Collaborative Research: From Edge Pixels to Recognition of Parts of Object Contours

计算机视觉中的目标识别虽然是机器人、监控和其他自动化领域中许多任务的主要处理步骤，但仍然是一个未解决的问题。人类视觉感知的最新研究结果表明，轮廓提取是物体识别的关键步骤。提出了一种基于轮廓的目标识别系统。新方法的第一步集中于从边缘图像中提取与人类感知的轮廓相对应的物体轮廓。由于提取完整的轮廓可能是不可能的（例如，由于遮挡），提取集中在轮廓的有意义的部分。提出的方法混合使用自底向上和自顶向下处理边缘分组。在金字塔结构中，自下而上的每一步处理之后，采用自上而下的评估来选择最有希望的分组星座。利用认知动机约束定义了一个有前景的分组星座。根据格式塔心理学的认知简单性原则，部分形状相似性将被用作此类约束的主要构建块。根据人类感知的最新成果，物体轮廓的边缘分组和形状相似度的识别在物体识别中起着关键作用。这意味着，如果只构造轮廓的一部分，物体识别是可能的，而整个轮廓的构造对于识别来说是不必要的。特别是，目标识别在存在遮挡和分割错误的情况下工作。本文提出的目标识别问题的解决方案，对提高视觉系统的应用范围具有重要意义。这项工作的结果将适用于视觉系统、大型图像数据库和视频分析系统。研究视觉部分之间的相互依赖关系和结构信息，有助于进一步了解人类的视觉感知和认知。建议的研究将为计算机科学和心理学的研究生和本科生提供跨学科工作的优秀资源。pi将提供有关拟议研究主题的课程和研讨会，将最先进的知识和技术带入教室。

Object recognition in Computer Vision, though being a main processing step in many tasks of robotics, surveillance, and other fields of automation, is still an unsolved problem. The recent results in human visual perception strongly suggest that contour extraction is a key step to object recognition. A development of a contour-based system for object recognition is proposed. The first step of the new approach concentrates on extraction of object contours from edge images that correspond to contours as perceived by humans. Since the extraction of complete contours may not be possible (e.g., due to occlusion), extraction is focused on meaningful parts of contours. The proposed approach uses a mixture of bottom up and top down processing for edge grouping. After each step of bottom-up processing in a pyramid architecture, top-down evaluation is applied to select the most promising grouping constellations. A promising grouping constellation is defined using cognitively motivated constraints. In accord with the cognitive simplicity principle known from Gestalt psychology, partial shape similarity will be used as a primary building block of such constraints. In accord with the newest results in human perception, grouping of edges to parts of object contours and recognition of the parts using shape similarity play a key role in object recognition. This means that object recognition is possible if only part of a contour is constructed, and the construction of the whole contour is not necessary for recognition. In particular, object recognition works in the presence of occlusion and segmentation errors. The proposed solution to the object recognition problem can make a significant step to improve the application scope of vision systems. The results of this work will be applicable to vision systems, large image databases, and video analysis systems. The proposed research to find interdependence and structural information among visual parts may lead to further understanding of human visual perception and cognition. The proposed research will provide an excellent resource for interdisciplinary work for graduate and undergraduate students in computer science and psychology. The PIs will offer courses and seminars on proposed research topics that will bring the state-of-the-art knowledge and technology to the classrooms.