Object Recognition in Cluttered Scenes Using Appearance-Based Parts and Relationships

在混乱的场景中识别一般的三维物体仍然是一个具有挑战性的问题。特别是，设计一个适合建模大量通用对象的良好表示，并且对遮挡和分割问题具有鲁棒性，一直是取得成功的绊脚石。为了克服这些问题，本研究提出了一种基于外观的部件（ABPs）和关系（ABRs）的表示方法。abp和abr用封闭区域定义，使用最小描述长度原则分割，其外观从图像集合中获得，并紧凑地存储在参数特征空间的分层结构中。这种新的表示允许自由形式对象的表示，并具有几个吸引人的特征，即(1)对分割问题的鲁棒性，因为它是从分割图像中学习的；(2)基于局部而非全局属性的遮挡鲁棒性；(3)处理大型对象数据库的能力，因为它具有层次结构。作为本研究的一部分，还将开发拟议代表权的歧视性权力的概率模型。该模型将在贝叶斯框架中使用，基于PI之前的工作，设计一个识别系统。新的表示和相关的概率模型，加上贝叶斯推理引擎，将使系统能够自动识别和定位混乱场景中的一般物体。本研究的主要贡献是：(1)一种新的对象表示，能够表示大型通用对象数据库，并且对分割问题和遮挡问题也具有鲁棒性；(2)提出的表征的歧视性权力的概率模型和基于贝叶斯框架的识别系统，不需要使用特别启发式；(3)严格的实验方案来表征系统在遮挡下的性能。

The recognition of general three-dimensional objects in cluttered scenes remains a challenging problem. In particular, the design of a good representation suitable to model large numbers of generic objects that is also robust to occlusion and segmentation problems, has been an stumbling block in achieving success. In this research, a new representation using appearance-based parts (ABPs) and relationships (ABRs) is proposed to overcome these problems. ABPs and ABRs are defined in terms of closed regions, segmented using the Minimum Description Length principle, whose appearance is obtained from collections of images and compactly stored in a hierarchical structure of parametric eigenspaces. This new representation allows the representation of free form objects and has several appealing features, namely (1) robustness to segmentation problems, since it is learned from segmented images; (2) robustness to occlusion steming from the fact that it is based on parts rather than on global properties; and (3) ability to handle large object databases, due to its hierarchical nature. As part of this research a probabilistic model of the discriminatory power of the proposed representation will be also developed. This model will be used in a Bayesian framework, based on the PI's previous work, to design a recognition system. The new representation and the associated probabilistic model, coupled with a Bayesian reasoning engine, will enable the system to automatically recognize and locate generic objects in cluttered scenes. The main contributions of this research are: (1) a new object representation capable of representing large databases of generic objects that is also robust to segmentation problems and occlusion; (2) a probabilistic model of the discriminatory power of the proposed representation and a recognition system based on a Bayesian framework that does not require the use o f ad hoc heuristics; and (3) a rigorous experimental protocol to characterize the performance of the system in the presence of occlusion.