Adaptive scene dependent filters in online learning environments
First Claim
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1. A computer based method for determining an object segment of an object represented in an electronic image, comprising the steps of:
- forming a multitude of binary maps obtained from a multitude of basic filter maps via unsupervised learning of a multi-feature segmentation, the step of unsupervised learning of a multi-feature segmentation further comprising the steps of;
forming training data vectors {right arrow over (m)}(x,y) using basic filter maps (Fi);
obtaining codebook vectors {right arrow over (c)}j from the training data vectors {right arrow over (m)}(x,y) using a vector quantization network (VQ);
generating adaptive topographic activation maps (VJ) from the training data vectors {right arrow over (m)}(x,y) and the codebook vectors {right arrow over (c)}j, the adaptive topographic activation map (VJ) being scene dependent and computed as Vj(x,y)=∥
{right arrow over (m)}(x,y)−
{right arrow over (c)}j∥
2;
forming a relevance map that serves as a prediction mask for a region around the object and computed as a superposition from a center map and a disparity map;
forming a selection of segments from the multitude of binary maps using the relevance map as a selection criterion; and
forming an object map based on the selection.
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Abstract
A method and a system for determining an object segment in an electronic image. Preferably the method or system is sufficiently fast to allow real-time processing. A method for determining an object segment in an electronic image may comprise the steps of unsupervised learning of a multi-feature segmentation and of forming a relevance map. The method may further comprise the step of estimating the probability of a segment belonging to an object by the overlap of the segment and the relevance map in the electronic image.
7 Citations
17 Claims
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1. A computer based method for determining an object segment of an object represented in an electronic image, comprising the steps of:
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forming a multitude of binary maps obtained from a multitude of basic filter maps via unsupervised learning of a multi-feature segmentation, the step of unsupervised learning of a multi-feature segmentation further comprising the steps of; forming training data vectors {right arrow over (m)}(x,y) using basic filter maps (Fi); obtaining codebook vectors {right arrow over (c)}j from the training data vectors {right arrow over (m)}(x,y) using a vector quantization network (VQ); generating adaptive topographic activation maps (VJ) from the training data vectors {right arrow over (m)}(x,y) and the codebook vectors {right arrow over (c)}j, the adaptive topographic activation map (VJ) being scene dependent and computed as Vj(x,y)=∥
{right arrow over (m)}(x,y)−
{right arrow over (c)}j∥
2;forming a relevance map that serves as a prediction mask for a region around the object and computed as a superposition from a center map and a disparity map; forming a selection of segments from the multitude of binary maps using the relevance map as a selection criterion; and forming an object map based on the selection. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A computer software program product embodied on a non-transitory computer readable medium for determining an object segment of an object represented in an electronic image, the computer software program product comprising instructions, which, when executed, cause a processor to perform the steps of:
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forming a multitude of binary maps obtained from a multitude of basic filter maps via unsupervised learning of a multi-feature segmentation, the step of unsupervised learning of a multi-feature segmentation further comprising the steps of; forming training data vectors {right arrow over (m)}(x,y) using basic filter maps (Fi); obtaining codebook vectors {right arrow over (c)}j from the training data vectors {right arrow over (m)}(x,y) using a vector quantization network (VQ); generating adaptive topographic activation maps (VJ) from the training data vectors {right arrow over (m)}(x,y) and the codebook vectors {right arrow over (c)}j, the adaptive topographic activation map (VJ) being scene dependent and computed as Vj(x,y)=∥
{right arrow over (m)}(x,y)−
{right arrow over (c)}j∥
2;forming a relevance map that serves as a prediction mask for a region around the object and computed as a superposition from a center map and a disparity map; forming a selection of segments from the multitude of binary maps using the relevance map as a selection criterion; and forming an object map based on the selection.
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Specification