Object category learning and recognition

物体类别学习与识别

• 批准号: 36807-2006
• 负责人: Lowe, David
• 金额: $ 4.15万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361744
• 关键词: Object; category; learning; recognition

Computer vision systems are starting to be used for many important applications, such as warning drivers of impending accidents, diagnosing disease from medical images, or allowing robots to perform useful tasks in household environments.  However, the capabilities of human vision remain far beyond what computer vision systems can do.  We are able to look at a scene, photograph, or drawing and immediately recognize the objects that are present.  Computer vision has made dramatic advances within the past few years, but has only reached the point at which small well-defined object catgories (such as "faces" or "cars") or larger libraries of specific objects (such as a database of particular buildings) can be reliably recognized.  The research in this proposal aims to expand the capabilities of computer vision systems so that they can generalize to much broader categories of objects and scenes, and thereby open up a broad range of new applications.  The best current approaches to object class recognition are based on using many local features corresponding to scale-invariant patches of an image.  We propose to extend the useful set of features to a much broader class of image descriptions.  We will develop features based on local contours, shapes, and textures.  A particular area of investigation will be feature classes that are robust to background clutter because they respond only to features that are likely to be part of the object while ignoring regions outside its bounding contours.  We intend to examine a range of learning methods, based upon AdaBoost, SVM, or sparse logistic regression, that can provide feedback on selecting appropriate image features for particular object categories.  We will also work on new methods for pose clustering and verification.  Our goal is to combine all of these new components into a complete system for object class recognition, opening up important new applications for computer vision.

计算机视觉系统开始被用于许多重要的应用，如警告司机即将发生的事故，根据医学图像诊断疾病，或者允许机器人在家庭环境中执行有用的任务。然而，人类视觉的能力仍然远远超出计算机视觉系统的能力。我们能够观看场景、照片或图画，并立即识别存在的对象。在过去的几年里，计算机视觉取得了戏剧性的进步但仅仅达到了可以可靠地识别定义明确的小对象类别(例如，面孔或汽车)或较大的特定对象的库(例如特定建筑物的数据库)的程度。该提案中的研究旨在扩展计算机视觉系统的能力，以便它们可以推广到更广泛的对象和场景类别，从而开辟了广泛的新应用。目前最好的对象类识别方法是基于使用与图像的比例不变的块相对应的许多局部特征。我们建议将有用的特征集扩展到更广泛的图像描述类别。我们将开发基于局部轮廓、形状和纹理的特征。*特定的研究区域将是对背景杂乱具有鲁棒性的要素类，因为它们只对可能是对象的一部分的特征作出响应，而忽略其边界轮廓之外的区域。*我们打算基于AdaBoost、支持向量机、或稀疏逻辑回归，它可以为特定对象类别选择合适的图像特征提供反馈。我们还将致力于姿势聚类和验证的新方法。我们的目标是将所有这些新组件组合到一个完整的对象类识别系统中，为计算机视觉开辟重要的新应用。