Space Recognition as Visual Information Processing by Means of Hypothetical Reasoning

(1)Formulation of Hypothetical Reasoning for Visula Information Processing（1993）我们已经提出了假设推理来从物体的单目图像中恢复物体的三维（3D）形状。更详细地说，我们已经实现了EWS上的松弛优化方案的过程中选择假设的对象的3D形状和验证他们的形状恢复的假设下。(2)通过假设和验证获得物体的描述（1993）作为（1）中提出的方法的扩展，我们致力于推断在当前视图中不可见的物体的表面。该推理采用了关于对象形状的拓扑结构的假设以及关于对象的几何3D形状的假设。我们还研究了恢复的对象之间的相对位置和地板的位置的假设的基础上的3D空间中的对象的布置。(3)Sensing Strategy to Acquire the Spatial Information of an Object's Shape（1994）我们已经开发了一种规划视点的方法，用于附加传感，以验证由（2）中提出的方法推断的对象的不可见表面。为了使机器人达到规划的观点，我们采用视觉伺服校正机器人的传感器数据的当前配置。(4)理解运动物体的动作（1993-1994）为了理解人类的协调动作，我们提出了一种方法，通过使用动作中的合作性和连续性知识作为一种假设，从一系列图像中识别动作模式。我们还开发了一种方法来提取图像帧的显着特征，以描述行动简洁地从一个图像序列中的动作理解的过程中。

(1) Formulation of Hypothetical Reasoning for Visula Information Processing (1993)We have proposed hypothetical reasoning to recover the three dimensional (3D) shape of an object from its monocular image. In more detail, we have implemented the scheme of relaxational optimization on an EWS by means of the process of choosing hypotheses on the object's 3D shape and verifying them by the shape recovered under the hypotheses.(2) Acquiring the Description of an Object by Hypothesization and Verificaion (1993)As extension of the method proposed in (1), we have worked on inferring an object's surface that is invisible in the current view. The inference employs hypotheses on the topological structure of an object's shape together with those on the object's geometric 3D shape. We have also studied recovering the arrangement of objects in the 3D space based on hypotheses on the relative positions among the objects and the position of the floor.(3) Sensing Strategy to Acquire the Spatial Information of an Object's Shape (1994)We have developed a method of planning viewpoints for additional sensing to verify the objects' invisible surface inferred by the method proposed in (2). In order for a robot to reach the planned viewpoint, we employed visual servo to correct the robot's current configuration on the sensory data.(4) Understanding the Actions of Moving Objects (1993-1994)Toward understanding humans' coordinated actions, we have presented a method to recognize the action patterns from a sequence of images by employing the knowledge of cooperativeness and continuity in actions as a kind of hypotheses. We have also developed a method to extract image frames with salient features to describe the action concisely from an image sequence in the process of the action understanding.