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首页> 外文期刊>Mobile robots >Active Vision and Image/Video Understanding Systems Built upon Network-Symbolic Models for Perception-Based Navigation of Mobile Robots in Real-World Environments
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Active Vision and Image/Video Understanding Systems Built upon Network-Symbolic Models for Perception-Based Navigation of Mobile Robots in Real-World Environments

机译：基于网络符号模型的主动视觉和图像/视频理解系统，用于在现实环境中基于感知的移动机器人导航

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摘要

To be completely successful, robots need to have reliable perceptual systems that are similar to human vision. It is hard to use geometric operations for processing of natural images. Instead, the brain builds a relational network-symbolic structure of visual scene, using different clues to set up the relational order of surfaces and objects with respect to the observer and to each other. Feature, symbol, and predicate are equivalent in the biologically inspired Network-Symbolic systems. A linking mechanism binds these features/symbols into coherent structures, and image converts from a "raster" into a "vector" representation. View-based object recognition is a hard problem for traditional algorithms that directly match a primary view of an object to a model. In Network-Symbolic Models, the derived structure, not the primary view, is a subject for recognition. Such recognition is not affected by local changes and appearances of the object as seen from a set of similar views. Once built, the model of visual scene changes slower then local information in the visual buffer. It allows for disambiguating visual information and effective control of actions and navigation via incremental relational changes in visual buffer. Network-Symbolic models can be seamlessly integrated into the MIST 4D/RCS architecture and better interpret images/video for situation awareness, target recognition, navigation and actions.
机译：为了完全成功，机器人需要具有类似于人类视觉的可靠感知系统。很难使用几何运算来处理自然图像。取而代之的是，大脑建立视觉场景的关系网络-符号结构，使用不同的线索来设置表面和对象相对于观察者以及彼此之间的关系顺序。功能，符号和谓词在受到生物学启发的网络符号系统中是等效的。链接机制将这些特征/符号绑定为一致的结构，并且图像从“光栅”转换为“矢量”表示。对于直接将对象的主视图与模型匹配的传统算法，基于视图的对象识别是一个难题。在网络符号模型中，派生的结构而不是主视图是识别的主题。从一组相似的视图中看到，这种识别不受对象的局部变化和外观的影响。建立后，视觉场景模型的变化要慢于视觉缓冲区中的本地信息。它允许通过视觉缓冲区中的增量关系变化来消除视觉信息的歧义，并有效控制动作和导航。网络符号模型可以无缝集成到MIST 4D / RCS架构中，并可以更好地解释图像/视频，以实现态势感知，目标识别，导航和行动。

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