By Sven Behnke
Human functionality in visible belief through a long way exceeds the functionality of latest desktop imaginative and prescient structures. whereas people may be able to understand their surroundings nearly immediately and reliably lower than a variety of stipulations, desktop imaginative and prescient structures paintings good purely less than managed stipulations in constrained domains.
This publication units out to breed the robustness and pace of human belief through presenting a hierarchical neural community structure for iterative photograph interpretation. The proposed structure should be knowledgeable utilizing unsupervised and supervised studying concepts.
Applications of the proposed structure are illustrated utilizing small networks. moreover, a number of higher networks have been proficient to accomplish numerous nontrivial machine imaginative and prescient tasks.
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Additional info for Hierarchical Neural Networks for Image Interpretation
The set of Li ’s is called a Laplacian pyramid. The Li are computed as pixel-wise differences between Gi and its estimate Gi = expand(Gi+1 ), obtained by supersampling Gi+1 to the higher resolution and interpolating the missing values. Fig. 1(b) shows the Laplacian pyramid for the example. It decomposes the image into a sequence of spatial frequency bands. Perfect reconstruction of G0 is possible when Gk and L0 , L1 , . . , Lk−1 are given by using the recursion Gi = Gi + Li . Since for natural images the values of Li are mostly close to zero, they can be compressed using quantization.
Their receptive fields have a center-surround shape, mostly with double color opponency. Similar substructures exist in the next higher area, V2. Here, not columns, but thin stripes, thick stripes, and interstripes alternate. The stripes are oriented orthogonally to the border between V1 and V2. A V2 ‘hyperstripe’ covers a larger part of the visual field than a V1 hypercolumn and represents different aspects of the stimuli present in that region. 4, the blobs in V1 send color information primarily to the thin stripes in V2, while the orientation sensitive interblobs in V1 connect to interstripes in V2.
The spotlight of attention requires a master map that combines details from individual feature maps which are essential for recognition. Another view on the effect of attention was recently proposed by Reynolds and Desimone . 14. Now, instead of many stimuli with different characteristics, such as color and form, only one stimulus is functionally present in the receptive field. A different approach to the binding problem has been proposed by Singer and Gray  and Eckhorn et al. . They found that when an object activates a population of neurons in the visual cortex, these neurons tend to oscillate and to synchronize their action potentials.
Hierarchical Neural Networks for Image Interpretation by Sven Behnke