Presents corrections to the article, "Automatic sea-surface obstacle detection and tracking in forward-looking sonar image sequences,” (Karoui, I., et al.) EEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4661–4669, Aug. 2015.

This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related to classical texture based segmentation approaches. Existing methods, even if they use different and various texture features, are mainly stated as the optimization of a criterion evaluating punctual pixel likelihoods or similarity measure computed within a local neighborhood. These approaches require sufficient dissimilarity between the considered texture features. An additional limitation is the choice of the neighborhood size and shape. These two parameters and especially the neighborhood size significantly influence the classification performances: the neighborhood must be large enough to capture texture structures and small enough to guarantee segmentation accuracy. These parameters are often set experimentally. These limitations are mitigated with the proposed variational methods stated at the region-level. It resorts to an energy criterion defined on image where regions are characterized by nonparametric distributions of their responses to a set of filters. In the supervised case, the segmentation algorithm consists in the minimization of a similarity measure between region-level statistics and texture prototypes and a boundary based functional that imposes smoothness and regularity on region boundaries. In the unsupervised case, the data-driven term involves the maximization of the dissimilarity between regions. The proposed similarity measure is generic and permits optimally fusing various types of texture features. It is defined as a weighted sum of Kullback-Leibler divergences between feature distributions. The optimization of the proposed variational criteria is carried out using a level-set formulation. The effectiveness and the robustness of this formulation at region-level, compared to classical active contour methods, are evaluated for various Brodatz and natural images.

This paper addresses weakly supervised object recognition. We show how the combination of an image-level inference, in terms of image-level object class priors, can lead to better training of object recognition models. Stated within a probabilistic setting, the proposed approach is applied to fisheries acoustics and fish school recognition.

In this paper, we propose and compare two supervised algorithms for the segmentation of textured sonar images, with respect to seafloor types. We characterize seafloors by a set of empirical distributions estimated on texture responses to a set of different filters. Moreover, we introduce a novel similarity measure between sonar textures in this feature space. Our similarity measure is defined as a weighted sum of Kullback-Leibler divergences between texture features. The weight setting is twofold. First, each filter is weighted according to its discrimination power: The computation of these weights are issued from a margin maximization criterion. Second, an additional weight, evaluated as an angular distance between the incidence angles of the compared texture samples, is considered to take into account sonar-image acquisition process that leads to a variability of the backscattered value and of the texture aspect with the incidence-angle range. A Bayesian framework is used in the first algorithm where the conditional likelihoods are expressed using the proposed similarity measure between local pixel statistics and the seafloor prototype statistics. The second method is based on a variational framework as the minimization of a region-based functional that involves the similarity between global-region texture-based statistics and the predefined prototypes.

We propose a novel multi-class method for texture segmentation. The segmentation issue is stated as the minimization of a region-based functional that involves a weighted Kullback-Leibler measure between distributions of local texture features and a regularization term that imposes smoothness and regularity of region boundaries. The proposed approach is implemented using level-set methods, and partial differential equations (PDE) are expressed using shape derivative tools introduced in S. Jehan-Besson et al. (2003). As an application, we have tested the method using cooccurrence distributions to segment synthetic mosaics of textures from the Brodatz album, as well as real textured sonar images. These results prove the relevance of the proposed approach for supervised and unsupervised texture segmentation