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A sample of a single resonant feature, C a r , comprised of 2 hypercomplex features (C a h and C b h ) and 6 complex features (C a c0 . . . C a c2 and C b c0 . . . C b c2 , where C a c2 = C b c2 ). The grid represents a simple cell field arranged in cortical columns, on top of which complex cell pooling occurs. Red areas represent active hypercomplex cells and orange areas represent active simple cells that have been pooled by complex cells (represented by lines). The primary complex cells, C a c0 and C b c0 , are associated with the largest angles in their respective hypercomplex cells. The cortical column is used to arrange complex cells in a clockwise manner for rotation invariance and to normalize complex cell length for scale invariance. The grid is represented as a texture on the GPU.  

A sample of a single resonant feature, C a r , comprised of 2 hypercomplex features (C a h and C b h ) and 6 complex features (C a c0 . . . C a c2 and C b c0 . . . C b c2 , where C a c2 = C b c2 ). The grid represents a simple cell field arranged in cortical columns, on top of which complex cell pooling occurs. Red areas represent active hypercomplex cells and orange areas represent active simple cells that have been pooled by complex cells (represented by lines). The primary complex cells, C a c0 and C b c0 , are associated with the largest angles in their respective hypercomplex cells. The cortical column is used to arrange complex cells in a clockwise manner for rotation invariance and to normalize complex cell length for scale invariance. The grid is represented as a texture on the GPU.  

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Conference Paper
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We present a biologically motivated classifier and feature descriptors that are designed for execution on single instruction multi data hardware and are applied to high speed multiclass object recognition. Our feature extractor uses a cellular tuning approach to select the optimal Gabor filters to process a given input, followed by the computation...

Contexts in source publication

Context 1
... orientation of the traversal path is α i and reflects the orientation of the underlying object that is activating the complex cells. The orientation difference between any two complex cell fea- tures is α ij = abs(α i − α j ); examples of this can be seen in Figure 2. Rotation invariance is achieved by determining the largest α ij value and setting C a ci as the primary complex cell. ...
Context 2
... sample resonant feature can be seen in Figure 2. The pooling operation links 2-tuples of hypercomplex cell fea- tures together into a resonant feature. ...

Citations

... Parks et al. [19] presented a CUDA implementation of a saliency system for detection and the HMAX model for recognition, both steps are 10 times faster compared with the original algorithms. Woodbeck et al. [20] presented a GPU implementation of a bio-inspired model-similar to the HMAX model-using the OpenGL framework that achieves speedups of up to three orders of magnitude. Note that these last three works are based on the HMAX model, a region-based visual feature system, which is similar to our proposed model called the AVC algorithm. ...
Article
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... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. ...
... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. ...
... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. It is therefore advocated that the same set of kernels present at the first layer (i.e. ...
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... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. ...
... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. ...
... Many variations of the above underlying ideas have been proposed, including various learning strategies at higher layers [145,147], wavelet based filters [71], different feature sparsification strategies [73,110,147] and optimizations of filter parameters [107,147]. Yet another body of research, advocates that the hierarchical processing (termed F ilter → Rectif y → F ilter) that takes place in the visual cortex deals progressively with higher-order image structures [5,48,108]. It is therefore advocated that the same set of kernels present at the first layer (i.e. ...
Preprint
Full-text available
This document will review the most prominent proposals using multilayer convolutional architectures. Importantly, the various components of a typical convolutional network will be discussed through a review of different approaches that base their design decisions on biological findings and/or sound theoretical bases. In addition, the different attempts at understanding ConvNets via visualizations and empirical studies will be reviewed. The ultimate goal is to shed light on the role of each layer of processing involved in a ConvNet architecture, distill what we currently understand about ConvNets and highlight critical open problems.
... There are some other object recognition models that have used the Adaptive Resonance Theory. For example, Woodbeck et al. [23] proposed a biologically plausible hierarchical structure which was an extension of the sparse localized features (SLF) suggested by Mutch et al. [24]. One of their contributions was that, instead of using support vector machines (SVM) for classification, they used Fuzzy ARTMAP as a biologically plausible multiclass classifier [25] which is based on the Adaptive Resonance Theory (ART). ...
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