The biological vision model is devoted to provide a novel technology approach by merging new cognitive visual features with inspired nerve cells cognitive intelligence cortex and try to relate with real worlds object recognition. To perceive an arbitrary natural scene from complex environment perception and sensing in robotic mobility and manipulation on unstructured random natural scene understanding is a challenging problem in the visual image processing. This paper has considered neural network (NN) which is nothing but the grid of “neurons like” nodes. Based on the NN technique,the authors have proposed a new scheme for the scene understanding and recognition. In addition, the significant intellectual visual features are also incorporated for scene expression; those are very crucial and provide cognitive intelligence to robot vision. Due to the dynamic nature of artificial neural network intelligence, we have adapted the attributes of the Gabor filter and Laplacian of Gaussian filter; those play the significant role in the robot visual perception. Through the study of perceptual ability of the natural scene image from complex environment for robot vision is enhanced with the integration of cognitive visual features and the scene expression.

中文翻译：

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基于复杂环境感知和定位的视觉信息检索

生物视觉模型致力于通过将新的认知视觉功能与受启发的神经细胞认知智能皮层融合，并尝试与现实世界的对象识别相关联，从而提供一种新颖的技术方法。从复杂的环境感知和机器人移动感测以及对非结构化随机自然场景理解的操纵中感知任意自然场景是视觉图像处理中的一个难题。本文考虑了神经网络（NN），它不过是“神经元样”节点的网格。基于神经网络技术，作者提出了一种新的场景理解和识别方案。另外，重要的智力视觉特征也被整合到场景表达中。这些非常关键，可以为机器人视觉提供认知智能。由于人工神经网络智能的动态性质，我们调整了Gabor滤波器和Gaussian滤波器的Laplacian属性；它们在机器人的视觉感知中起着重要的作用。通过研究来自复杂环境的自然场景图像对机器人视觉的感知能力，通过认知视觉特征和场景表达的集成来增强。