A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
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Proceedings of the 15th international conference on Multimedia
Modeling the Interactions of Bottom-Up and Top-Down Guidance in Visual Attention
Attention in Cognitive Systems
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Browsing large pictures under limited display sizes
IEEE Transactions on Multimedia
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Combined morphological-spectral unsupervised image segmentation
IEEE Transactions on Image Processing
A hybrid approach for Pap-Smear cell nucleus extraction
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
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In this paper, we propose a biologically inspired model of the middle stages of attention, with specific algorithmic details. Existing computational models of attention concentrate on their role in visual feature extraction and the selection of spatial regions. However, these methods ignore the role of attention in other stages. Extension of these models has been proposed by augmenting the unit of attentional selection to "proto-objects". In our approach, we extend attention to the middle stages and integrate the selection process with the perceptual grouping process. Integration is achieved by our innovative saliency driven perceptual grouping strategy, extending the traditional pixel-based saliency map to salient proto-objects. The proposed selective attention is made in two stages. Firstly, to achieve salient region localization, our method enhances the saliency map with region information from image segmentation and selects the most salient region (proto-object). Then, regions are organized using perceptual groupings, and their pop-out sequence is determined. Compared with traditional attention models our model provides saliency maps with meaningful region information, by eliminating misleading high-contrast edges, and focus of attention shifts in unit of perceptual object rather than spatial region. These two improvements fit to high stage vision information processing such as object recognition. Experiments in a reduced set of images show that our proposed model is able to automatically detect meaningful proto-objects.