Abstract: Color image processing and analysis were implemented in HSI color space, concerning the difficulty in distinguishing the color similarity effectively in RGB color space. Firstly, the chromatic aberration components of hue, saturation, and intensity were calculated. Then through quoting fuzzy entropy, a group of information measures based on the fuzzy entropy were constructed to describe the natural characteristics of image edge quantitatively. Four component vectors were gotten via trained image samples, BP neural network was trained with some eigenvector of this four component vectors, and in the end the trained BP neural network was used for edge detection directly. Both the architecture and training of the BP neural are simple. Moreover, the proposed edge detector needs no threshold for conventional edge detection and has strong retention capacity of the details.

Key words: chromatic aberration component, fuzzy entropy, eigenvector, BP neural network, edge detection

摘要： 针对在RGB空间很难有效区分颜色相似性的问题，选择HSI颜色空间进行图像处理和分析。首先计算饱和度、色度、亮度等色差分量，通过引入模糊熵，构造出一组基于模糊熵的信息测度分量来定量描述图像的边缘特征。利用训练样本获取该组分量，并组成一特征向量对BP神经网络进行训练，然后将训练的BP网络直接用于边缘检测。BP网络的结构和训练比较简单，而且不需要设定阈值检测边缘。实验表明，该方法具有较强的细节保持能力，达到了令人满意的边缘检测效果。

关键词: 色差分量, 模糊熵, 特征向量, BP神经网络, 边缘检测