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Constraint Satisfying Image Processing Using Top-down Information

使用自上而下的信息满足约束的图像处理

基本信息

批准号：
05680296
负责人：
MINOH Michihiko
金额：
$ 1.15万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1994
项目状态：
已结题

项目摘要

In order to recognize an image, we need knowledge processing based on constructive inference, because there are not enough information for image recognition in an image. Top-down process, which is used to verify candidates, were recognized as control system which controls the processing area of images or parameters of image processing algorithm. In this research, we propose the concept of "Constraint Satisfying Image Processing" which is generalization of image processing algorithm which uses top-down information, and we implemented this concept.1. In the former image recognition system, knowledge processing like object hypothesis generation, were applied to the results of bottom-up image processing. However, this approach is something like "smoothing over vague results" and this is the main fact that the image recognition system can not be used practically. Therefore, we constructed the concept of "Constraint Satisfying Image Processing" which gives the information of what kind of object is needed, or what kind of result is needed, to the image processing system.2. We discussed about the representation of constraints in order to realize "Constraint Satisfying Image Processing". As a result, we realized that the image processing can be controlled by the constraints of feature value level representation.3. Active Contour Model (snakes) is a contour detection method which is an application of regularization method that is based on energy minimization principle. We developped the mechanism which controls the snakes to detect needed contour by providing information which represents what kind of contour must be detected.4. We have done the experience of controlling the process by belief factor of constraints in order to realize "Constraint Satisfying Image Processing" in various situation.

为了识别图像，我们需要基于构造推理的知识处理，因为图像中没有足够的信息用于图像识别。用于验证候选者的自顶向下过程被认为是控制图像处理区域或图像处理算法参数的控制系统。在本研究中，我们提出了“约束满足图像处理”的概念，它是使用自上而下信息的图像处理算法的概括，并实现了这个概念。 1．在以前的图像识别系统中，诸如对象假设生成之类的知识处理被应用于自下而上的图像处理结果。然而，这种方法有点像“对模糊结果进行平滑”，这是图像识别系统无法实际使用的主要事实。因此，我们提出了“约束满足图像处理”的概念，即向图像处理系统给出需要什么样的对象，或者需要什么样的结果的信息。 2．为了实现“满足约束的图像处理”，我们讨论了约束的表示。结果，我们意识到图像处理可以通过特征值级别表示的约束来控制。 3．主动轮廓模型（蛇）是一种轮廓检测方法，是基于能量最小化原理的正则化方法的应用。我们开发了一种机制，通过提供代表必须检测哪种轮廓的信息来控制蛇检测所需的轮廓。 4．我们已经完成了通过约束的置信因子来控制过程的经验，以实现各种情况下的“满足约束的图像处理”。