Autor según el artículo: Sanroma, Gerard; Penate-Sanchez, Adrian; Alquezar, Rene; Serratosa, Francesc; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; Gonzalez Ballester, Miguel Angel
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Serratosa Casanelles, Francesc d'Assís
Palabras clave: Writing Vision Theoretical model Priors Models, theoretical Graph Extract Distance Algorithms Algorithm
Resumen: We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods. © 2016 Sanroma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Áreas temáticas: Zootecnia / recursos pesqueiros Sociology Sociología Serviço social Saúde coletiva Química Psychology Psicología Planejamento urbano e regional / demografia Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinary research in the social sciences Interdisciplinar Human geography and urban studies History & philosophy of science Historia Geografía Geociências General medicine General biochemistry,genetics and molecular biology General agricultural and biological sciences Farmacia Environmental studies Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação física Educação Economia Direito Demography Comunicação e informação Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência política e relações internacionais Ciência de alimentos Ciência da computação Biotecnología Biology Biodiversidade Biochemistry, genetics and molecular biology (miscellaneous) Astronomia / física Arquitetura, urbanismo e design Archaeology Antropologia / arqueologia Anthropology Agricultural and biological sciences (miscellaneous) Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: francesc.serratosa@urv.cat
Identificador del autor: 0000-0001-6112-5913
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Plos One. 11 (1): e0145846-
Referencia de l'ítem segons les normes APA: Sanroma, Gerard; Penate-Sanchez, Adrian; Alquezar, Rene; Serratosa, Francesc; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; Gonzalez Ballester, Miguel (2016). MSClique: Multiple structure discovery through the maximum weighted clique problem. Plos One, 11(1), e0145846-. DOI: 10.1371/journal.pone.0145846
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2016
Tipo de publicación: Journal Publications
Repositori URV