![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Comparative Analysis of Image Segmentation Techniques
Image segmentation is a prominent task done in computer vision. Image thresholding is one such technique in image segmentation. Thresholding is a method of categorizing image intensities into two classes and on basis of that yielding an image which is a binary image, and ideally also has all the fine details of region of interest which an image should have for analysis. Image thresholding is widely used as it reduces the computational cost of processing the image and makes processing feasible in real world applications like medical imaging, object detection, recognition task, character recognition etc. This paper dwells into the depth of thresholding techniques to know which technique can perform better on all kind of images so as to extract region of interest. We found out that not every technique is good for all cases, Otsu’s global thresholding is a promising and faster way to segment and generate a binary image, but works well with images having negligible noise and region of interest already being very much clear in original image. Whereas the hybrid technique used are combination of global and local thresholding.
Keywords
Segmentation, Thresholding, Otsu’s, Niblack’s, Sauvola’s, Image Processing.
User
Font Size
Information
- Gurusamy, V., Kannan, S., & Nalini, G. (2013). REVIEW ON IMAGE SEGMENTATION TECHNIQUES. Journal of Pharmacy Research, 20125, 4548-4553.
- Verma, N., & Sharma, D. Region Merging Based Image Segmentation Using Maximal Similarity Mechanism. International Journal of Engineering Research and Development e-ISSN.
- Gonzalez,R.,C. and Woods,E.,R. , Digital Image Processing. 3 Edition. Pearson(2018)
- Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern recognition, 26(9), 1277-1294.
- Kumar, N. (2018). Thresholding in salient object detection: a survey. Multimedia Tools and Applications, 77(15), 19139-19170.
- Al-amri, S. S., Kalyankar, N. V., & Khamitkar, S. D (2010). Image Segmentation by Using Threshold Techniques” journal of computing..
- Khan, A. M., & Ravi, S. (2013). Image segmentation methods: A comparative study.
- Bhargavi, K., & Jyothi, S. (2014). A survey on threshold based segmentation technique in image processing. International Journal of Innovative Research and Development, 3(12), 234-239.
- Niblack, W. (1986). An introduction to digital image processing(Vol. 34). Englewood Cliffs: Prentice-Hall.
- Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern recognition, 33(2), 225-236.
- Vala, H. J., & Baxi, A. (2013). A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2(2), 387-389.
- Rabinovich, A., Vedaldi, A., & Belongie, S. J. (2007). Does image segmentation improve object categorization?. Department of Computer Science and Engineering, University of California, San Diego.
- Chang, K. Y., Liu, T. L., Chen, H. T., & Lai, S. H. (2011, November). Fusing generic objectness and visual saliency for salient object detection. In 2011 International Conference on Computer Vision (pp. 914-921). IEEE.
- Kuo, T. Y., Lai, Y. Y., & Lo, Y. C. (2010, July). A novel image binarization method using hybrid thresholding. In 2010 IEEE International Conference on Multimedia and Expo (pp. 608-612). IEEE.
- Chou, C. H., Huang, C. C., Lin, W. H., & Chang, F. (2005, September). Learning to binarize document images using a decision cascade. In IEEE International Conference on Image Processing 2005 (Vol. 2, pp. II-518). IEEE.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 180
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0