This study presents a novel method based on entropy‐driven gradient evaluation (P‐EdGE) for detecting perceptual edges that represent boundaries of objects as perceived by human eyes. P‐EdGE is characterised by iteratively employing a shape‐changeable mask centred at a target pixel to sample gradient orientations of neighbouring pixels for measuring the directivity of the target pixel. The mask deforms to automatically cover pixels most suitable for exhibiting the directivity of the target pixel. The authors show that such an iterative scheme satisfies the similarity and proximity laws in Gestalt theory. The converged directivity in conjunction with the gradient magnitude are subjected to a Bayesian process, they show that doing so can help effectively determine whether the target pixel belongs to a perceptual edge. Experimental results are presented to justify the superiority of P‐EdGE over other methods in detecting perceptual edges.

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This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regional and inter-regional local statistics is used. Such extension in adjacency not only helps achieve more accurate segmentation results, but also improves input noise tolerance. Through iterating the three phases of simultaneous updating, self-organizing fusion, and extended merging, the training process converges without manual intervention, thereby conveniently obviating the need of pre-specifying the terminating number of objects. Unlike existing methods that sequentially merge regions, all regions in SOFNN can be processed in parallel fashion, thus providing great potentiality for a fully parallel hardware implementation.

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