摘要
针对目前局部阈值二值化结果存在目标虚假或断裂的缺陷,提出了一种基于图像分块的局部阈值二值化方法。首先,将图像分成若干子块并分析每个子块像素灰度变化情况;接着,取一定大小的局部窗口在图像中移动,比较该局部窗口内与包含窗口自身且比窗口更大区域内的像素灰度变化情况,更大区域由窗口模板当前覆盖的所有子块组成,以此判断窗口内是否为灰度变化平坦(或剧烈)区域;最后,根据不同的区域,给出具体的二值化方案。利用7种不同算法对4种不同类型的4组图像进行了二值化实验。实验结果表明该算法在屏蔽背景噪声和保留目标细节方面表现最优,特别地通过对车牌图像的二值化结果进行定量分析后发现该算法能够得到最高召回率和准确率。
Aiming at the defects of local threshold binarization methods resulting in false or broken targets, a local threshold binarization method based on image blocks was proposed. Firstly, the image was divided into several sub-blocks and the distribution of gray-value in each block was analyzed. Then, a local window of a certain size was moved within the image and the gray-value variation of the pixels in this local window was compared with that in a larger area including aforementioned local window. The larger area consists of all the sub-blocks currently covered by the window template to determine whether the window is gray-value flat （or violent）. Finally, a specific binarization scheme was given according to different regions. Seven different algorithms were used to binarize four different types of four sets of images. The experimental results show that the proposed algorithm has the best performance in masking the background noise and preserving the target details. In particular, the algorithm can get the highest recall rate and accuracy rate through quantitative analysis of the license plate image binarization results.
出处
《计算机应用》
CSCD
北大核心
2017年第3期827-831,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61501522)~~
