Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Review
Dictionary learning algorithms for sparse representation
Neural Computation
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Sparse coding via thresholding and local competition in neural circuits
Neural Computation
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
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Research in computational neuroscience via Functional magnetic resonance imaging (fMRI) argued that recognition of objects in mammalian brain follows a sparse representation of responses to bar-like structures. We considered different scales and orientations of Gabor wavelets to form a dic-tionary. While previous works in the literature used greedy pursuit based meth-ods for sparse coding, this work takes advantage of a locally competitive algo-rithm (LCA) which calculates more regular sparse coefficients by combining the interactions of artificial neurons. Moreover proposed learning algorithm can be implemented in parallel processing which makes it efficient for real-time ap-plications. A synergetic neural network is used to form a prototype template, representing general characteristic of a class. A classification experiment is performed based on multi-template matching.