Feature Group Utility in Model-Based 3D Object Recognition Systems

基于模型的 3D 对象识别系统中的功能组实用程序

• 批准号: 9209212
• 负责人: Patrick Flynn
• 金额: $ 10万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1995-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9209212&HistoricalAwards=false
• 关键词: Feature; Group; Utility; Model; Based

One of the most demanding applications in computer vision is model-based 3D object recognition. Such systems often use correspondence techniques to identify objects in an image; sets of bindings are formed between groups of scene features (e.g., points, curves, surfaces, or volumes) and compatible groups of model features. The discrimination ability ( or utility) of individual features (e.g. points, curves, surfaces, or volumes) and compatible groups of model features. The discrimination ability (or utility) of individual feature groups in these systems is a challenging and important problem in computer vision research. When recognition systems are expected to select the correct model(s) out of a database of hundreds or thousands, indexing methods (which quickly reject a subset of the model database from further consideration by the system) are useful or often essential if objects are to be recognized efficiently, and the use of feature groups offers a promising technique for performing this indexing task. This research investigates two facets of the feature group utility concept: saliency of feature groups extracted from the input image, and symmetric arrangements of features arising from inherent symmetries of the object models in the database. Preliminary experimental results are also described. Specific goals of the proposed research are to (a) place both the saliency and symmetry concepts on a firm theoretical basis, (b) generalize existing techniques for identifying salient and symmetric feature groups out of their current domain (of polyhedral or quadric surfaces into more expressive and generic classes of features, and (c) expand a current database of over 100 object models to include additional, more realistic objects.

计算机视觉中最苛刻的应用之一是基于模型的 3D 对象识别。 此类系统通常使用对应技术来识别图像中的对象；绑定集是在场景特征组（例如，点、曲线、曲面或体积）和兼容的模型特征组之间形成的。 单个特征（例如点、曲线、曲面或体积）和模型特征的兼容组的辨别能力（或效用）。 这些系统中各个特征组的辨别能力（或效用）是计算机视觉研究中的一个具有挑战性且重要的问题。 当识别系统需要从数百或数千个数据库中选择正确的模型时，索引方法（快速拒绝系统进一步考虑模型数据库的子集）是有用的，或者通常是必要的，如果对象被有效地识别，并且特征组的使用为执行该索引任务提供了一种有前景的技术。 这项研究研究了特征组效用概念的两个方面：从输入图像中提取的特征组的显着性，以及数据库中对象模型的固有对称性所产生的特征的对称排列。 还描述了初步实验结果。 所提出研究的具体目标是（a）将显着性和对称性概念置于坚实的理论基础上，（b）将用于识别当前领域（多面体或二次曲面）的显着性和对称性特征组的现有技术推广到更多领域。富有表现力和通用的特征类别，以及 (c) 扩展当前包含 100 多个对象模型的数据库，以包括其他更真实的对象。