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For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of an edge-map (as obtained from a standard edge-detection operation),(More)
The problem considered in this paper is how to localize and extract object boundaries (salient contours) in an image. To this end, we present a new active contour model, which is a neural network, based on self-organization. The novelty of the model consists in exploiting the principles of spatial isomorphism and self-organization in order to create #exible(More)
We propose a two-stage hierarchical arti®cial neural network for the segmentation of color images based on the Kohonen self-organizing map (SOM). The ®rst stage of the network employs a ®xed-size two-dimensional feature map that captures the dominant colors of an image in an unsupervised mode. The second stage combines a variable-sized one-dimensional(More)
We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability–plasticity trade-offs in neural architectures that employ ART; and (ii) the lack(More)
Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the <i>l</i><sub>0</sub> norm, even though, in practice, the <i>l</i><sub>1</sub> or the <i>l</i><sub>p</sub> ( 0 &lt;; <i>p</i> &lt;; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as(More)
We propose a novel approach to the detection and classification of human facial expressions using a morphable 3D model. We acquire the various expressions of an individual using a face scanner that produces textured 3D meshes using stereoscopic reconstruction. A morphable expression model (MEM), that incorporates emotion-dependent face variations in terms(More)