Abstract
With deep learning techniques, a revolution has taken place in the field of image processing and computer vision. The survey paper emphasizes the importance of representation learning methods for machine learning tasks. Deep learning, the modern machine learning is commonly used in the vision tasks—semantic segmentation, image captioning, object detection, recognition, and image classification. The paper focuses on the recent developments in the domain of remote sensing, retinal image understanding, and scene understanding based on newly proposed deep architectures. The author finds it quite intriguing of the classical building blocks of image segmentation (Gabor, K-means), shifting gear, and contributing to image recognition tasks based on deep learning (Gabor convolutional network, K-means dictionary learning). The survey makes an attempt to serve as a concise guide in providing latest works in computer vision applications based on deep learning and giving futuristic insights.
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References
Trémeau, A., Tominaga S., Plataniotis, K. N.: Color in image and video processing: Most recent trends and future research directions. Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing (2008).
Bengio, Y., Courville, A., Vincent P.: Representation Learning: A Review and New Perspectives: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 Issue 8, (2013), 1798–1828, https://doi.org/10.1109/TPAMI.2013.50.
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., and Seung, H. S. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Computation, 22, (2010). 511–538.
Goswami Tilottama, Agarwal Arun, Rao C.R.: Statistical Learning for Texture Characterization. ICVGIP (2014): 11:1–11:8.
Srinivas S, Sarvadevabhatla RK, Mopuri KR, Prabhu N, Kruthiventi SSS and Babu RV A Taxonomy of Deep Convolutional Neural Nets for Computer Vision. Front. Robot. AI 2:36. (2016), https://doi.org/10.3389/frobt.2015.00036A.
Nicolas A., Le Saux Bertrand; Lefèvre, Sébastien: Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images: Remote Sensing, Vol. 9 Issue 4, (2017), p 1–18.
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE T-MI 26(10), (2007), 1357–1365.
Kar Sudeshna S., Maity Santi P., Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy, Computer Methods and Programs in Biomedicine, Volume 133 Issue C, (2016), 111–132.
Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Andrés Arbeláez, Luc Van Gool: Deep Retinal Image Understanding, MICCAI (2016), https://doi.org/10.1007/978-3-319-46723-8_17.
Karpathy A. and Fei-Fei L,: Deep Visual-Semantic Alignments for Generating Image Descriptions, IEEE Trans. Pattern Anal. Mach. Intell 39(4), (2015), 664–676.
Hu Yao, Li Chuyi, Hu Dan, Yu Weiyu,: Gabor Feature Based Convolutional Neural Network for Object Recognition in Natural Scene, Information Science and Control Engineering ICISCE, (2016), https://doi.org/10.1109/ICISCE.2016.91.
Shangzhen Luan, Baochang Zhang, Chen Chen, Xianbin Cao, Jungong Han, Jianzhuang Liu: Gabor Convolutional Networks. CoRR abs/1705.01450 (2017).
Goswami Tilottama, Agarwal Arun, Rao C.R.: Hybrid Region and Edge Based Unsupervised Color-Texture Segmentation for Natural Images, International Journal of Information Processing, 9(1), (2015), 77–92, ISSN:0973-8215.
Jain, A. K. and F. Farrokhnia: Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12), (1991) 1167–1186.
Adam Coates and Andrew Y. Ng, Learning Feature Representations with K-means, G. Montavon, G. B. Orr, K.-R. Muller (Eds.), Neural Networks: Tricks of the Trade, 2nd edn, Springer LNCS 7700 (2012).
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Goswami, T. (2018). Impact of Deep Learning in Image Processing and Computer Vision. In: Anguera, J., Satapathy, S., Bhateja, V., Sunitha, K. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-10-7329-8_48
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DOI: https://doi.org/10.1007/978-981-10-7329-8_48
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