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Journal of Optimization
Volume 2013 (2013), Article ID 345287, 15 pages
Research Article

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

School of Computing, National University of Singapore, Singapore 117417

Received 5 June 2013; Revised 21 July 2013; Accepted 2 August 2013

Academic Editor: Manuel Lozano

Copyright © 2013 Dilip K. Prasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel method that uses both the local and the global nature of fit for dominant point detection is proposed. Most other methods use local fit to detect dominant points. The proposed method uses simple metrics like precision (local nature of fit) and reliability (global nature of fit) as the optimization goals for detecting the dominant points. Depending on the desired level of fitting (very fine or crude), the threshold for precision and reliability can be chosen in a very simple manner. Extensive comparison of various line fitting algorithms based on metrics such as precision, reliability, figure of merit, integral square error, and dimensionality reduction is benchmarked on publicly available and widely used datasets (Caltech 101, Caltech 256, and Pascal (2007, 2008, 2009, 2010) datasets) comprising 102628 images. Such work is especially useful for segmentation, shape representation, activity recognition, and robust edge feature extraction in object detection and recognition problems.