
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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.

8,059 citations

Journal ArticleDOI
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
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.

7,849 citations

Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
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.

7,116 citations

Journal ArticleDOI
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
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.

5,318 citations