In this paper a new algorithm to compute the component tree is presented. As compared to the state of the art, this algorithm does not use excessive memory and is able to work efficiently on images whose values are highly quantized or even with images having floating values. We also describe how it can be applied to astronomical data to identify relevant objects.

In this paper, we propose an algorithm to detect smooth local symmetries and contours of ribbon-like objects in natural images. The detection is formulated as a spatial tracking task using a particle filtering approach, extracting one part of a structure at a time. Using an adaptive local geometric model, the method can detect straight reflection symmetries in perfectly symmetrical objects as well as smooth local symmetries in curved elongated objects. In addition, the proposed approach jointly estimates spine and contours, making it possible to generate back ribbon objects. Experiments for local symmetry detection have been conducted on a recent extension of the Berkeley segmentation data sets. We also show that it is possible to retrieve specific geometrical objects using intuitive prior structural information.

We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscale edgelet structure naturally embeds semi-local information and is the basic element of the proposed recursive Bayesian modeling. Prior and transition distributions are learned offline using a shape database. Likelihood functions are learned online, thus are adaptive to an image, and integrate color and gradient information via local, textural, oriented, and profile gradient-based features. The underlying model is estimated using a sequential Monte Carlo approach, and the final soft contour detection map is retrieved from the approximated trajectory distribution. We also propose to extend the model to the interactive cut-out task. Experiments conducted on the Berkeley Segmentation data sets show that the proposed MultiScale Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.

We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which locally tracks small pieces of edges called edgelets. The combination of the Bayesian modeling and the edgelets enables the use of semi-local prior information and image-dependent likelihoods. We use a mixed offline and online learning strategy to detect the most relevant edgelets. The detection problem is then modeled as a sequential Bayesian tracking task, estimated using a particle filtering technique. Experiments on the Berkeley Segmentation Datasets show that the proposed Particle Filter Contour Detector method performs well compared to competing state-of-the-art methods.

In this paper, we propose a novel method to introduce spatial information in particle filters. This information may be expressed as spatial relations (orientation, distance, etc.), velocity, scaling, or shape information. Spatial information is modeled in a generic fuzzy-set framework. The fuzzy models are then introduced in the particle filter and automatically define transition and prior spatial distributions. We also propose an efficient importance distribution to produce relevant particles, which is dedicated to the proposed fuzzy framework. The fuzzy modeling provides flexibility both in the semantics of information and in the transitions from one instant to another one. This allows one to take into account situations where a tracked object changes its direction in a quite abrupt way and where poor prior information on dynamics is available, as demonstrated on synthetic data. As an illustration, two tests on real video sequences are performed in this paper. The first one concerns a classical tracking problem and shows that our approach efficiently tracks objects with complex and unknown dynamics, outperforming classical filtering techniques while using only a small number of particles. In the second experiment, we show the flexibility of our approach for modeling: Fuzzy shapes are modeled in a generic way and allow the tracking of objects with changing shape.