Topological Coding and Its Application in the Refinement of SIFT

Abstract

Point pattern matching plays a prominent role in the fields of computer vision and pattern recognition. A technique combining the circular onion peeling and the radial decomposition is proposed to analyze the topology structure of a point pattern. The analysis derives a feature which records the topological structure of a point pattern. This novel feature is free from isometric assumption. It can resist various deformations such as adding points, suppressing points, affine transformations, projective transformations and elastic transformations to some degree. A refinement solution of the well known scale invariant feature transform (SIFT) algorithm is also proposed based on the probabilistic analysis of this feature. Experimental results show that the proposed refinement solution for SIFT using this feature is effective and robust.

Graph refining via iterative regularization framework

“…Graphical representations, which characterize the affinities among data points, have played an important role in machine learning, image processing [1][2][3][4], writer identification [5][6][7], visual tracking [8][9][10][11][12], and especially for clustering problems [13][14][15][16]. For graph-based clustering methods, the graph construction is guided under certain learned or pre-defined pairwise similarities [17,18].…”

Inferring topologies via driving-based generalized synchronization of two-layer networks

“…Since the groundbreaking works of Watts and Strogatz [1] on small-world networks in 1998 and of Barabási and Albert [2] on scale-free networks in 1999, complex dynamical networks as a new scientific branch have experienced rapid development and have permeated various fields, such as mathematics, computer sciences, engineering sciences and so on [3,4]. Initial research attention is mainly focused on complex dynamical networks' statistical properties and dynamical behaviors with previously known topologies.…”