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Proceedings ArticleDOI

Planar shape classification using hidden Markov model

TLDR
A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters and explores the characteristic relations between consecutive segments to make classification at a finer level.
Abstract
A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate much shape contour perturbation, and a moderate amount of occlusion. The overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added. >

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Citations
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Proceedings ArticleDOI

Recognizing human action in time-sequential images using hidden Markov model

TL;DR: The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.
Dissertation

Visual Recognition of American Sign Language Using Hidden Markov Models.

Thad Starner
TL;DR: Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL), achieving high recognition rates for full sentence ASL using only visual cues.
Journal ArticleDOI

Controlling eye movements with hidden Markov models

TL;DR: This work proposes these augmented HMMs as a theory of adaptive skill acquisition and generation, and gives an example, the what-where-AHMM, which creates a hybrid skill from separate skills based on object location and object identity.
Journal Article

Sensei, a real-time recognition, feedback and training system for T'ai chi gestures

TL;DR: Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.
Journal ArticleDOI

Image analysis and computer vision: 1991

TL;DR: A bibliography of nearly 1200 references related to computer vision and image analysis, arranged by subject matter is presented, covering topics including architectures; computational techniques; feature detection and segmentation; image analysis; and motion.
References
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Journal ArticleDOI

An introduction to hidden Markov models

TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Book

Robot Vision

TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
Journal ArticleDOI

Fourier Descriptors for Plane Closed Curves

TL;DR: It is established that the Fourier series expansion is optimal and unique with respect to obtaining coefficients insensitive to starting point and the amplitudes are pure form invariants as well as are certain simple functions of phase angles.
Journal ArticleDOI

Computer Processing of Line-Drawing Images

TL;DR: Various forms of line drawing representation are described, different schemes of quantization are compared, and the manner in which a line drawing can be extracted from a tracing or a photographic image is reviewed.
Journal ArticleDOI

Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes

TL;DR: The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved and the result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve.
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