Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Object Detection using Background Context
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Attentional mechanisms for interactive image exploration
EURASIP Journal on Applied Signal Processing
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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In this paper we propose a novel approach for visual object recognition. The main idea is to consider the object recognition task as an active process which is guided by multi-cue attentional indexes, which at the same time correspond to object's parts. In this method, a visual attention mechanism is carried out. It does not correspond to a different stage (or module) of the recognition process; on the contrary, it is inherent in the recognition strategy itself. Recognition is achieved by means of a sequential search of object's parts: parts selection depends on the current state of the recognition process. The detection of each part constraints the process state in order to reduce the search space (in the overall feature space) for future parts matching. As an illustration, some results for face and pedestrian recognition are presented.