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RESEARCH OF NATURAL SCENE IMAGE RECOGNITION SYSTEMS HAVING IMAGE SEGMENTATION FUNCTIONS USING NONLINEAR DYNAMICS

具有非线性动力学图像分割功能的自然场景图像识别系统研究

基本信息

批准号：
11555102
负责人：
MORIE Takashi
金额：
$ 5.89万
依托单位：
HIROSHIMA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2000
项目状态：
已结题

项目摘要

The final target of this research is to construct an intelligent VLSI system that recognizes and understands real-world scene images with real-time. This system sequentially extracts target image regions from the input image in a time scale of microseconds and performs feature extraction using fast wavelet transformation. In this research, processing models suitable for VLSI systems have been proposed, and prototype LSI chips have been designed, fabricated, and evaluated. We have used resistive-fuse networks for rough region segmentation, nonlinear oscillator networks for region extraction, and nonlinear cellular networks for feature extraction.1. Processing models and algorithms suitable for VLSI implementationWe have proposed an oscillator network model that can segment gray-level images. This model requires only 5 bit calculation precision. We have also proposed a double-thresholding algorithm that can climinate incomplete segmentation timing. We have proposed a model for segmentation and extraction of target regions from natural scene images with arbitrary resolution by combining resistive-fuse and oscillator networks. We have achieved flexible human face recognition from natural scene images by combining these network models, Gabor-wavelet transformation, and a flexible graph matching method based on the dynamic-link architecture.2. Design and evaluation of prototype VLSI chips for image processing(1) By using newly developed pulse modulation circuits for arbitrary nonlinear transformation, we have designed oscillator network chips : (a) die size : 4.5 mm sq., 11x11 pixels, 0.6 micron CMOS technology, (b) die size : 9 mm sq., 50 x 50 pixels, 0.35 micron CMOS technology. The measurement results have confirmed oscillator operation with 100 micro-second.(2) We have designed a 1-dimensional pixel-parallel processing network VLSI chip that can perform resistive-fuse networks and cellular networks for Gabor transformation.

本研究的最终目标是建构一个能即时辨识与理解真实世界场景影像的智慧型超大型积体电路系统。该系统以微秒的时间尺度从输入图像中顺序提取目标图像区域，并使用快速小波变换进行特征提取。在这项研究中，已提出了适合超大规模集成电路系统的处理模型，原型LSI芯片的设计，制造和评估。我们使用连续融合网络进行粗略的区域分割，使用非线性振荡器网络进行区域提取，使用非线性细胞网络进行特征提取。适合于VLSI实现的处理模型和算法我们提出了一种能够分割灰度图像的振荡器网络模型。该模型只需要5位的计算精度。我们还提出了一个双阈值算法，可以climinate不完整的分割时间。本文提出了一种结合连续融合和振荡器网络的任意分辨率自然场景图像目标区域分割和提取模型。结合这些网络模型、Gabor小波变换和基于动态链接结构的灵活图匹配方法，实现了自然场景图像中灵活的人脸识别.图像处理用VLSI原型芯片的设计与评价（1）利用新开发的用于任意非线性变换的脉冲调制电路，设计了振荡器网络芯片：（a）芯片尺寸：4.5 mm sq.，11x11像素，0.6微米CMOS技术，（B）芯片尺寸：9 mm sq.，50 x 50像素，0.35微米CMOS技术。测量结果证实了振荡器工作在100微秒。(2)我们设计了一个一维像素并行处理网络的VLSI芯片，它可以执行连续融合网络和细胞网络的Gabor变换。