Journal ArticleDOI
Feature extraction methods for character recognition--a survey
TLDR
This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters in terms of invariance properties, reconstructability and expected distortions and variability of the characters.About:
This article is published in Pattern Recognition.The article was published on 1996-04-01. It has received 1376 citations till now. The article focuses on the topics: Feature extraction & Feature (computer vision).read more
Citations
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Journal ArticleDOI
Clustering by compression
TL;DR: Evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors is reported.
Book
Feature Extraction and Image Processing
Mark S. Nixon,Alberto S. Aguado +1 more
TL;DR: The new edition of Feature Extraction and Image Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner, and features a companion website that includes worksheets, links to free software, Matlab files, solutions and new demonstrations.
Posted Content
Clustering by compression
TL;DR: The normalized compression distance (NCD) as discussed by the authors is a similarity metric that approximates universality based on the normalized information distance (NIC) metric, which was proposed by the authors of this paper.
Journal ArticleDOI
Goal-directed evaluation of binarization methods
O.D. Trier,Anil K. Jain +1 more
TL;DR: This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example, and defines the performance of the character recognition module as the objective measure.
Journal ArticleDOI
A linear-time component-labeling algorithm using contour tracing technique
TL;DR: A new linear-time algorithm is presented in this paper that simultaneously labels connected components and their contours in binary images and extracts component contours and sequential orders of contour points, which can be useful for many applications.
References
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Journal ArticleDOI
Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.