Knowledge extraction and understanding of images using Fuzzy inference neural network

研究结果如下：(1)基于模糊推理神经网络的图像识别与理解本文提出了一种新的用于图像识别与理解的神经网络系统。它由学习能力强的神经网络和处理规则能力强的模糊系统组成。系统中有3个过程：分割、图像识别和图像解释。计算机实验表明，正确识别的像素率为71.9%，获得了合理的理解结果。(2)考虑相对位置的景物图像识别在我们识别图像时，物体的相对位置起着重要的作用。提出了考虑这一事实的识别系统。(3)基于假设和测试的风景图像识别。识别系统可以从图像中反映出感性信息，并进行假设和测试，以获得更好的性能。(4)基于视觉系统的运动物体识别神经网络。提出了一种受生物视觉系统启发的新型神经网络系统。它可以从物体的运动和形状来识别物体。(5)三维物体识别的层次-并行神经网络。提出了一种由类似Neo-cognitron结构和双向信息处理组成的新型神经网络。(6)混沌模拟联想记忆。记忆功能对高级图像处理至关重要。这项研究旨在为下一代先进的识别系统。(7)句子概念的神经网络联想记忆我们的思维和推理等高级智能处理都是基于语言的。对于下一步的图像理解，神经网络的语言处理是必不可少的。在本研究中，我们提出了这样一种神经网络。

The following research results were obtained.(1) Image recognition and understanding using fuzzy inference neural networkIn this research, a new neural network system for image recognition and understanding is proposed. It consists of neural network which has high learning ability and fuzzy system which is good at treating rules. There are 3 processes in the system: segmentation, image recognition, and image interpretation. According to the computer experiments, 71.9% of pixels are correctly recognized and reasonable understanding results are obtained.(2) Scenery image recognition considering relative positionWhen we recognize images, relative positions of objects play an important role. The recognition system considering the fact is proposed.(3) Scenery image recognition based on hypothesis and testingThe recognition system can reflect Kansei information from the images and perform hypothesis and testing for better performance.(4) Moving object recognition neural network based on visual systemA new neural network system inspired by biological visual system is proposed. It can recognize objects from the movement and the shape.(5) Hierarchical-parallel neural network for 3-D object recognitionA new neural network composed of Neo-cognitron like structure and bi-directional information processing is proposed.(6) Chaos analog associative memory.Memory function is crucial for higher level image processing. This research is aimed for advanced recognition system for the next generation.(7) Neural network associative memory for concepts of sentenceOur higher level intelligent processing such as thinking and inference are based on language. For the next step of image understanding, language processing by neural network is indispensable. In this research, we proposed such a kind of neural network.