Cooperation-Oriented Object Recognition in Cloeed Space Influenoed by Time-Variant Lighting

为了开发残疾人和老年人辅助机器人，我们开发了一些人工和自然光下物体的识别方法。[1]环境光照的识别：（1）颜色补偿：我们开发了一种利用神经网络检测环境光照变化特性的方法，并证明该方法可以应用于人工和自然光照改变的真实场景图像的补偿。（2）真实场景图像的分割和压缩：利用具有协作功能的多个神经网络，我们开发了人工和自然光照改变的真实场景图像的分割和压缩方法。我们还表明，他们的方法可以检测环境照明的变化特性。 [2]基于形状分析的物体识别：（1）圆形物体的识别：我们制定了一些代数公式来表示椭圆具有直径和共轭直径的几何特性，并表明使用这些公式可以在短时间内准确地识别圆形物体。（2）轮廓分割：由于从真实图像中获得的大多数轮廓成为不同物体边缘的链接，因此我们提出了一种通过估计曲率将轮廓分割成单个物体部分的方法相邻轮廓。[3]环境和物体的协同识别：我们提出了一种场景预测CG系统，用于支持环境照明和物体的协同识别，并通过实验证明该系统对于识别真实场景中的物体和环境照明非常有效。该系统大大降低了识别受时变照明影响的封闭空间中的物体的处理成本。

For the purpose of development of the assistant robot for handicapped and aged persons, we have developed some recognition methods for objects in artificial and natural lightings.[1] Recognition of environmental lighting :(1) Color compensation : We have developed a method for the detection of the changing property of the environmental lighting by using neural networks, and shown that this method could be applied to the compensation of real scene images changed by artificial and natural lightings.(2) Segmentation and compression for real scene images : By using multiple neural networks with the cooperative function, we have developed segmentation and compression methods for real scene images changed by artificial and natural lightings. Also we have shown that their methods could detect the changing property of the environmental lighting.[2] Object recognition based on the shape analysis :(1) Recognition of circular objects : We have made some algebraic formulas for representing the geometrical properties of ellipses that they have diameters and conjugate diameters, and shown that circular objects could be recognized accurately in short processing time by using the formulas.(2) Contour segmentation : Because most of contours obtained from real images become links of edges of different objects, we have proposed a method to segment the contours into individual object sections by estimating the curvatures of neighboring contours.[3] Cooperative recognition of environments and objects : We have proposed a scene predicting CG-system for supporting the cooperative recognition of environmental lighting and objects, and shown experimentally that the system is very effective to recognize objects and environmental lightings in real scene. The system drastically cuts the processing cost of recognizing objects in closed space influenced by time-variant lighting.