CAREER: Learning to See - A Unified Segmentation and Recognition Approach

职业：学习观察 - 统一的细分和识别方法

• 批准号: 0447953
• 负责人: Jianbo Shi
• 金额: $ 40万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0447953&HistoricalAwards=false
• 关键词: CAREER; Learning; See; Unified; Segmentation

Image segmentation, shape detection and object recognition are three tightly coupled and intertwined visual processes. Only by integrating them together into a coherent, unified system can there be hope for achieving the long-standing goal of large-scale visual recognition. This proposal puts forward a learning-based unified graph formulation providing a principled and workable framework for attacking this extremely difficult problem. The proposed research efforts are directed along three fronts: 1) complete inference of object information by constructing multi-layer graph for shape detection, object recognition and segmentation; 2) unbiased integration of bottom-up low level cues with top-down knowledge of object shape; and 3) direct learning of the object recognition graph with supervised spectral graph cuts learning technique. The proposed 100- and 1000- Object Recognition Challenge will provide a highly objective and rigorous evaluation its success. This research draws upon ideas from a diverse set of disciplines: computer vision, machine learning, numerical analysis, and theory of computation. PI will continue to promote inter-disciplinary researches through graduate courses, conference tutorials and workshops, internships and research experience for undergraduates, as well as web page resources offering tutorial and open source code.

图像分割、形状检测和目标识别是三个紧密耦合、相互交织的视觉过程。只有将它们整合成一个连贯、统一的系统，才有希望实现大规模视觉识别的长期目标。 该提案提出了一个基于学习的统一图公式，为解决这个极其困难的问题提供了一个原则性和可行的框架。 提出的研究工作是针对沿着三个方面：1）通过构建多层图的形状检测，对象识别和分割的对象信息的完整的推理; 2）无偏集成的自下而上的低级别线索与自上而下的知识的对象形状;和3）直接学习的对象识别图的监督谱图切割学习技术。拟议的100和1000物体识别挑战赛将提供一个高度客观和严格的评估其成功。 这项研究借鉴了来自不同学科的想法：计算机视觉，机器学习，数值分析和计算理论。 PI将继续通过研究生课程，会议教程和工作坊，实习和本科生的研究经验，以及提供教程和开放源代码的网页资源，促进跨学科研究。