Development of a high-speed image-understanding system designed directly from image data

本课题的目标是实现传统技术无法实现的高速图像理解系统。为了实现该系统，我们将设计的电路直接从图像实现到vlsi上。并将这种设计方法应用到“Parzen窗口法”和“概率神经网络法”中。这两种方法都使用了由大量样本图像构造的函数，并直接从图像样本数据中设计函数。由于这种数据直接实现，函数的计算速度比传统方法快得多。使用可重构的fpga建立了原型系统，并获得了以下结果：1)使用GA（遗传算法）优化了Parzen窗口方法中的窗口函数。利用窗口函数在人脸识别问题中获得了较高的识别精度2)提出的数据直接实现的思想不仅有效地应用于图像识别，而且有效地应用于声纳频谱识别3)开发了基于“概率神经网络方法”的图像理解系统，并与PC机连接。使用PC机对该系统进行了有效和精确的评估。为了提高识别精度，还采用主成分分析方法对系统输出进行主成分分析

The goal of this project is to realize a high-speed image-understanding system that has not been realized under the conventional technologies. In order to achieve the system, we implement the circuits designed directly from the images onto the VLSIs. And we apply this design approach to the "Parzen Window Method" and the "Probabilistic Neural Network Method". In both methods, the functions constructed using a great number of sample images are used, and the functions are directly designed from the image sample data. Because of this data-direct-implementation, the functions are calculated much faster than those used in the conventional approachesThe prototype system was established using reconfigurable LSIs (FPGAs) and following were obtained1)Window functions in the Parzen Window Method were optimized using the GA (genetic algorithm). High recognition accuracy was obtained with the window functions in the face recognition problem2)The proposed idea of the data-direct-implementation was efficiently applied not only to the image recognition but to the sonar-spectrum recognition3)The image-understanding system based on the "Probabilistic Neural Network Method" was developed and is connected with a PC. 'The system was effectively and precisely evaluated using the PC. The PC was also used to apply the principal component analysis to the outputs from the system in order to improve the recognition accuracy