Dimension reduction by local principal component analysis
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Mixtures of Local Linear Subspaces for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
View-Based Dynamic Object Recognition Based on Human Perception
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
An attention-driven model for grouping similar images with image retrieval applications
EURASIP Journal on Applied Signal Processing
Cognitive vision: The case for embodied perception
Image and Vision Computing
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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Brain imaging studies suggest that expert object recognition is a distinct visual skill, implemented by a dedicated anatomic pathway. Like all visual pathways, the expert recognition pathway begins with the early visual system (retina, LGN/SC, striate cortex). It is defined, however, by subsequent diffuse activation in the lateral occipital cortex (LOC), and sharp foci of activation in the fusiform gyrus and right inferior frontal gyrus. This pathway recognizes familiar objects from familiar viewpoints under familiar illumination. Significantly, it identifies objects at both the categorical and instance (subcategorical) levels, and these processes cannot be disassociated. This paper presents a four-stage functional model of the expert object recognition pathway, where each stage models one area of anatomic activation. It implements this model in an end-to-end computer vision system, and tests it on real images to provide feedback for the cognitive science and computer vision communities.