An Integrated Method for Simultaneous Recognition and Segmentation of Deformable Objects

一种可变形物体同时识别与分割的集成方法

• 批准号: 9530768
• 负责人: James Duncan
• 金额: $ 24.5万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9530768&HistoricalAwards=false
• 关键词: Integrated; Method; Simultaneous; Recognition; Segmentation

This research is primarily aimed at robustly locating deformable structures/objects of approximately known shape from natural images. The problem of locating and recognizing underlying object structures in an image is of importance in many image analysis and computer vision applications including robot vision, pattern recognition and biomedical image processing. Algorithms that can reliably perform these basis tasks are at the core of a variety of systems that will permit humans to more effectively interact with pictorial data for the purposes of visualization, analysis, control or, as in the case of image database systems, retrieval of task-specific information within these applications areas. The robust identification and measurement of such structure is not always achievable using a single analysis technique that depends on a single image derived source of information. This is especially true when one is dealing with a wide range of images obtained under different conditions having different image content. The approach in this research project utilizes two different sources of image-derived information: 1.) gray-level gradients and ii.) homogeneity of intensity or texture elements, etc., as well as model-based information (i.e approximate object shape). Furthermore, two different processing methods are combined in this approach, in order to integrate the above-mentioned information sources: a) regionbased methods, which are primarily based on homogeneity properties and b.) boundary methods which capitalize on both gradient (imagederived) and shape (model based) properties. A game theoretic framework will be used, where, unlike the global objective approach which is widely used as an integration method in computer vision, the modularity of the underlying objectives are retained. The integration problem is then framed as a family of coupled and coex isting objectives whereby the output of one module depends upon the previous outputs of the other modules. This effort will build on initial work which has been able to locate structure in two dimensional images. It will also extend the concept to analyzing three dimensional image data. Both theoretical and experimental investigations will be carried out regarding the applicability and the limits of the approach.

这项研究的主要目的是从自然图像中鲁棒地定位近似已知形状的可变形结构/对象。 图像中目标结构的定位和识别问题在机器人视觉、模式识别和生物医学图像处理等图像分析和计算机视觉应用中具有重要意义。 能够可靠地执行这些基本任务的算法是各种系统的核心，这些系统将允许人类为了可视化、分析、控制或在图像数据库系统的情况下检索这些应用领域内的任务特定信息的目的而更有效地与图像数据交互。 的鲁棒辨识与测量 使用依赖于单个图像导出信息源的单个分析技术并不总是能够实现这种结构。 当处理在具有不同图像内容的不同条件下获得的大范围图像时，尤其如此。 本研究项目中的方法利用了两种不同的图像衍生信息来源：1）。 灰度梯度和ii.）强度或纹理元素的均匀性， 等等， 以及模型信息 (i.e近似 对象 形状）。 此外，委员会认为， 两 为了整合上述信息源，在该方法中组合了不同的处理方法：a）基于区域的方法，其主要基于均匀性特性，以及B.） 边界 方法，利用 两 梯度（图像导出）和形状（基于模型）属性。 将使用博弈论框架，其中与广泛用作计算机视觉集成方法的全局目标方法不同，保留了底层目标的模块化。 集成问题，然后作为一个家庭的耦合和coex的目标，其中一个模块的输出取决于以前的输出的其他模块。 这项工作将建立在已经能够在二维图像中定位结构的初步工作的基础上。 它还将扩展的概念，分析三维图像数据。 理论和实验研究将进行有关的方法的适用性和局限性。