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首页> 外文会议>Conference on Targets and Backgrounds >Information processes in visual and object buffers of scene understanding system for reliable target detection, separation from background, and identification
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Information processes in visual and object buffers of scene understanding system for reliable target detection, separation from background, and identification

机译：Visual and对象缓冲区的信息流程，用于可靠的目标检测，从背景分离和识别

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Modern target recognition systems suffer from the lack of human-like abilities to understand the visual scene, detect, unambiguously identify and recognize objects. As result, the target recognition systems become dysfunctional if target doesn't demonstrate remarkably distinctive and contrast features that allow for unambiguous separation from background and identification upon such features. This is somewhat similar to visual systems of primitive animals like frogs, which can separate and recognize only moving objects. However, human vision unambiguously separates any object from its background. Human vision combines a rough but wide peripheral, and narrow but precise foveal systems with visual intelligence that utilize both scene and object contexts and resolve ambiguity and uncertainty in the visual information. Perceptual grouping is one of the most important processes in human vision, and it binds visual information into meaningful patterns and structures. Unlike the traditional computer vision models, biologically-inspired Network-Symbolic models convert image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction between Visual and Object Buffers and the top-level system of Visual Intelligence. This interaction provides recursive rough context identification of regions of interest in the visual scene and their analysis in the object buffer for precise and unambiguous separation of the object from background/clutter with following recognition of the target.
机译：现代目标识别系统缺乏人类类似的能力来理解视觉场景，检测，明确识别和识别对象。结果，如果目标没有证明允许从背景中的明确分离和在这些特征上识别出明显的分离的显着独特和对比度，则目标识别系统变得功能失调。这有点类似于像青蛙这样的原始动物的视觉系统，这可以分离和识别移动物体。然而，人类视力明确地将任何物体与其背景分开。人体视觉结合了粗糙但宽的外围设备，狭窄但精确的硬质系统，具有视觉智能，可以利用场景和对象上下文并解决视觉信息中的歧义和不确定性。感知分组是人类视野中最重要的过程之一，它将视觉信息与有意义的模式和结构绑定。与传统的计算机视觉模型不同，生物启发网络符号模型将图像信息转换为“可理解的”网络符号格式，类似于关系知识模型。通过视觉和对象缓冲器和视觉智能顶级系统之间的相互作用实现了网络符号系统中的外围和硬质系统之间的相互作用。该交互为视觉场景区域的递归粗糙的上下文识别和对象缓冲区中的分析，以便在识别目标的背景/杂波中精确和明确地分离对象。

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