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. 1. Introduction In many applications, boundaries are represented using polygonal approximation [1–8]. The problem of dominant points detection is to determine the points only from a digital curve for such representation. This representation reduces the memory and computational complexity in storing and processing the digital curves and helps in the determination of geometrical properties like inflexion points, perimeter, and tangent estimation. It is useful for topological representation, character recognition, segmentation, and contour feature extraction in the applications of computer vision. Further, it reduces the problems of digitization and related noise issues. The problem of fitting lines on curves (including dominant point detection) is quite old. The method of Teh and Chin [9] relies primarily on the accurate determination of the support region based on chord length and the perpendicular distance of the pixels from the chords to determine the dominant points. Ansari and Huang [10] proposed a method in which a support region is assigned to each boundary point based on its local properties. A combination of Gaussian filtering and a significance measure is used on each pixel for identifying the dominant points. Cronin’s [11] method finds the support region for every pixel based on a non-uniform significance measure criterion calculated by locally determining the support region for each point. B. K. Ray and K. S. Ray [12] proposed a k-cosine-transform based method to determine the support region. Sarkar [13] proposed purely a chain code manipulation based method
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