Learning invariance from transformation sequences
Neural Computation
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning invariant object recognition in the visual system with continuous transformations
Biological Cybernetics
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by artificial cortical maps
Neural Networks
Class specific redundancies in natural images: a theory of extrastriate visual processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Human can perform object recognition with high accuracy under a variety of object rotations and translations. The structure and function of the visual cortex has inspired many models for invariant object recognition. In this paper, we propose a hierarchical model for object recognition based on the two well-known properties of the visual cortex neurons: invariant responses to stimulus transformations and redundancy reduction. We used the trace learning rule to provide the neurons in the model with invariant responses to object transformations. In hierarchical neural networks, neighboring neurons are tuned to similar features because their receptive fields in the image overlap. This similarity results in a form of redundancy in neuronal responses. We used a variant of divisive normalization mechanism to increase the efficiency of responses of neurons in the model. Results of experiments demonstrate the high recognition rates of the proposed model.