Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The handbook of brain theory and neural networks
Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Link Matching between Feature Columns for Different Scale and Orientation
Neural Information Processing
Rapid correspondence finding in networks of cortical columns
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Learning of lateral connections for representational invariant recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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We here address the problem of scale and orientation invariant object recognition, making use of a correspondence-based mechanism, in which the identity of an object represented by sensory signals is determined by matching it to a representation stored in memory. The sensory representation is in general affected by various transformations, notably scale and rotation, thus giving rise to the fundamental problem of invariant object recognition. We focus here on a neurally plausible mechanism that deals simultaneously with identification of the object and detection of the transformation, both types of information being important for visual processing. Our mechanism is based on macrocolumnar units. These evaluate identity- and transformation-specific feature similarities, performing competitive computation on the alternatives of their own subtask, and cooperate to make a coherent global decision for the identity, scale and rotation of the object.