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.

Unveiling Optical Properties in Underwater Images

Abstract: The appearance of underwater scenes is highly governed by the optical properties of the water (attenuation and scattering). However, most research effort in physics-based underwater image reconstruction methods is placed on devising image priors for estimating scene transmission, and less on estimating the optical properties. This limits the quality of the results. This work focuses on robust estimation of the water properties. First, as opposed to previous methods that used fixed values for attenuation, we estimate it from the color distribution in the image. Second, we estimate the veiling-light color from objects in the scene, contrary to looking at background pixels. We conduct an extensive qualitative and quantitative evaluation of our method vs. most recent methods on several datasets. As our estimation is more robust our method provides superior results including on challenging scenes.

Matching and Predicting Street Level Images

Abstract: The paradigm of matching images to a very large dataset has been used for numerous vision tasks and is a powerful one. If the image dataset is large enough, one can expect to find good matches of almost any image to the database, allowing label transfer [3, 15], and image editing or enhancement [6, 11]. Users of this approach will want to know how many images are required, and what features to use for finding semantic relevant matches. Furthermore, for navigation tasks or to exploit context, users will want to know the predictive quality of the dataset: can we predict the image that would be seen under changes in camera position? We address these questions in detail for one category of images: street level views. We have a dataset of images taken from an enumeration of positions and viewpoints within Pittsburgh. We evaluate how well we can match those images, using images from non-Pittsburgh cities, and how well we can predict the images that would be seen under changes in camera position. We compare performance for these tasks for eight different feature sets, finding a feature set that outperforms the others (HOG). A combination of all the features performs better in the prediction task than any individual feature. We used Amazon Mechanical Turk workers to rank the matches and predictions of different algorithm conditions by comparing each one to the selection of a random image. This approach can evaluate the efficacy of different feature sets and parameter settings for the matching paradigm with other image categories.

On the Reprojection of 3D and 2D Scenes Without Explicit Model Selection

Abstract: It is known that recovering projection matrices from planar configurations is ambiguous, thus, posing the problem of model selection -- is the scene planar (2D) or non-planar (3D)? For a 2D scene one would recover a homography matrix, whereas for a 3D scene one would recover the fundamental matrix or trifocal tensor. The task of model selection is especially problematic when the scene is neither 2D nor 3D -- for example a "thin" volume in space. In this paper we show that for certain tasks, such as reprojection, there is no need to select a model. The ambiguity that arises from a 2D scene is orthogonal to the reprojection process, thus if one desires to use multilinear matching constraints for transferring points along a sequence of views it is possible to do so under any situation of 2D, 3D or "thin" volumes.

SampleNet: Differentiable Point Cloud Sampling

Abstract: There is a growing number of tasks that work directly on point clouds. As the size of the point cloud grows, so do the computational demands of these tasks. A possible solution is to sample the point cloud first. Classic sampling approaches, such as farthest point sampling (FPS), do not consider the downstream task. A recent work showed that learning a task-specific sampling can improve results significantly. However, the proposed technique did not deal with the non-differentiability of the sampling operation and offered a workaround instead. We introduce a novel differentiable relaxation for point cloud sampling that approximates sampled points as a mixture of points in the primary input cloud. Our approximation scheme leads to consistently good results on classification and geometry reconstruction applications. We also show that the proposed sampling method can be used as a front to a point cloud registration network. This is a challenging task since sampling must be consistent across two different point clouds for a shared downstream task. In all cases, our approach outperforms existing non-learned and learned sampling alternatives. Our code is publicly available at this https URL.

The power of feature clustering: An application to object detection

Abstract: We give a fast rejection scheme that is based on image segments and demonstrate it on the canonical example of face detection. However, instead of focusing on the detection step we focus on the rejection step and show that our method is simple and fast to be learned, thus making it an excellent pre-processing step to accelerate standard machine learning classifiers, such as neural-networks, Bayes classifiers or SVM. We decompose a collection of face images into regions of pixels with similar behavior over the image set. The relationships between the mean and variance of image segments are used to form a cascade of rejectors that can reject over 99.8% of image patches, thus only a small fraction of the image patches must be passed to a full-scale classifier. Moreover, the training time for our method is much less than an hour, on a standard PC. The shape of the features (i.e. image segments) we use is data-driven, they are very cheap to compute and they form a very low dimensional feature space in which exhaustive search for the best features is tractable.

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.