计算机应用与软件2013，Vol.30Issue(4)：40-43,4.DOI:10.3969/j.issn.1000-386x.2013.04.012
基于3D直方图与爬山法的K-means车灯零件检测算法
K-MEANS ALGORITHM OF AUTOMOBILE LAMP PARTS INSPECTION BASED ON 3D HISTOGRAM AND CLIMBING HILL
摘要
Abstract
Since traditional K-means clustering algorithm is sensitive to initial value and easy to fall into local optimum, in order to improve the accuracy of complex automobile lamp parts classification, a new K-means lamp parts inspection algorithm based on the combination of 3D histogram and climbing hill is introduced. First, the local equidistant quantisation is conducted according to the distribution of colour lamp parts in colour space, and 3D colour histogram with self-adaptive resolution is obtained. Then, the hill-climbing algorithm is employed to find the maximum value of 3D colour histogram which is locally quantified equidistantly, according to the characteristic of maximum value the initial clustering K and the clustering centre are adaptively determined. Finally, the practical lamp parts images are used to carry out validating experiment. Experiment demonstrates that the algorithm can accurately detect the complex lamp parts with fairly good stability and applicability.关键词
K-means/3D直方图/爬山法/车灯零件/彩色视觉Key words
K-means/ 3D Histogram/ Hill-climbing algorithm/ Lamp parts/ Colour vision分类
信息技术与安全科学引用本文复制引用
张彩艳,穆平安,戴曙光,邬敏杰..基于3D直方图与爬山法的K-means车灯零件检测算法[J].计算机应用与软件,2013,30(4):40-43,4.基金项目
国家自然科学基金项目(51075280) （51075280）
上海市教育委员会重点学科项目(J50505). （J50505）
