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

协作研究：从边缘像素到物体轮廓部分的识别

• 批准号: 0534929
• 负责人: Longin Jan Latecki
• 金额: --
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534929&HistoricalAwards=false
• 关键词: Collaborative; Research; Edge; Pixels; Recognition

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.

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的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物体识别系统的开发方法。 新方法的第一步集中于从边缘图像中提取对象轮廓，该边缘图像对应于人类感知的轮廓。由于完整轮廓的提取可能是不可能的（例如，由于遮挡），提取集中在轮廓的有意义的部分。所提出的方法使用自下而上和自上而下的边缘分组处理的混合。在金字塔架构中的每一步自下而上的处理之后，应用自上而下的评估来选择最有希望的分组星座。一个有前途的分组星座定义使用认知动机的约束。在符合雅阁简单性原则，从格式塔心理学，部分形状相似性将被用作这种约束的主要组成部分。根据雅阁的最新研究成果，将物体的边缘划分为轮廓的一部分，并利用形状相似性对轮廓进行识别是物体识别的关键。这意味着，如果仅构造轮廓的一部分，则对象识别是可能的，并且整个轮廓的构造对于识别是不必要的。特别地，对象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。视觉部分之间的相互依赖性和结构信息的研究可能会导致人类视觉感知和认知的进一步理解。 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跨学科工作的优秀资源。PI将提供课程和研讨会上提出的研究课题，将国家的最先进的知识和技术的教室。