A study of high-level recognition integrated system realizing scene understanding by using combination of coarsely- segmented image regions

我们开发了一种图像处理模型和算法，将粗糙区域分割和提取，而不考虑精细结构，并根据分割区域之间的关系识别和理解包含多个物体的场景图像。我们已经开发了实现这些算法的vlsi和实时高阶图像识别的集成系统。在该系统中，通过USB连接PC机的fpga进行了电阻熔丝网络的粗区域分割和弹性图匹配（EGM）。相关的基础技术也得到了发展。在粗分割的区域中，当一个目标被另一个目标部分隐藏并被分割成两个区域时，我们在使用Terman-Wang非线性振荡网络同步发射时，通过考虑区域的亮度，成功地同时提取了两个分离的区域。此外，我们提出了一种基于简化振荡器网络模型的元胞自动机型区域提取算法，并在FPGA上实现了像素并行数字电路。为了弥补粗糙区域分割中边缘信息的缺失，我们提出了两种数字和模拟类型的像素并行各向异性扩散算法和生成主观轮廓的电路。这些算法已分别在CMOS数字电路和合并a /D电路中实现。使用这些扩散算法，我们已经成功地生成了典型图形的主观轮廓，如Kanizsa三角形。

We have developed an image processing model and algorithms, where coarse regions are segmented and extracted irrespective of fine structures, and a scene image including plural objects is recognized and understood based on the relationship between segmented regions. We have developed VLSIs implementing these algorithms and an integrated system for real-time higher-order image recognition. In the developed system, coarse region segmentation by a resistive-fuse network and elastic graph matching (EGM) are performed in FPGAs connected with a PC via USB. Related fundamental technologies have also been developed.In coarsely segmented regions, when an object is partly hidden by another object and separated into two regions, we have succeeded to extract two separated regions simultaneously by considering the brightness of the regions when synchronous firing in Terman-Wang nonlinear oscillator networks is used. Furthermore, we have proposed a cellular-automaton-type region extraction algorithm based on a simplified oscillator network model, and a pixel-parallel digital circuit performing this algorithm, which has been implemented in an FPGA.In order to compensate for lacking of edge information in coarse region segmentation, we have proposed two digital and analog-type pixel-parallel anisotropic diffusion algorithms and circuits for generating subjective contours.These proposed algorithms have been implemented in a CMOS digital circuit and merged A/D circuit, respectively. Using these diffusion algorithms, we have successfully generated subjective contours for typical figures such as a Kanizsa's triangle.