Constraint Satisfying Image Processing Using Top-down Information

由于图像中没有足够的图像识别信息，需要进行基于构造性推理的知识处理。自顶向下的过程被认为是控制系统，控制图像的处理区域或图像处理算法的参数，用于验证候选人。在本研究中，我们提出了“满足约束的图像处理”的概念，它是对使用自顶向下信息的图像处理算法的推广，并实现了这一概念.在以往的图像识别系统中，对自底向上图像处理的结果进行了对象假设生成等知识处理。然而，这种方法有点像“平滑模糊的结果”，这是图像识别系统不能实际使用的主要事实。因此，我们构造了“满足约束的图像处理”的概念，它给出了图像处理系统需要什么样的对象，或者需要什么样的结果的信息.为了实现“满足约束的图像处理”，我们讨论了约束的表示。因此，我们认识到图像处理可以通过特征值水平表示的约束来控制.主动轮廓模型（Snakes）是一种基于能量最小化原理的正则化方法的应用。我们开发了控制蛇检测所需的轮廓的机制，通过提供信息表示什么样的轮廓必须检测.我们已经做了经验，控制过程中的信任因素的约束，以实现“约束满足图像处理”在各种情况下。

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.