Efficient edge detection and object segmentation using Gabor filters
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
How Human Visual Systems Recognize Objects - A Novel Computational Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Facial feature detection using Haar classifiers
Journal of Computing Sciences in Colleges
Number Plate Recognition Based on Support Vector Machines
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically Inspired Object Categorization in Cluttered Scenes
AIPR '07 Proceedings of the 36th Applied Imagery Pattern Recognition Workshop
An effective recognition method of breast cancer based on PCA and SVM algorithm
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
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Object recognition has attracted the attention of many researchers as it is considered as one of the most important problems in computer vision. Two main approaches have been utilized to develop object recognition solutions i.e. machine and biological vision. Many algorithms have been developed in machine vision. Recently, Biology has inspired computer scientist to map the features of the human and primate's visual systems into computational models. Some of these models are based on the feed-forward mechanism of information processing in cortex; however, the performance of these models has been affected by the increase of clutter in the scene. Another mechanism of information processing in cortex is called the feedback. This mechanism has also been mapped into computational models. However, the results were also not satisfying. In this paper an object recognition model based on the integration of the feed-forward and feedback functions in the visual cortex is proposed.