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Moving shadow detection is a fundamental step in video-surveillance applications since it is generally confused with foreground. In this paper, we propose a novel statistical non-parametric method to detect moving shadow in a road traffic image sequences. We consider a diagonal model to describe the shadow distortion in the RGB color space. A Support Vector(More)
In this paper, we present an adaptive variational segmentation algorithm of spectral-texture regions in satellite images using level set. Satellite images contain both textured and non-textured regions, so for each region cues of spectral and texture are integrated according to their discrimination power. Motivated by Fisher-Rao's linear dis-criminant(More)
In this paper, we present a probabilistic framework for edge and region grouping using conditional random field. Our model is built on a hybrid adjacency graph of atomic region and contour primitives. Unary and pairwise potentials that capture similarity, proximity and curvilinear continuity are defined. Similarity, for both region and edge cues, is(More)
We propose a non-homogeneous Conditional Random Field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the labels.(More)
In this paper, we develop a novel Conditional Random Field (CRF) formulation to jointly extract road networks from a set of high resolution satellite images. Our fully unsupervised method relies on a pairwise CRF model defined over a set of test images, which encodes prior assumptions about the roads such as thinness, elongation. Four competitive energy(More)
Motion-based segmentation is an important task in object-oriented video applications. Different approaches to motion segmentation with level sets. have been proposed. The key features of level sets representation are its ability to handle variations in the topology of the segmentation and its numerical stability. These approaches rely on certain prior(More)
In this paper, we address the issue of designing a smoke detector robust to illumination variations. Our contribution consists in resorting to color invariants as salient smoke features. More precisely, the proposed detector employs consecutively of an illumination invariant color representation, a photometric gain based background subtraction, a(More)