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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.

/pdf/object-tracking-a-survey-3706dclxb5.pdf

Object tracking: A survey

http://ieeexplore.ieee.org/iel5/38/4118477/04118492.pdf

Nonparametric Acceleration, Super-Resolution,

Image deshadowing algorithms remove shadows from images. This requires both detecting where the shadow is and, once detected, removing it from the image. This work focuses on the shadow removal part. We follow a common physical shadow formation model and learn its parameters using a deep neural network. Our model consists of an existing network for shadow detection, and a novel network for shadow removal. The shadow removal network gets the predicted mask of the shadow region and the shadow image and predicts six parameters per pixel. Remarkably, a straightforward network architecture, that is considerably smaller compared to alternative methods, produces better results on standard datasets1.

Physics Based Image Deshadowing Using Local Linear Model

How many labeled pixels are needed to segment an image, without any prior knowledge? We conduct an experiment to answer this question. In our experiment, an Oracle is using Active Learning to train a network from scratch. The Oracle has access to the entire label map of the image, but the goal is to reveal as little pixel labels to the network as possible. We find that, on average, the Oracle needs to reveal (i.e., annotate) less than 0.1% of the pixels in order to train a network. The network can then label all pixels in the image at an accuracy of more than 98%. Based on this single-image-annotation experiment, we design an experiment to quickly annotate an entire data set. In the data set level experiment the Oracle trains a new network for each image from scratch. The network can then be used to create pseudo-labels, which are the network predicted labels of the unlabeled pixels, for the entire image. Only then, a data set level network is trained from scratch on all the pseudo-labeled images at once. We repeat both image level and data set level experiments on two, very different, real-world data sets, and find that it is possible to reach the performance of a fully annotated data set using a fraction of the annotation cost.

How Low Can We Go? Pixel Annotation for Semantic Segmentation

Point cloud registration (PCR) is an important task in many ﬁelds including autonomous driving with LiDAR sen-sors. PCR algorithms have improved signiﬁcantly in recent years, by combining deep-learned features with robust estimation methods. These algorithms succeed in scenar-ios such as indoor scenes and object models registration. However, testing in the automotive LiDAR setting, which presents its own challenges, has been limited. The standard benchmark for this setting, KITTI-10m, has essentially been saturated by recent algorithms: many of them achieve near-perfect recall. In this work, we stress-test recent PCR techniques with LiDAR data. We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets. They contain a balanced representation of the different relative motions that appear in a dataset, i.e. small and large rotations, small and large offsets in space and time, and various combinations of these. We perform a thorough comparison of accuracy and run-time on these benchmarks. Perhaps unexpectedly, we ﬁnd that the fastest and simultaneously most accurate approach is a version of advanced RANSAC. We further improve results with a novel pre-ﬁltering method. Full code is available 1 .

Stress-Testing LiDAR Registration

We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. This code can be used to apply fast and memory-efficient approximation to nearest-neighbor searches. Our framework is flexible, enabling different proximity definitions between data points. In contrast to previous methods that extract binary codes based on unsigned graph partitioning, our system models the attractive and repulsive forces in the data by incorporating positive and negative graph weights. The proposed framework is shown to boil down to finding the minimal cut of a signed graph, a problem known to be NP-hard. We offer an efficient approximation and achieve superior results by constructing the code bit after bit. We show that the proposed approximation is superior to the commonly used spectral methods with respect to both accuracy and complexity. Thus, it is useful for many other problems that can be translated into signed graph cut.

Shai Avidan

Papers

Nonparametric Acceleration, Super-Resolution,

Physics Based Image Deshadowing Using Local Linear Model

How Low Can We Go? Pixel Annotation for Semantic Segmentation

Stress-Testing LiDAR Registration

Proximity Preserving Binary Code using Signed Graph-Cut