Morphological Segmentation on Learned Boundaries

International audienceColour information is usually not enough to segment natural complex scenes. Texture contains relevant information that segmentation approaches should consider. Martin et al. [Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5) (2004) 530-549] proposed a particularly interesting colour-texture gradient. This gradient is not suitable for Watershed-based approaches because it contains gaps. In this paper, we propose a method based on the distance function to fill these gaps. Then, two hierarchical Watershed-based approaches, the Watershed using volume extinction values and the Waterfall, are used to segment natural complex scenes. Resulting segmentations are thoroughly evaluated and compared to segmentations produced by the Normalised Cuts algorithm using the Berkeley segmentation dataset and benchmark. Evaluations based on both the area overlap and boundary agreement with manual segmentations are performed

P algorithm, a dramatic enhancement of the waterfall transformation

This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

Configuração de sistemas de visão robótica

Mestrado em Engenharia Electrónica e TelecomunicaçõesEsta dissertação apresenta o trabalho desenvolvido na criação de uma aplicação para calibração da visão robótica de robôs jogadores de futebol do projecto CAMBADA da Universidade de Aveiro. Paralelamente à aplicação foi desenvolvida uma biblioteca de apoio à comunicação com periféricos de captura de vídeo, que também é discutida aqui. Com vista a resolver alguns problemas subjacentes ao reconhecimento de objectos através de informação de cor, é apresentado um estudo sobre a influência da utilização de diversas representações da cor neste mesmo reconhecimento. ABSTRACT: This thesis presents the work developed in the creation of an application for the callibration of robotic vision in soccer playing robots of the CAMBADA project at the University of Aveiro. Simultaneously to the application, a support library to communicate with video capture peripherals was also developed and is also discussed. Aiming to resolve some of the underlying problems with color information based object recognition, a study is presented about the influence of several color representations in object recognition

B.: Waterfall segmentation of complex scenes

Abstract. We present an image segmentation technique using the morphological Waterfall algorithm. Improvements in the segmentation are brought about by using improved gradients. These are based on the detection of object boundaries learnt from human segmentations introduced by Martin et al. (2004). We avoid the usual pitfall found when applying Watershed algorithms to these boundaries, namely that the boundary lines usually contain gaps, by making use of distance functions on the boundary image. Two types of distance function are used: the classic distance function and a distance function for numerical images recently introduced by Beucher (2005). Resulting segmentations are compared to human segmentations using the Berkeley segmentation benchmark. The benchmark results show that the proposed segmentation algorithm produces segmentations comparable to those produced by the Normalised Cuts algorithm.