The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

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ImageNet Large Scale Visual Recognition Challenge

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Multiple View Geometry in Computer Vision.

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

Accuracy of geometric measurement of object environments for robotic vision tasks is of increasing importance as these tasks become more sophisticated. This paper is concerned with the accurate measurement of orientations of lines and planar surfaces from two camera stereo. Most existing stereo algorithms build up geometric descriptions from absolute position information about points. We emphasize in this paper the accuracy advantages of determining orientation of lines and planar surfaces using the correspondence of linear features instead of point features. Intuitively, the determination of only orientation from absolute position measurement of points uses too much information which requires the unnecessary accurate calibration of certain camera parameters. Orientation information is invariant to knowledge about absolute positional information. If the orientation of lines are computed from the intersection of planar sheets of projection from a stereo pair of cameras, in the absence of all camera errors, the result is independent of baseline. Orientation of planar surfaces can be determined from the cross product of the orientations of at least two coplanar linear features. It is shown that even in the presence of typical camera errors that measurement of the orientations of lines and surfaces using line correspondence stereo is relatively insensitive to base-line errors. Even more, the measurement of orientation from line correspondence stereo is relatively insensitive to typical localization error on the image plane. As a result orientations of lines and planar surfaces far from the baseline can be measured much better using line correspondence stereo rather than point correspondence stereo.

/pdf/using-line-correspondence-stereo-to-measure-surface-13a7pltnp7.pdf

Using line correspondence stereo to measure surface orientation

Optimal algorithms: Tools for mathematical modeling

Replicate Statistics for Efficient Vision System Evaluation

Significant research progress has been made in electronic surveillance, signals intelligence, and biometric identification that has increased the deployment and effectiveness of these technologies. For many, video domestic surveillance cameras, wiretapping and biometrics epitomize the (misperceived) “inherent” tradeoff between security and privacy, with staunch defenders of these technologies promoting them as indispensable tools for security and equally vocal groups that berate them as an ineffective siege. Congress, over the past thirty years, has enacted a slew of legislation to protect constitutional rights to privacy and speech, and other laws to provide for enhanced national security, yet little has been done to address, technologically, the balance between liberty and security. While the balance of liberty and security is important, it is equally important to note that this is not a zero sum game or an inherent tradeoff. This paper presents a method that demonstrates the adaptation and application of cryptographic ideas to sculpt technological approaches that can move the “balance point” and provide simultaneous improvements to both security and privacy. Privacy Enhancement via Adaptive Cryptographic Embedding (PEACE) is a method utilizing encryption techniques to improve privacy while allowing security applications to continue to use much of the data in context, and allowing full access (i.e. violation of privacy) only with possession of a decryption key. We introduce the application of PEACE in three areas: video surveillance, wiretapping, and biometrics. We present an in-depth case study for “warrantless” wiretapping, showing the details necessary for a new tool that protects both our country and the privacy of our citizens.

Privacy Enhancement via Adaptive Cryptographic Embedding Balancing Liberty and Security in an Inform

Deformable models in the “physically-based” paradigm are almost always formulated in an adhoc fashion and are not related to physical reality. We reinterpret these techniques by putting them into a framework of robust statistics and using this framework to analyze the problems and ad-hoc solutions found in common physically-based formulations. These include incorrect prior shape models; bad relative weights of various energies; and the two-stage approach to minimization (adjusting global, then local shape parameters). We examine the statistical implications of common deformable object formulations. In our reformulation, the units are meaningful, training data plays a fundamental role, different kinds of information may be fused, and certainties can be reported for the segmentation results. We suggest robust statistics to combat interference from the necessarily large amount of unmodeled image information.

Terrance E. Boult

Papers

Using line correspondence stereo to measure surface orientation

Optimal algorithms: Tools for mathematical modeling

Replicate Statistics for Efficient Vision System Evaluation

Privacy Enhancement via Adaptive Cryptographic Embedding Balancing Liberty and Security in an Inform

Reinterpreting Physically-Motivated Modeling