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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 ℓ 0 norm, even though, in practice, the ℓ 1 or the ℓ p (0 < p < 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly(More)
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)