CAJ | 학술논문

基于认知科学的研究提出一个新颖的计算模型用于物体识别.特征整合理论为计算模型提供了总体路线.基于最大熵原理构建学习过程,获得必要的先验知识构成认知网络.利用认知网络,将底层的图像特征和高层知识捆绑起来.利用条件随机场的基本概念和原理建模捆绑过程.将计算模型应用于现实世界的物体识别,在标准图像库上进行评估,取得了很好的效果.
기우인지과학적연구제출일개신영적계산모형용우물체식별.특정정합이론위계산모형제공료총체로선.기우최대적원리구건학습과정,획득필요적선험지식구성인지망락.이용인지망락,장저층적도상특정화고층지식곤방기래.이용조건수궤장적기본개념화원리건모곤방과정.장계산모형응용우현실세계적물체식별,재표준도상고상진행평고,취득료흔호적효과.
We propose a new computational model for object recognition based on the vision cognitive findings. Feature integration theory offers the roadmap for our computing model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on the basis of the hypothesis-maximum entropy principle.With the recognition network,we can bind the low-level image features and the high-level knowledge.Fundamental concepts and principles of conditional random fields are employed to model the binding process.We apply our model to real object recognition problem and evaluate it on the benchmark image databases to show its satisfactory performance.