【作者】 范瑾；

【导师】 田沛；

【作者基本信息】 华北电力大学（河北） ， 控制理论与控制工程， 2007， 硕士

【摘要】 本文以犯罪现场遗留足迹为对象,阐述了图像处理和模式识别的相关知识,实现复杂背景下的对象特征提取。足迹是刑侦工作中经常用到的生物特征,针对足迹照片存在噪声干扰大,边缘不清晰,人工识别受主观因素影响等问题,提出了基于二维最大熵的类间方差、基于模糊聚类和形态学滤波的足迹特征识别方法。利用二维最大熵的类间方差算法或模糊聚类算法将图像进行阈值分割,得到鞋底各部分受力情况的粗略估计;同时结合形态学滤波和阈值面积消除法抑制噪声,并自适应地确定特征识别门限值;最后在原始图像上标识出特征区。该方法运算简单,能充分利用图像的梯度和灰度信息,有效消除噪声,提高特征区域边缘检测的准确性,为刑侦工作的足迹自动识别提供了新途径。更多还原

【Abstract】 Taking the tread in alibi as object, this thesis introduces some knowledge about image processing and pattern recognition, and extracts characteristic feature in complex background. Tread feature recognition is one of the most important biometrics in criminal investigation work. A scheme was developed to correctly reproduce distinct, continuous edges and decrease manual intervene based on the maximum variance between clusters (Otsu) method and the algorithms of fuzzy C-means clustering. In this paper, an improved Otsu algorithm is proposed, which is based on the two-dimensional bound histogram. First the Otsu method and the algorithms of fuzzy C-means clustering is used to segment the heavy pressure surface form the image, then the morphological filter and area threshold removing method are applied to filter the small area and the noise, with an adaptive method to select the area extraction threshold. Experimental results show that the scheme reproduces accurate, smooth edges due to the use of the gradient and gray information, providing a new way for tread feature automatic recognition.更多还原