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- W4237573116 abstract "This chapter presents two algorithms for hand posture recognition, based on the computational model of the ventral stream of visual cortex. In the first algorithm, the hand postures are recognized by elastic graph matching between a model graph and patterns in an input image. Graph nodes are assigned to geometrically significant positions in a hand image and each node in the graph is labeled using an image feature extracted using the computational model of the ventral stream. A radial basis function is utilized as the similarity function for the matching process. In the second algorithm, shape based features which are good in interclass discrimination are identified. To classify the patterns, similarities of the shapes are compared. The second algorithm needs only single training image per class. The overall algorithm is computationally efficient due to the simplicity of the classification process. The algorithm is implemented in real-time for interaction between a human and a virtual character Handy. Handy can symbolically express the recognized hand posture and pronounce the relevant class name. The experimental results show that the algorithm is robust to hand posture size and shape variations and varying lighting conditions." @default.
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- W4237573116 date "2014-01-01" @default.
- W4237573116 modified "2023-09-25" @default.
- W4237573116 title "Hand Posture Recognition Using Neuro-Biologically Inspired Features" @default.
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- W4237573116 doi "https://doi.org/10.1007/978-981-287-056-8_7" @default.
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