杨杰, 张铭钧, 尚云超. 一种移动机器人视觉图像特征提取及分割方法[J]. 机器人, 2008, 30(4): 311-317.
引用本文: 杨杰, 张铭钧, 尚云超. 一种移动机器人视觉图像特征提取及分割方法[J]. 机器人, 2008, 30(4): 311-317.
YANG Jie, ZHANG Ming-jun, SHANG Yun-chao. A Visual Image Feature Extraction and Segmentation Method for Mobile Robot[J]. ROBOT, 2008, 30(4): 311-317.
Citation: YANG Jie, ZHANG Ming-jun, SHANG Yun-chao. A Visual Image Feature Extraction and Segmentation Method for Mobile Robot[J]. ROBOT, 2008, 30(4): 311-317.

一种移动机器人视觉图像特征提取及分割方法

A Visual Image Feature Extraction and Segmentation Method for Mobile Robot

  • 摘要: 提出一种基于HSI(Hue-Saturation-Intensity)颜色分量的颜色特征提取方法.该方法结合HSI颜色分量反映物体本质颜色的特点和直方图多阈值分类对图像内容的自适应优点,采用直方图多阈值分类方法量化各HSI颜色分量,组合量化后的颜色分量提取图像颜色特征.对该方法提取的视觉图像颜色特征进行聚类,并对视觉图像进行分割;分割结果验证了本文所提颜色特征提取方法的有效性和可行性.针对颜色分量直方图数据局部极大值引起的颜色分量过分类问题,提出一种通过修改极大值对之间的数据来去除颜色分量直方图局部极大值的方法.该方法定量分析颜色分量直方图数据的分布特点,从而确定并修改颜色分量直方图数据中的局部极大值.实验结果验证了该方法能够有效去除直方图数据局部极大值.

     

    Abstract: A color feature extraction method based on HSI(Hue-Saturation-Intensity) color components is proposed.The presented method integrates the characteristics that HSI color components express the intrinsic color information with the advantage that the multi-threshold classification method of component histogram adapts to the image content,quantifies HSI color components using multi-threshold classification method based on histogram,and extracts image color features by combining quantified color components.Color features of visual image extracted by this method are clustered,the visual image is segmented,and the segmentation results prove that the color feature extraction method is effective and feasible.For the excessive classification problem of color components caused by local maximums of color component histogram,a method is presented to eliminate local maximums in the color component histogram by amending the data between maximum pairs.Data distribution characteristics of the color component histogram are analyzed quantificationally,and then the local maximums in color component histogram are confirmed and amended.The experiment results indicate that the presented method can effectively eliminate local maximums in the histogram data.

     

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