本文選題：圖像二值化　切入點：圖像分塊　出處：《計算機應用》2017年03期 　論文類型：期刊論文

【摘要】：針對目前局部閾值二值化結果存在目標虛假或斷裂的缺陷,提出了一種基于圖像分塊的局部閾值二值化方法。首先,將圖像分成若干子塊并分析每個子塊像素灰度變化情況;接著,取一定大小的局部窗口在圖像中移動,比較該局部窗口內與包含窗口自身且比窗口更大區(qū)域內的像素灰度變化情況,更大區(qū)域由窗口模板當前覆蓋的所有子塊組成,以此判斷窗口內是否為灰度變化平坦(或劇烈)區(qū)域;最后,根據(jù)不同的區(qū)域,給出具體的二值化方案。利用7種不同算法對4種不同類型的4組圖像進行了二值化實驗。實驗結果表明該算法在屏蔽背景噪聲和保留目標細節(jié)方面表現(xiàn)最優(yōu),特別地通過對車牌圖像的二值化結果進行定量分析后發(fā)現(xiàn)該算法能夠得到最高召回率和準確率。
[Abstract]:Aiming at the defect of false or broken target in the current local threshold binarization results, a local threshold binarization method based on image partitioning is proposed. Firstly, the image is divided into several sub-blocks and the gray level changes of each sub-block pixel are analyzed. Then, a local window of a certain size is taken to move in the image to compare the changes of pixel grayscale between the local window and the region containing the window itself and in a larger area than the window. The larger region is composed of all the sub-blocks currently covered by the window template. To determine whether the window is a flat (or violent) region of grayscale change; finally, depending on the region, A specific binarization scheme is presented. Four groups of images of four different types are binarized by using seven different algorithms. The experimental results show that the algorithm performs best in shielding background noise and preserving the details of the target. In particular, through the quantitative analysis of the binarization results of license plate images, it is found that the algorithm can obtain the highest recall rate and accuracy.
【作者單位】： 中國藥科大學理學院;
【基金】：國家自然科學基金資助項目(61501522)~~
【分類號】：TP391.41

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本文編號：1645703