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、监控等自动化领域许多任务的主要处理步骤，但仍然是一个尚未解决的问题。人类视觉感知的最新研究结果有力地表明，轮廓提取是目标识别的关键步骤。提出了一种基于轮廓的目标识别系统的开发方案。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对象轮廓。由于完整轮廓的提取可能是不可能的(例如，由于遮挡)，所以提取集中在轮廓的有意义的部分。该方法采用自下而上和自上而下的混合处理方法进行边缘分组。在金字塔体系结构中的每一步自底向上处理之后，采用自上而下的评估来选择最有希望的分组星座。使用认知激励约束定义了一种有前景的分组星座。根据格式塔心理学中已知的认知简单性原则，部分形状相似性将被用作此类限制的主要构件。与人类感知的最新结果相一致，边缘到物体轮廓部分的分组和利用形状相似度识别部分在物体识别中起着关键作用。这意味着如果只构造部分轮廓，并且识别不需要构造整个轮廓，则对象识别是可能的。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，将为扩大视觉系统的应用范围迈出重要一步。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。寻找视觉部分之间的相互依赖和结构信息的研究可能会导致对人类视觉感知和认知的进一步理解。这项拟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一个很好的跨学科工作资源。专业督学将就建议的研究课题提供课程和研讨会，将最先进的知识和技术带到课堂上。

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.