An Improved Adaptive Binarization Algorithm Based on Fuzzy Logic
| Autor: | Kwang Baek Kim, Ho Chang Lee, Hyun Jun Park, Eui-Young Cha |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Adaptive neuro fuzzy inference system business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Boundary line Image processing Pattern recognition 02 engineering and technology Object (computer science) Fuzzy logic Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Local algorithm Algorithm Software Complement (set theory) |
| Zdroj: | International Journal of Software Engineering and Its Applications. 10:1-8 |
| ISSN: | 1738-9984 |
| DOI: | 10.14257/ijseia.2016.10.10.01 |
| Popis: | Image binarization is divided into global algorithm and local algorithm. Global binarization algorithms have a problem to describe objects that have similar brightness with a single threshold. Local binarization algorithms make boundary lines because these algorithms split the image into a specific size of blocks. Therefore, in this paper, we propose a binarization method to complement these problems. The proposed method uses triangular fuzzy membership function to classify the image into obvious regions and ambiguous regions. Obvious regions are binarized by using global binarization algorithm. Whereas ambiguous regions are binarized by using improved local algorithm. Experimental results show the proposed method binarizes the image with less information loss. Moreover, binarized image describes the object in more detail than global binarization methods and more natural than local binarization method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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