Jean-Marc Boucher

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Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. We propose to filter this noise with a(More)
Shift invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, e.g., the double-tree complex WT (DTCWT), have these useful properties. In this paper, we propose the use of a recently introduced implementation of such a WT, namely,(More)
In this paper, we address invariant keypoint-based texture characterization and recognition. Viewing keypoint sets associated with visual textures as realizations of point processes, we investigate probabilistic texture models from multivariate log-Gaussian Cox processes. These models are parameterized by the covariance structure of the spatial patterns.(More)
We propose to use evidential reasoning in order to relax Bayesian decisions given by a Markovian classification algorithm (ICM). The Dempster–Shafer rule of combination enables us to fuse decisions in a local spatial neighborhood which we further extend to be multisource. This approach enables us to more directly fuse information. Application to the(More)
We propose a novel unsupervised region based criterion for multi-class texture segmentation. The proposed criterion relies on the maximization of a weighted sum of Kullback-Leibler measure between distributions of local texture features associated to the different image regions. Hence, the segmentation issue is stated as the maximization of the proposed(More)
In this paper, we propose a new descriptor of texture images based on the characterization of the spatial patterns of image keypoints. Regarding the set of visual keypoints of a given texture sample as the realization of marked point process, we define texture features from multivariate spatial statistics. Our approach initially relies on the construction(More)