Iris Vanhamel

Bio: Iris Vanhamel is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 8, co-authored 24 publications receiving 174 citations.

Unsupervised watershed-driven region-based image retrieval

Abstract: 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 automatically. In this framework, the proposed robust feature extraction and the many-to-many region matching along with the novel region weighting for enhancing feature discrimination play a major role. Experimental results demonstrate the performance of the proposed strategy.

Scale Selection for Compact Scale-Space Representation of Vector-Valued Images

Abstract: 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. For the latter, we propose to adapt the optimal diffusion stopping time criterion of Mrazek and Navara in such a way that it may identify multiple scales of importance.

Scale space segmentation of color images using watersheds and fuzzy region merging

Abstract: 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 region distribution are estimated as region dissimilarity features across the scale-space stack, and combined using a fuzzy rule based system. A minima-linking process by downward projection is carried out and subsequently the region dissimilarity, combining color, scale and homogeneity is estimated for the finer scale (localization scale). The final segmentation is derived using a previously presented merging process. To validate its performance qualitative and quantitative results are provided.

Hierarchical Segmentation Using Dynamics of Multiscale Color Gradient Watersheds

Abstract: In this paper, we describe and compare two multiscale color segmentation schemes basedon the Gaussian multiscale and the Perona and Malik anisotropic diffusion. The proposed segmentation schemes consist of an extension to color images of an earlier multiscale hierarchical watershedsegm entation for scalar images. Our segmentation scheme constructs a hierarchy among the watershedre gions using the principle of dynamics of contours in scale-space. Each contour is valuated by combining the dynamics of contours over the successive scales. We conduct experiments on the scale-space stacks created by the Gaussian scale-space andt he Perona and Malik anisotropic diffusion scheme. Our experimental results consist of the comparison of both schemes with respect to the following aspects: size and in formation reduction between successive levels of the hierarchical stack, dynamics of contours in scale space and computation time.

Scale selection for compact scale-space representation of vector-valued images

Abstract: This paper investigates the scale selection problem for vector-valued nonlinear diffusion scale-spaces. We present a new approach for the localization scale selection, which aims at maximizing the image content's presence by finding the scale having a maximum correlation with the noise-free image. For scale-space discretization, we propose to address an adaptation of the optimal diffusion stopping time criterion introduced by Mrazek and Navara [1], in such a way that it identifies multiple scales of importance.

Introduction to the special issue on multimedia implementation », IEEE Trans. On Circuits and Systems for Video Technology

Abstract: Keywords: LTS3 Reference LTS-ARTICLE-2004-019 Record created on 2006-06-14, modified on 2016-08-08

Survey of contemporary trends in color image segmentation

Abstract: In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

A Stochastic Framework for the Identification of Building Rooftops Using a Single Remote Sensing Image

Abstract: The identification of building rooftops from a single image, without the use of auxiliary 3-D information like stereo pairs or digital elevation models, is a very challenging and difficult task in the area of remote sensing. The existing methodologies rarely tackle the problem of 3-D object identification, like buildings, from a purely stochastic viewpoint. Our approach is based on a stochastic image interpretation model, which combines both 2-D and 3-D contextual information of the imaged scene. Building rooftop hypotheses are extracted using a contour-based grouping hierarchy that emanates from the principles of perceptual organization. We use a Markov random field model to describe the dependencies between all available hypotheses with regard to a globally consistent interpretation. The hypothesis verification step is treated as a stochastic optimization process that operates on the whole grouping hierarchy to find the globally optimal configuration for the locally interacting grouping hypotheses, providing also an estimate of the height of each extracted rooftop. This paper describes the main principles of our method and presents building detection results on a set of synthetic and airborne images.

Multiscale gradient watersheds of color images

Abstract: 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, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.