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

[1]环境光的识别：(1)环境光的识别：(1)颜色补偿：提出了一种利用神经网络检测环境光变化特性的方法，并证明了该方法可以应用于人工和自然光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：利用具有协同功能的多个神经网络，提出了对人工和自然光改变的真实场景图像的分割和压缩方法。[2]基于形状分析的物体识别：(1)圆形物体的识别：我们建立了一些代数公式来表示椭圆的几何性质，如它们的直径和共轭直径，并证明了利用这些公式可以在很短的处理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都是不同物体的边缘的连接，我们提出了一种通过估计相邻轮廓的曲率将轮廓分割成单独的对象部分的方法。[3)环境和对象的协同识别：我们提出了一个场景预测CG系统来支持环境光照和对象的协同识别，实验表明该系统对真实场景中的对象和环境光照的识别非常有效。该系统大大降低了在封闭空间中识别受时变照明影响的对象的处理成本。

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.