【摘要】 为解决自主移动农业机器人在复杂工作环境进行视觉导航的场景识别,根据what-where双通道理论建立了场景感知模型以及场景表示模型,提出了一种基于概率框架的场景仿生识别方法。基于对比度假设和中心假设计算显著图并建立能量函数对该显著图进行优化;利用专家网络分析视觉注意焦点的内容作为what信息,以视觉注意焦点转移形成的扫视序列作为where信息;根据动作识别规则,利用what信息和where信息建立可观测的马尔可夫链模型实现场景识别。移动机器人场景识别过程与人的识别过程相似,实验证明所提方法对室内场景识别性能良好,准确率平均达87.3%。更多还原

【Abstract】 Scene recognition is the key to visual navigation for the agricultural mobile robot in unknown environment. This paper used what-where dual channel theory to build the models of scene perception,scene representation and scene recognition,and proposed a bionic method of scene recognition on the basis of probabilistic framework. This method first computed the bottom-up saliency map of scene based on the contrast prior and the center prior,which can be further optimized with the global energy function.Then shifted the visual focus of saliency map to obtain the saccade sequence as the "where information",and analyzed the content of the visual focus to obtain the "what information"with the experts network comprised of single layer perceptron. Lastly,according to the action recognition regularity of human,built the discrete and observable Markov model using the "what information " and the "where information". The parameters of the model can be determined by training the frame images from the camera on the mobile robot and can be viewed as the prior knowledge about different scenes,which can be recognized by maximizing the likelihood probability of the Markov recognition model. The whole recognition process is similar to human’s. Experimental results show that this method has good performance for indoor scenes and the recognition accuracy averaged out at 87. 3%.更多还原