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Comparative Analysis of Image Segmentation Techniques


Affiliations
1 Department of Computer Applications,National Institute of Technology,Kurukshetra, India
 

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.
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  • Comparative Analysis of Image Segmentation Techniques

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Authors

Snehil Saxena
Department of Computer Applications,National Institute of Technology,Kurukshetra, India
Sidharth Jain
Department of Computer Applications,National Institute of Technology,Kurukshetra, India
Saurabh Tripathi
Department of Computer Applications,National Institute of Technology,Kurukshetra, India
Kapil Gupta
Department of Computer Applications,National Institute of Technology,Kurukshetra, India

Abstract


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.

References