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.

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Object tracking: A survey

A technique for identifying the material properties of objects in an image using multiple images taken through a polarizing lens at various rotations in front of a stationary camera (only the filter moves). Using these images, it is possible to obtain the classification of material surfaces at all points on a spectacular highlight. The algorithm is demonstrated on laboratory images. The authors assume a point source, the theory can only be applied at points where specular reflection dominates. Extensions of the theory to deal with extended light sources, which greatly increase the portion of the image giving rise to specular reflection, are also considered. >

Polarization/radiometric based material classification

This paper explores the mutual benefits of biometrics technology and cloud computing. Cloud computing can take advantage of the strong authentication property of biometrics in order to enhance the security of the cloud and to introduce new service models (i.e. Biometric authentication as a Service (BioAaaS)). On the other hand, biometrics technology can leverage the cloud's unbounded computational resources and attractive properties of flexibility, scalability, and cost reduction in order to reduce the cost of the biometrics system requirements of different computational resources (i.e. processing power or data storage) and to enhance the performance of biometrics systems' processes (i.e. biometric matching). In this paper, we explore five potential areas where there are mutual advantages between the cloud and biometrics. For each of these five areas, the current status and how biometrics/cloud can enhance or solve problems of the current state-of-the-art is investigated. Next, the challenges facing biometric/cloud applications in each area are discussed. Finally, the literature of applying biometrics/cloud in each area is surveyed.

Problems and Promises of Using the Cloud and Biometrics

Superellipsoids are parameterized solids which can appear like cubes or spheres or octahedrons or 8-pointed stars or anything in between. They can also be stretched bent tapered and combined with boolean to model a wide range of objects. Columbia's vision group is interested in using superquadrics as model primitives for computer vision applications because they are flexible enough to allow modeling of many objects, yet they can be described by a few (5-14) numbers. This paper discusses research into the recovery of superellipsoids from 3-D information, in particular range data. This research can be divided into two parts, a study of potential error-of-fit measures for recovering superquadrics, and implementation and experimentation with a system which attempts to recover superellipsoids by minimizing an error-of-fit measure. This paper presents an overview of work in both areas. Included are data from an initial comparison of 4 error-of-fit measures in terms of the inter-relationship between each measure and the parameters defining the superellipsoid. Also discussed is an experimental system which employs a nonlinear least square minimization technique to recover the parameters. This paper discusses both the advantages of this technique, and some of its major drawbacks. Examples are presented, using both synthetic and range-data, where the system successfully recovers superlliposids. Including "negative" volumes as would occur if superellipsoids were used in a constructive solid modeling system.

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On the Recovery of Superellipsoids

This paper addresses power reduction in wireless sensor networks (WSNs), proposing an application-level solution for the IEEE 802.15.4 compliant ZigBee protocol. ZigBee protocol supports the least power-consuming 'sleep' mode of operation only for end-nodes that do not route packets. This significantly limits ZigBee's WSN applications. This paper presents a ZigBee-based power-conserving network design in which a multi-mode scheduler can be used at the application-level for all network nodes that brings the whole net up and down. This allows all nodes to 'deep sleep', hence increasing the per-node lifetime, leading to an increased overall network lifetime. Results of network simulations with a rate monotonic-based scheduler, schedules operating times for various nodes in the entire network, setting the devices to the lowest power operating mode - sleep mode - for all other times when no operation needs to be performed. When the network wakes up, all nodes necessary for a particular scheduled transmission awake and rebuild the ZigBee network. The paper presents power savings estimates from simulations showing significant advantages over standard ZigBee

https://www.vast.uccs.edu/~tboult/PAPERS/ieee-iswpc07-viswanathan-boult-power-conservation-in-zibeee.pdf

Power Conservation in ZigBee Networks using Temporal Control

Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwise correctly recognized inputs. Various approaches have been developed for efficiently generating these so-called adversarial examples, but those mostly rely on ascending the gradient of loss. In this paper, we introduce the novel logits optimized targeting system (LOTS) to directly manipulate deep features captured at the penultimate layer. Using LOTS, we analyze and compare the adversarial robustness of DNNs using the traditional Softmax layer with Openmax, which was designed to provide open set recognition by defining classes derived from deep representations, and is claimed to be more robust to adversarial perturbations. We demonstrate that Openmax provides less vulnerable systems than Softmax to traditional attacks, however, we show that it can be equally susceptible to more sophisticated adversarial generation techniques that directly work on deep representations.

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Terrance E. Boult

Papers

Polarization/radiometric based material classification

Problems and Promises of Using the Cloud and Biometrics

On the Recovery of Superellipsoids

Power Conservation in ZigBee Networks using Temporal Control

Adversarial Robustness: Softmax versus Openmax.