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.

/pdf/imagenet-large-scale-visual-recognition-challenge-caky5nmmix.pdf

ImageNet Large Scale Visual Recognition Challenge

Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

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

The model of the present invention incorporates incompressibility constraints to infer 3-D motion. Discrete, projected 2-D motion information from two different views of an object are input into the system. A model is realized via the tessellation component, and the material property of incompressibility is imposed on model movement. The output of the system is a continuous 3-D motion model which has interpolated the projected 2-D data.

Incompressibility constraints for inferring 3-D motion from 2-D projected displacements in a thick-walled ellipsoid with a geodesic-based tessellation

https://academiccommons.columbia.edu/doi/10.7916/D89K4KBQ/download

Can we approximate zeros of functions with nonzero topological degree

Describable visual attributes are a powerful way to label aspects of an image, and taken together, build a detailed representation of a scene’s appearance. Attributes enable highly accurate approaches to a variety of tasks, including object recognition, face recognition and image retrieval. An important consideration not previously addressed in the literature is the reliability of attribute classiers as the quality of an image degrades. In this paper, we introduce a general framework for conducting reliability studies that assesses attribute classier accuracy as a function of image degradation. This framework allows us to bound, in a probabilistic manner, the input imagery that is deemed acceptable for consideration by the attribute system { without requiring ground truth attribute labels. We introduce a novel dierential probabilistic model for accuracy assessment that leverages a strong normalization procedure based on the statistical extreme value theory. To demonstrate the utility of our framework, we present an extensive case study using 64 unique facial attributes, computed on data derived from the Labeled Faces in the Wild (LFW) data set. We also show that such reliability studies can result in signicant compression benets for mobile applications.

