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• W2282544530 abstract "The paper presents a perception oriented formal linguistic model for 3D sensors depth images. The objective of the proposed model is a compact abstract representation of the application environment naturally translatable to a target application. The applications targeted are tightly coupled perception action subsystems involved in independent environment exploration and learning be it human or machine. Field of view depth images obtained with recently developed CMOS 3D sensors are analyzed in their capacity to provide action oriented consistent data. For the 3D scene images a selective segmented is proposed in terms of salient objects from the depth image. The segmentation robustness and computation cost where the central constraints considered. The model as proposed uses a representation of the scene depth image in terms of object area and mean center location. An original formal language representation is proposed. A context free grammar is shown to be natural to 3D sensor data. The extension of the context free grammar with attributes adds structure to the model. It is also shown that the generated language translates directly depth labeling into action planning on the environment. The attribute added semantics is shown to facilitate an easy learning of the representation for human perception. The formal representation method proposed is analyzed in its performance in terms of estimated computation time and semantic relevance for sample applications. Further development of the model for multi layered representations for more complex applications areas are finally outlined." @default.
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• W2282544530 date "2008-07-22" @default.
• W2282544530 modified "2023-09-23" @default.
• W2282544530 title "A perception oriented formal model for 3D sensor depth images" @default.
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