倒數(shù)粗糙熵圖像閾值化分割算法
doi: 10.11999/JEIT190559 cstr: 32379.14.JEIT190559
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西安郵電大學通信與信息工程學院 西安 710121
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電子信息現(xiàn)場勘驗應用技術(shù)公安部重點實驗室 西安 710121
基金項目: 國家自然科學基金(61671377, 61571361, 61601362)，西安郵電大學西郵新星團隊項目(xyt2016-01)
Image Thresholding Segmentation Method Based on Reciprocal Rough Entropy
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School of Communication and Information Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
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Key Laboratory of Electronic Information Application Technology for Scene Investigation, Public Security Ministry, Xi’an 710121, China
Funds: The National Natural Science Foundation of China(61671377, 61571361, 61601362), The Project of New Star Team of Xi’an University of Posts & Telecommunications (xyt2016-01)
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摘要:
基于粗糙集理論的粗糙熵閾值法不需要圖像之外的先驗信息。粗糙熵閾值法需要解決兩個問題，一是圖像信息不完整性的度量，二是圖像的?；?。該文基于倒數(shù)信息熵，提出一種倒數(shù)粗糙熵用來度量圖像中信息的不完整性。為了更好地對圖像進行?；捎靡环N基于均勻性直方圖的粒子選取方式。該文提出的倒數(shù)粗糙熵表述簡潔，計算簡單。實驗驗證了該文方法的有效性。
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關(guān)鍵詞:
- 圖像處理 /
- 閾值分割 /
- 粗糙熵 /
- 倒數(shù)粗糙熵 /
- ?；?/a>
Abstract:Image thresholding methods based on the rough entropy segment the images without prior information except the images. There are two problems to be considered in the rough entropy based thresholding methods, i.e., measuring the incompleteness of knowledge about an image and granulating the image. In this paper, reciprocal rough entropy, a new form of rough entropy, is defined to measure the incompleteness of the image information. In order to granulate the image effectively, a granule size selection method based on the homogeneity histogram is employed. The proposed reciprocal rough entropy is simple in expression and calculation. The experimental results verify the effectiveness of the proposed algorithm.
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Key words:
- Image processing /
- Thresholding segmentation /
- Rough entropy /
- Reciprocal rough entropy /
- Granulation
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表 1 6種算法的閾值比較
最大粗糙熵法 模糊熵法 羅的方法 Masi熵法 倒數(shù)熵法 倒數(shù)粗糙熵法 NDT image1 177 51 (151,151) 83 116 221 NDT image2 52 177 (106,115) 45 160 72 D5\irw02\000215 68 75 (66,70) 46 148 211 D5\irw06\000225 65 75 (66,67) 46 128 209 
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表 2 6種算法的ME值與SSIM值比較
NDT image1 NDT image2 D5\irw02\000215 D5\irw06\000225 ME SSIM ME SSIM ME SSIM ME SSIM 最大粗糙熵法 0.3605 0.0283 0.1996 0.5880 0.5556 0.0011 0.5671 0.0021 模糊熵法 0.9507 0.0015 0.2250 0.2345 0.5082 0.0013 0.4722 0.0029 羅的方法 0.6341 0.0098 0.0077 0.9822 0.5596 0.0013 0.5679 0.0021 Masi熵法 0.9136 0.0033 0.5470 0.1658 0.6841 0.0007 0.7181 0.0013 倒數(shù)熵法 0.8486 0.0049 0.2041 0.3172 0.0366 0.1367 0.0461 0.1585 倒數(shù)粗糙熵法 0.0016 0.9765 0.0429 0.9015 0.0051 0.7286 0.0084 0.6833
下載: 導出CSV
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