A Topology-Based Approach to Computing Neighborhood-of-Interest Points Using the Morse Complex

Abstract A central problem in image processing and computer vision is the computation of corresponding interest points in a given set of images. Usually, interest points are considered as independent elements described by some local information. Due to the limitations of such an approach, many incorrect correspondences can be obtained. A specific contribution of this paper is the proposition of a topological operator, called Local Morse Context (LMC), computed over Morse complexes, introduced as a way of efficiently computing neighborhoods of interest points to explore the structural information in images. The LMC is used in the development of a matching algorithm, that helps reducing the number of incorrect matches, and obtaining a confidence measure of whether a correspondence is correct or incorrect. The approach is designed and tested for the correspondence of narrow-baseline synthetic and specially challenging underwater stereo pairs of images, for which traditional methods present difficulties for finding correct correspondences

A Topology-based Approach To Computing Neighborhood-of-interest Points Using The Morse Complex

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)A central problem in image processing and computer vision is the computation of corresponding interest points in a given set of images. Usually, interest points are considered as independent elements described by some local information. Due to the limitations of such an approach, many incorrect correspondences can be obtained. A specific contribution of this paper is the proposition of a topological operator, called Local Morse Context (LMC), computed over Morse complexes, introduced as a way of efficiently computing neighborhoods of interest points to explore the structural information in images. The LMC is used in the development of a matching algorithm, that helps reducing the number of incorrect matches, and obtaining a confidence measure of whether a correspondence is correct or incorrect. The approach is designed and tested for the correspondence of narrow-baseline synthetic and specially challenging underwater stereo pairs of images, for which traditional methods present difficulties for finding correct correspondences. (C) 2015 Elsevier Inc. All rights reserved.30299311Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES