摘要
针对一维压缩采样丢失图像的结构信息,并带来识别精度损失的问题,提出了二维压缩采样的方法.利用一组稀疏基对疵点原始数据进行感知得到稀疏化数据,将织物疵点数据用二维稀疏表示,再利用范数优化的方法实现压缩数据的准确重建,根据稀疏基的不同得到织物疵点的不同分类.该方法解决了采集数据的泛滥和传感器的浪费,降低了计算的复杂度,有利于织物疵点的分类研究,进而为机器视觉识别织物疵点打下理论基础.
As to sampling loss of the structural information of the image for the one-dimensional compression and the loss of recognition accuracy,a concept of two-dimensional compression samples is proposed.Using a set of sparse-based perception,the sparse data on the raw data of the defect,fabric defect two-dimensional sparse are gotten.Finally,using norm optimization method accurate reconstruction of the compressed data is realized,and different fabric defect classification is gotten.This approach solves the proliferation of data collection and the sensor waste and greatly reduces the computational complexity,fabric defect classification,and thus to lay a theoretical foundation for machine vision to identify fabric defects.
出处
《中原工学院学报》
CAS
2012年第3期24-28,共5页
Journal of Zhongyuan University of Technology
基金
河南省科技攻关计划项目(0721002210032)
关键词
二维稀疏
织物疵点
范数优化
分类
two-dimensional sparse
fabric defects
norm optimization
classification
