NDLI: A new model-based estimation of ellipses for object representation

Content Provider	IEEE Xplore Digital Library
Author	Kong, J. Boyer, K.L. Saltz, J.H. Kun Huang
Copyright Year	2009
Description	Author affiliation: Ohio State University, Dept. of Biomedical Informatics, Columbus, OH 43210 (Kun Huang) \|\| Rensselaer Polytechnic Institute, Dept. of Electrical, Computer, and Systems Engineering, Troy, NY 12180 (Boyer, K.L.) \|\| Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322 (Kong, J.; Saltz, J.H.)
Abstract	Fitting geometric models to objects of interest in images is one of the most classical problems studied in computer vision field. As a result of its strong representation power and flexibility, conic is one of the geometric primitives widely used in a large number of image analysis applications, in practice. As opposed to most existing conic fitting methods minimizing the fitting error with the use of the second order polynomial representation, in this paper, we propose a new method that formulates the geometric fitting problem as a process of seeking for the optimal mapping to a bivariate normal distribution model. As a result, some critical disadvantages tightly coupled with those methods following the routine polynomial representation can be well overcome. To demonstrate this, a set of carefully designed comparison experiments is conducted to show the superiority of the newly proposed method to a representative method using the polynomial representation. Additionally, the practical effectiveness of the proposed method is further manifested using a set of real image data with a promising accuracy.
Starting Page	3637
Ending Page	3640
File Size	1190689
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Physiology Electromyography Microscopy Neuromuscular Low pass filters Musculoskeletal system Predictive models Filtering Joints
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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