NDLI: Contour models for curvature estimation and shape decomposition

Content Provider	IEEE Xplore Digital Library
Author	Eom, K.B. Park, J.
Copyright Year	1992
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., DC, USA (Eom, K.B.; Park, J.)
Abstract	A statistical contour model is developed. A digital contour is modeled by a noisy observation which is represented by polynomial functions of coordinate variables. To estimate curvature functions of digital contours, the authors develop maximum likelihood estimators by fitting the model over a small neighborhood. The neighborhood size is determined by a maximum likelihood decision rule. Statistical properties of the estimators are also investigated. The contour is decomposed at curvature extrema points by finding zero-crossings of the first derivative of estimated curvature function. Experimental results show that the model based approach performs better in estimating curvature functions and detecting extrema points than other conventional approaches based on low-pass filtered curvature functions.
Starting Page	393
Ending Page	396
File Size	327128
Page Count	4
File Format	PDF
ISBN	0818629150
DOI	10.1109/ICPR.1992.201800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-08-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Maximum likelihood detection Maximum likelihood estimation Humans Digital images Machine vision Filters Noise shaping Polynomials Curve fitting
Content Type	Text
Resource Type	Article

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