Object category learning and recognition

计算机视觉系统开始用于许多重要的应用，例如警告驾驶员即将发生的事故，从医学图像诊断疾病，或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所能做的。 我们能够看到一个场景，照片或绘画，并立即识别出存在的对象。 计算机视觉在过去的几年里取得了巨大的进步，但只达到了可以可靠地识别小的定义明确的对象类别（如“人脸”或“汽车”）或更大的特定对象库（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，使其可以推广到更广泛的对象和场景类别，从而开辟广泛的新应用。 目前最好的目标类别识别方法是基于使用对应于图像的尺度不变补丁的许多局部特征。 我们建议将有用的功能集扩展到更广泛的图像描述类。 我们将开发基于局部轮廓，形状和纹理的功能。 一个特定的研究领域将是对背景杂波具有鲁棒性的特征类，因为它们只对可能是对象一部分的特征做出响应，而忽略其边界轮廓之外的区域。 我们打算研究一系列基于AdaBoost、SVM或稀疏逻辑回归的学习方法，这些方法可以为特定对象类别选择适当的图像特征提供反馈。 我们还将研究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联合收割机组合成一个完整的对象类别识别系统，为计算机视觉开辟重要的新应用。

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.