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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 1 • Date Jan. 2000

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Displaying Results 1 - 7 of 7
  • The 20th anniversary of the IEEE Transactions on pattern analysis and machine intelligence

    Publication Year: 2000 , Page(s): 1 - 3
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    Freely Available from IEEE
  • Medical image analysis: progress over two decades and the challenges ahead

    Publication Year: 2000 , Page(s): 85 - 106
    Cited by:  Papers (167)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5340 KB)  

    The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come. View full abstract»

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  • Twenty years of document image analysis in PAMI

    Publication Year: 2000 , Page(s): 38 - 62
    Cited by:  Papers (127)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1344 KB)  

    The contributions to document image analysis of 99 papers published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) are clustered, summarized, interpolated, interpreted, and evaluated View full abstract»

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  • Statistical pattern recognition: a review

    Publication Year: 2000 , Page(s): 4 - 37
    Cited by:  Papers (1415)  |  Patents (80)
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    The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field View full abstract»

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  • Angular bisector network, a simplified generalized Voronoi diagram: application to processing complex intersections in biomedical images

    Publication Year: 2000 , Page(s): 120 - 128
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    One of the major goals of computer vision is the research and the development of flexible methods for shape description. A large group of shape description techniques is given by heuristic approaches, which yield acceptable results in the description of simple shapes and regions. In this case, objects are represented by a planar graph with nodes symbolizing subregions from region decomposition, and region shape is then described by the graph properties. In the paper, the angular bisector network (ABN), a descriptor of polygonal shape, is used to automatically detect intersections between neurites of cell structures. Some properties of the ABN, such as linear algebraic complexity, easy extraction of characteristic points, etc., are very useful and experimental results are promising View full abstract»

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  • Looking at people: sensing for ubiquitous and wearable computing

    Publication Year: 2000 , Page(s): 107 - 119
    Cited by:  Papers (112)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (812 KB)  

    The research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is “fourth generation” embedded computing: “smart” environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art. Four areas receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper discusses some of the research challenges and opportunities View full abstract»

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  • Online and off-line handwriting recognition: a comprehensive survey

    Publication Year: 2000 , Page(s): 63 - 84
    Cited by:  Papers (537)  |  Patents (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1216 KB)  

    Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered View full abstract»

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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
David A. Forsyth
University of Illinois