A REVIEW ON IMAGE SEGMENTATION USING GPU
DOI:
https://doi.org/10.24297/ijct.v15i10.4502Keywords:
Segmentation, GPU, Image Processing, OpenCV, Region Growing algorithm, CUDAAbstract
Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources.
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References
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[2] J. X. Hua and M.-H. Jeong, "Real-Time Range Image Segmentation on GPU," International Conference on Control, Automation and Systems (ICCAS), pp. 150-153, 2014.
[3] S. Katsigiannis, E. Zacharia and D. Maroulis, "MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU," IEEE, pp. 1-8, 2015.
[4] H. Cho, S.-J. Kang, S. I. Cho and Y. H. Kim, "Image Segmentation Using Linked Mean-Shift Vectors and Its Implementation on GPU," IEEE, pp. 719-727, 2014.
[5] Darian M. Onchis, D. Frunzaverde, M. Gaianu and R. Ciubotariu, "Multi-phase identification in microstructures images using a GPU accelerated fuzzy c-means segmentation," IEEE, pp. 602-607, 2015.
[6] N. Aitali, B. Cherrad, O. Bouattane, M. Youssfi and A. Raihani, "New Fine-Grained Clustering Algorithm on GPU Architecture for Bias Field Correction and MRI Image Segmentation," IEEE, pp. 118-121, 2015.
[7] C. S. Cho and S. Lee, "Low-Complexity Topological Derivative-Based Segmentation," IEEE, pp. 734-741, 2015.
[8] J. Chalfoun, M. Majurski, T. Blattner, W. Keyrouzl, P. Bajcsy and M. Brady, "MIST: Microscopy Image Stitching Tool," IEEE, p. 1757, 2015.
[9] Arthur D. Costea and Sergiu Nedevschi , "Multi-Class Segmentation for Traffic Scenarios at Over 50 FPS," IEEE, pp. 1390-1395, 2014.
[10] X. Zhang, G. Tan and M. Chen, "A Reliable Distributed Convolutional Neural Network for Biology Image Segmentation," IEEE, pp. 777-780, 2015.
[11] Mohammed A. Shehab, , Mahmoud Al-Ayyoub and Yaser Jararweh, "Improving FCM and T2FCM Algorithms Performance using GPUs for Medical Images Segmentation," IEEE, pp. 130-135, 2015.
[12] Z. Liu, X. Li, P. Luo , C. C. Loy and X. Tang, "Semantic Image Segmentation via Deep Parsing Network," IEEE, pp. 1377-1785, 2015.
[13] Z. Yi, X. Hu, B. Jang and K. K. Kim, "A Robust and Parallel-Friendly Distance Image Based Hand Detection," IEEE, pp. 33-34, 2015.
[14] J. Sirotkovic, H. Dujmic and V. Papic , "Image segmentation based on complexity mining and mean-shift algorithm," IEEE, pp. 1-6, 2014.
[15] A.-R. Baek, K. Lee and H. Choi , "Speed-up Image Processing on Mobile CPU and GPU," IEEE, pp. 79-81, 2015.
[16] Z. Juhasz and G. Kozmann, "A GPU-based Simultaneous Real-Time EEG Processing and Visualization System for Brain Imaging Applications," IEEE, pp. 299-304, 2015.
[17] H. L. Khor, S.-C. Liew and Jasni Mohd. Zain , "A Review on Parallel Medical Image Processing on GPU," IEEE, pp. 45-48, 2015.
[18] A. Asaduzzaman, A. Martinez and A. Sepehri , "A Time-Efficient Image Processing Algorithm for Multicore/Manycore Parallel Computing," IEEE, pp. 1-5, 2015.
[19] A. Fabija and J. Gocławski, "New accelerated graph-based method of image segmentation applying minimum spanning tree," IET Image Processing, pp. 239-251, 2013.
[20] A. S. Baby and Balachandran K, "A Parallel Approach For Region-Growing Segmentation," IEEE, pp. 196-200, 2015.
[21] C. Cho and S. Lee, "Effective Five Directional Partial Derivatives-based Effective Five Directional Partial Derivatives-based," IEEE, pp. 1-9, 2015.
[22] K. Y. Lee, G. Kyung, T. R. Park, J. C. Kwak and Y. S. Koo, "A Design of a GP-GPU based Stream Processor for an Image Processing," IEEE, pp. 535-539, 2015.
[23] M. G. McGaffin and Jeffrey A. Fessler, "Edge-Preserving Image Denoising via Group Coordinate Descent on the GPU," IEEE, pp. 1273-1281, 2015.
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Published
2016-07-21
How to Cite
Kaur, G., & Jindal, S. (2016). A REVIEW ON IMAGE SEGMENTATION USING GPU. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(10), 7160–7163. https://doi.org/10.24297/ijct.v15i10.4502
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Research Articles
