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Gupta, Kapil
- Comparative Analysis of Image Segmentation Techniques
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1 Department of Computer Applications,National Institute of Technology,Kurukshetra, IN
1 Department of Computer Applications,National Institute of Technology,Kurukshetra, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 32 (2020), Pagination: 13-20Abstract
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.Full Text
References
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- YOLO-An Object Detection Algorithm
Abstract Views :152 |
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Authors
Bhawna Vohra
1,
Kapil Gupta
1
Affiliations
1 Department of Computer Applications, National Institute of Technology, Kurukshetra, IN
1 Department of Computer Applications, National Institute of Technology, Kurukshetra, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 32 (2020), Pagination: 29-43Abstract
In this paper, an approach to object detection known as YOLO is presented. It is extremely fast. We use this algorithm to detect multiple objects in an image. The base YOLO model processes image in real-time at 45 frames per second. YOLO outperforms other detection methods including R-CNN as it is more generalized. It works on various types of datasets including artworks.Keywords
YOLO, Object Detection, RCNN.Full Text
References
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
- Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
- Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. CVPR (1), 1(511-518), 3.
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).