Iris Vanhamel

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We propose a new classification method for vector-valued images, based on: 1) a causal Markovian model, defined on the hierarchy of a multiscale region adjacency tree (MRAT), and 2) a set of nonparametric dissimilarity measures that express the data likelihoods. The image classification is treated as a hierarchical labeling of the MRAT, using a finite set(More)
We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges,(More)
A multi-resolution segmentation approach for color images is proposed. The scale space is generated using the Perona-Malik diffusion approach and the watershed algorithm is employed to produce the regions in each scale. The dynamics of contours and the relative entropy of color regions distribution are estimated as region dissimilarity features across the(More)
This paper investigates the scale selection problem for nonlinear diffusion scale-spaces. This topic comprises the notions of localization scale selection and scale space discretization. For the former, we present a new approach. It aims at maximizing the image content’s presence by finding the scale that has a maximum correlation with the noise-free image.(More)
A novel unsupervised strategy for content-based image retrieval is presented. It is based on a meaningful segmentation procedure that can provide proper distributions for matching via the earth mover’s distance as a similarity metric. The segmentation procedure is based on a hierarchical watershed-driven algorithm that extracts meaningful regions(More)
In this paper the issue of image segmentation within the framework of nonlinear multiscale watersheds in combination with graph theory based techniques is addressed. First, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. In the subsequent step the obtained graph is partitioned using recursive(More)
Objective and detailed mapping of urban land-cover types over large areas is important for hydrological modelling, as most man-made land-cover consist of sealed surfaces which strongly reduce groundwater recharge. Moreover, impervious surfaces are the predominant type in urbanized areas and can lead to increased surface runoff. Classification of man-made(More)