文章基本信息

标题：PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection
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作者：Dilip K. Prasad
期刊名称：Journal of Optimization
电子版ISSN：2314-6486
出版年度：2013
卷号：2013
DOI：10.1155/2013/345287
出版社：Hindawi Publishing Corporation
摘要：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.