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Proceedings Paper

A multiphase active contour model based on the Hermite transform for texture segmentation
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Paper Abstract

Texture is one of the most important elements used by the human visual system (HVS) to distinguish different objects in a scene. Early bio-inspired methods for texture segmentation involve partitioning an image into distinct regions by setting a criterion based on their frequency response and local properties in order to further perform a grouping task. Nevertheless, the correct texture delimitation still remains as an important challenge in image segmentation. The aim of this study is to generate a novel approach to discriminate different textures by comparing internal and external image content in a set of evolving curves. We propose a multiphase formulation with an active contour model applied on the highest energy coefficients generated by the Hermite transform (HT). Local texture features such as scale and orientation are reflected in the HT coefficients which guide the evolution of each curve. This process leads to the enclosure of similar characteristics in a region associated with a level set function. The efficiency of our proposal is evaluated using a variety of synthetic images and real textured scenes.

Paper Details

Date Published: 24 May 2018
PDF: 9 pages
Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791H (24 May 2018); doi: 10.1117/12.2306541
Show Author Affiliations
Erik Carbajal-Degante, Univ. Nacional Autónoma de México (Mexico)
Jimena Olveres, Univ. Nacional Autónoma de México (Mexico)
Boris Escalante-Ramírez, Univ. Nacional Autónoma de México (Mexico)

Published in SPIE Proceedings Vol. 10679:
Optics, Photonics, and Digital Technologies for Imaging Applications V
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal, Editor(s)

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