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Multiple View Geometry in Computer Vision.

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.

Machine Learning : A Probabilistic Perspective

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.

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

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.

Random graphs

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.

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Object tracking: A survey

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a realizable analytic expression that approximates the multi-class K-NN classification error in the presence of label noise, which is of independent importance. We then suggest that the expression for K-NN may serve as a first-order approximation for the DNN error. Finally, we demonstrate empirically the proximity of the developed expression to the observed performance of K-NN and DNN classifiers. Our result may explain the already observed surprising resistance of DNN to some types of label noise. It also characterizes an important factor of it showing that the more concentrated the noise the greater is the degradation in performance.

The Resistance to Label Noise in K-NN and DNN Depends on its Concentration

The field of self-supervised monocular depth estimation has seen huge advancements in recent years. Most methods assume stereo data is available during training but usually under-utilize it and only treat it as a reference signal. We propose a novel self-supervised approach which uses both left and right images equally during training, but can still be used with a single input image at test time, for monocular depth estimation. Our Siamese network architecture consists of two, twin networks, each learns to predict a disparity map from a single image. At test time, however, only one of these networks is used in order to infer depth. We show state-of-the-art results on the standard KITTI Eigen split benchmark as well as being the highest scoring self-supervised method on the new KITTI single view benchmark. To demonstrate the ability of our method to generalize to new data sets, we further provide results on the Make3D benchmark, which was not used during training.

Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation

Boundary snapping is an interactive image cutout algorithm that requires a small number of user supplied control points, or landmarks, to infer the cutout contour. The key idea is to match the appearance of all points along the desired contour to the landmark points, where appearance is given by an intensity profile perpendicular to the boundary. An optimization process attempts to find a contour that maximizes the similarity score of its points with the landmarks. This approach works well in the typical case where the foreground and background differ in appearance, as well as in challenging cases where the subject is clearly perceived, but the regions on both sides of the boundary are similar and cannot be easily discriminated. By enabling the user to define the boundary points directly, the technique is not limited to boundaries that necessarily have to be the most salient or high gradient feature in the region. It can also be used for margin cutout around the boundary. The use of multiple control points along the boundary can handle spatially varying attributes as both foreground and background may change in appearance along the boundary. The final result is accurate, because it allows the user to enforce hard constraints on the boundary directly, at the expense of moderate user labor in positioning the landmark points. Finally, the algorithm is fast, works on a variety of images, and handles situations where the boundary is not obvious.

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Boundary snapping for robust image cutouts

We present a probabilistic algorithm for finding correspondences across multiple images. The algorithm runs in a distributed setting, where each camera is attached to a separate computing unit, and the cameras communicate over a network. No central computer is involved in the computation. The algorithm runs with low computational and communication cost. Our distributed algorithm assumes access to a standard pairwise wide-baseline stereo matching algorithm (\(\mathcal{WBS}\)) and our goal is to minimize the number of images transmitted over the network, as well as the number of times the \(\mathcal{WBS}\) is computed. We employ the theory of random graphs to provide an efficient probabilistic algorithm that performs \(\mathcal{WBS}\) on a small number of image pairs, followed by a correspondence propagation phase. The heart of the paper is a theoretical analysis of the number of times \(\mathcal{WBS}\) must be performed to ensure that an overwhelming portion of the correspondence information is extracted. The analysis is extended to show how to combat computer and communication failures, which are expected to occur in such settings, as well as correspondence misses. This analysis yields an efficient distributed algorithm, but it can also be used to improve the performance of centralized algorithms for correspondence.

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Probabilistic Multi-view Correspondence in a Distributed Setting with No Central Server

Time-varying phenomenon, such as ripples on water, trees waving in the wind and illumination changes, produces false motions, which significantly compromises the performance of an outdoor-surveillance system. In this paper, we propose a corner-based background model to effectively detect moving-objects in challenging dynamic scenes. Specifically, the method follows a three-step process. First, we detect feature points using a Harris corner detector and represent them as SIFT-like descriptors. Second, we dynamically learn a background model and classify each extracted feature as either a background or a foreground feature. Last, a Lucas-Kanade feature tracker is integrated into this framework to differentiate motion consistent foreground objects from background objects withrandom or repetitive motion. The key insight of our work is that a collection of SIFT-like features can effectively represent the environment and account for variations caused by natural effects with dynamic movements. Features that do not correspond to the background must therefore correspond to foreground moving objects. Our method is computational efficient and works in real-time. Experiments on challenging video clips demonstrate that the proposed method achieves a higher accuracy in detecting the foreground objects than the existing methods.

Shai Avidan

Papers

The Resistance to Label Noise in K-NN and DNN Depends on its Concentration

Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation

Boundary snapping for robust image cutouts

Probabilistic Multi-view Correspondence in a Distributed Setting with No Central Server

Learning a Sparse, Corner-Based Representation for Background Modelling