/pdf/how-reliable-are-your-visual-attributes-16qhehz1qa.pdf

How Reliable are Your Visual Attributes

The problem considered in this paper is motivated by the independence between log ical and physical topology in Wavelength Division Multiplexing WDM based local and metropolitan lightwave networks This paper suggests logical embeddings of digraphs into multihop lightwave networks to maximize the throughput under nonuniform tra c conditions De ning congestion as the maximum ow carried on any link two perturbation heuristics are presented to nd a good logical embedding on which the routing problem is solved with minimum congestion A constructive proof for a lower bound of the problem is given and obtaining an optimal solution for integral routing is shown to be NP Complete The performance of the heuristics is empirically analyzed on various tra c models Sim ulation results show that our heuristics perform on the average from a computed lower bound Since this lower bound is not quite tight we suspect that the actual performance is better In addition we show that performance improvements can be obtained over the previous work Introduction The vast optical bandwidth of a ber confronted with the electro optic bottleneck moti vates the e ort on employing concurrency and parallelism in optical networks as well as new architectures The Wavelength Division Multiplexing technique realized by tunable opti cal transceivers has been proposed as a way of constructing multichannel multihop lightwave networks A station in such a network has electronic and optical components The optical com ponents are transmitters and receivers used to tap into the optical medium by tuning to a special wavelength whereas the electronic components constitute the rest to control the operation of the switch We consider networks whose underlying physical topology is based on sharing a single ber e g a linear bus a tree or a star topology In this case potentially all the stations can reach the medium by tapping into it Pairing a transmitter on one node with a receiver on another one by tuning them to the same wavelength establishes a logical connection between these two stations Therefore allocation of the wavelengths to the stations constructs a logical embedding into the network independent from the physical topology Although sharing is possible we consider unique end to end assign ments of the wavelengths no channel sharing The logical embeddings are constrained by some design parameters such as the number of available transceivers or wavelengths We refer to for a recent survey on WDM based architectures In this paper we propose logical embeddings of directed graphs called con gurations for achieving high throughput under non uniform tra c conditions Candidate con gurations are generated by perturbation heuristics and the routing problem is solved on these con gurations by Linear Programming The routing problem is formulated as a Multi Commodity Flow MCF problem with the objective function to minimize the maximum ow congestion on an edge This paper organized as follows In the following section we de ne the problem and state our approach more formally In section the algorithmic issues involved with the perturbation process are addressed In section and we present our algorithms and their computational performance analysis respectively A constructive proof for a lower bound of the problem by using special spanning trees and some complexity results on the problem are presented in sections respectively Problem De nition and Approach Given a tra c matrix T ts t for N stations such that an entry ts t is the amount of average tra c to be sent from a source station s to a destination t let integer i denote the number of incoming and outgoing links for each station i De ne a con guration CONF as a strongly connected digraph without self loops such that each node has in out degree exactly d A con guration is a logical representation of a net work topology in which each node corresponds to a station and each edge to a logical physical connection Given a tra c matrix T and a con guration CONF de ne routing R as an assignment of the tra c ts t to directed path s on CONF from source s to sink t for all s t pair De ne congestion zi j on a directed edge i j CONF as the total amount of tra c carried on this edge for routing the tra c in T Let Z max i j fzi jg For a given T and d we are interested in nding a con guration CONF and a routing R to minimize Z while satisfying the tra c T Note that this problem has two parts how to nd a good embedding how to nd a good routing algorithm A uni ed solution can be obtained by solving the following mixed integer programming prob lem by de ning a unique commodity for a station k such that P j tk j Denote by f k u v the ow on edge u v of commodity k Then Min Z such that Z P k f k u v k pair u v P i f k i j P i f k j i tk j k j where k j f u v P j tk j xu v P j xi j P j xj i i i j xi j f i u v There are two variables of interest the ow variable f i j the integer variable xi j which is if there is an edge from i to j otherwise We show in section that for an integral tra c matrix T nding a con guration CONF and an integral routing of the tra c with minimum congestion on this CONF is NP complete The conjecture is that the problem is still NP hard with the integrality relaxation on the routing problem due to remaining integrality constraint on the edge variables Therefore we introduce two perturbation heuristics for approximate solutions based on Variable Depth Local Search and Simulated Annealing techniques respectively Each heuristic performs a greedy search in an exponentially large con guration space the set of all CONFs The direction of the search is guided by the value of a cost function which is the amount of maximum congestion Value of congestion is computed by solving the routing problem formulated as a multicommodity ow problem with Linear Programming LP on the current con guration The Subroutine LP Given a con guration CONF the tra c matrix T and the degree bound d we formu late the problem of nding minimum congestion routing as an instance of Multicommodity ow problem MCF In this formulation each commodity corresponds to a source node leading to polynominal number of rows and columns in the Simplex Tableau Precisely de ne as a commodity a station k such that P j tk j Denote by f u v the ow on edge u v of commodity k Then Min Z such that Z P k f k u v commodity k and edge u v P u f k u v P u f k v u tk v k v where k v f u v The output of the LP solution is the minimized maximum congestion Z and the ow assignment f u v for all the edges u v and commodities k Although we relaxed the integrality constraint for the routing problem note here that if the entries of the tra c matrix are large real numbers a good rounding of the ow to obtain an integer solution to routing problem does not substantially change the value of the congestion Therefore LP solution approximates to the optimal integral routing as the tra c matrix consists of larger numbers Starting with an initial con guration the heuristics perform the following three basic steps Ask Subroutine LP for the value of maximum congestion for the current con guration Determine the direction of the search Modify the current con guration to obtain a new one Termination of the heuristics can be realized either externally by an input parameter or internally when no more decrease is achieved on the value of the cost function Choice of a starting con guration is important for nding a local optimum solution Starting points can be generated randomly as well as with greedy algorithms We give an empirical comparison of various starting points in section A candidate con guration for which the routing problem can be solved with less congestion is accepted and the search of the con guration space continues from that point An accepted CONF is modi ed to obtain a new candidate which is called valid if it maintains the CONF property Maintenance of CONF property is realized with a validity test prior to the perturbation operation which determines whether or not the CONF property would be destroyed after the operation The modi cation of the current con guration is achieved by perturbation operation which is based on a n change operation A set of edges with cardinality n is randomly chosen to be replaced by a new set of edges with the same cardinality to obtain a new con guration In other words one of the con gurations that can be obtained from the current one by n changes is chosen randomly Choice of perturbation determines the direction and the step size of the search For instance big changes cause big jumps in the search space therefore if the space has narrow valleys a nearby local optimum could be missed On the other hand small step size may require longer search process to nd a local optimum In the following section we introduce two di erent perturbation operations and then compare their performance in section Perturbation and Maintenance of CONF property In this section we present two perturbation operations node and edge perturbation and show how to test the validity in advance Node perturbation case change Two distinct nodes are randomly chosen among N nodes and their out going edges are exchanged as follows Choose randomly with probability N N a distinct pair of nodes u v from the con gu ration see gure

/pdf/logical-embeddings-for-minimum-congestion-routing-in-41wlg6sd7y.pdf

Terrance E. Boult

Papers

Incompressibility constraints for inferring 3-D motion from 2-D projected displacements in a thick-walled ellipsoid with a geodesic-based tessellation

Can we approximate zeros of functions with nonzero topological degree

How Reliable are Your Visual Attributes

Logical Embeddings for Minimum Congestion Routing in Lightwave Networks

Applications of g-neighbors to image processing and morphology