IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Image Segmentation Using Convolutional Neural Network

[Full Text]

 

AUTHOR(S)

Ravi Kaushik, Shailender Kumar

 

KEYWORDS

Computer Vision, Convolution Neural Networks, Deep Learning, Edge Detection Models, Fully Connected Layer, Image Segmentation, Max Pooling.

 

ABSTRACT

Identifying regions in an image and labeling them to different classes is called image segmentation. Automatic image segmentation has been one of the major research areas, which is in trend nowadays. Every other day a new model is being discovered to do better image segmentation for the task of computer vision. Computer vision is making human’s life easier by automating the tasks which humans used to do manually. In this survey we are comparing various image segmentation techniques and after comparing them with each other we have explained the merits and demerits of each technique in detail. Detailed analysis of each methodology is done on the basis of various parameters, which are used to provide a comparison among different methods discussed in our work. Our focus is on the techniques which can be optimized and made better than the one which are present before. This survey emphasizes on the importance of applications of image segmentation techniques and to make them more useful for the mankind in daily life. It will enable to us to take full benefits of this technology in monitoring of the time consuming repetitive activities occurring around, as doing such tasks manually can become cumbersome and also increases the possibility of errors.

 

REFERENCES

Olaf Ronneberger, Philipp Fischer and Thomas Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 234-241.
[2] Sadegh Karimpouli, Pejman Tahmasebi, Segmentation of digital rock images using deep Convolutional auto encoder networks, Computers & Geosciences, Volume 126, 2019,Pages 142-150,ISSN 0098-3004,https://doi.org/10.1016/j.cageo.2019.02.003.
[3] Xiaomeng Fu and Huiming Qu, “Semantic Segmentation of High-resolution Remote Sensing Image Based on Full Convolutional Neural Network”, 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE) DOI: 10.1109/ISAPE.2018.8634106
[4] Pim Moeskops, Jelmer M. WolterinkBas, H. M. van der Velden, Kenneth G. A. Gilhuijs, Tim Leiner, Max A. Viergever and Ivana Išgum, “Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities” MICCAI 2016: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 pp 478-486 047
[5] Zhuoling Li, Minghui Dong, Shiping Wen, Xiang Hu, Pan Zhou, Zhigang Zeng,CLU-CNNs: Object detection for medical images,Neurocomputing,Volume 350,2019,Pages 53-59,ISSN 0925-2312,
[6] Hai Huang, Hao Zhou, Xu Yang, LuZhang, LuQi and Ai-Yun Zang, “Faster R-CNN for marine organisms detection and recognition using data augmentation”, Neurocomputing, Volume 337, 14 April 2019, Pages 372-384
[7] Deniz CM, Xiang S, Hallyburton RS, Welbeck A, Babb JS, Honig S, Cho K, Chang G. Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks. Sci Rep. 2018 Nov 7;8(1):16485. doi: 10.1038/s41598-018-34817-6. PubMed PMID: 30405145; PubMed Central PMCID: PMC6220200.
[8] S. Ji, W. Xu, M. Yang and K. Yu, "3D Convolutional Neural Networks for Human Action Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221-231, Jan. 2013.doi: 10.1109/TPAMI.2012.59
[9] Ahmed Bassiouny , Motaz El-Saban, “Semantic segmentation as image representation for scene recognition”, 2014 IEEE International Conference on Image Processing (ICIP).
[10] M. L.S., V.K. G. (2011) Convolutional Neural Network Based Segmentation. In: Venugopal K.R., Patnaik L.M. (eds) Computer Networks and Intelligent Computing. ICIP 2011.Communications in Computer and Information Science, vol 157. Springer, Berlin, Heidelberg
[11] Ji Shunping & Chi, Zhang & Xu, Anjian & Shi, Yun & Duan, Yulin.(2018). “3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images" 10. 75. 10.3390/rs10010075.
[12] Florent Marie, Lisa Corbat, Yann Chaussy, Thibault Delavelle, Julien Henriet, Jean-Christophe Lapayre,Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network,Expert Systems with Applications,Volume 127,2019,Pages 282-294,ISSN 0957-4174
[13] Bullock, Joseph & Cuesta, Carolina &Quera-Bofarull, Arnau. (2018). XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets.
[14] Yan Song, Bo He, Peng Liu, Tianhong Yan, Side scan sonar image segmentation and synthesis based on extreme learning machine, Applied Acoustics, Volume 146, 2019, Pages 56-65, ISSN 0003-682X
[15] Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban, Simon Graham, David Epstein, Stella Pelengaris, Michael Khan, Nasir M. Rajpoot, Micro-Net: A unified model for segmentation of various objects in microscopy images, Medical Image Analysis, Volume 52, 2019, Pages 160-173,ISSN 1361-8415,
[16] Dan C Cirean, Alessandro Giusti, Luca M Gambardella and Schmidhuber, “Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images”, Advances in neural information processing systems 25 • January 2012
[17] Wang, Shengke& Liu, Lu & Qu, Liang & Yu, Changyin& Sun, Yujuan& Gao, Feng & Dong, Junyu. (2018). “Accurate Ulva Prolifera Regions Extraction of UAV Images with Superpixel and CNNs for Ocean Environment Monitoring” Neurocomputing. 10.1016/j.neucom.2018.06.088.
[18] Jie Chang, Luming Zhang, Naijie Gu and Xiaoci Zhang, “A Mix-pooling CNN Architecture with FCRF for Brain Tumor Segmentation”, Journal of Visual Communication and Image Representation 58 • December 2018, DOI: 10.1016/j.jvcir.2018.11.
[19] AyseBetulOktay, Anıl Gurses,Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images,Micron,Volume 120,2019,Pages 113-119,ISSN 0968-4328,
[20] Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, SébastienOurselin, Tom Vercauteren,Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,Neurocomputing,Volume 338,2019,Pages 34-45.