Motion Planning for USV using Classification Capabilities
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Bhanu, C. and Holben, R. D.: Model-based segmentation of FLIR images. IEEE Trans. Aerosp. Electron. Syst. 26, 2–10 (1990).
Ben-Yosef, N. Bahat, B. and FeiginBhanu, G. and Holben, C.: Simulation of IR images of natural backgrounds. Appl. Opt. 22, 190–193 (1983).
Ratches, J. A. Walters, C. P. Buser, R. G. and Guenther, B. D.:Aided and automatic target recognition based upon sensor inputs from image forming systems. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1004–1019 (1997).
Casasent, D. P. and Neiberg, L. M.:Classifier and shift-invariant automatic target recognition neural networks. Neural Networks. 8, 1117–1129 (1995).
Schachter, B. J. Lev, A. Zucker, S.W. and Rosenfeld, A.:An application of relaxation methods to edge reinforcement. IEEE Trans. Syst. Man Cybern. 7, 813–816 (1997).
Walters, D. K. W.:Computer vision model based on psychophysical experiments.Pattern Recognition by Humans and Machines, 2 (1986).
Broy, M.: Early vision. In: Rosenfeld,A.. (eds.) Perspectives in Computing, pp. 190-206. Academic Press, New York (1986).
Marham, K. C.:Comparison of segmentation processes for object acquisition in infrared scenes. IEEE Radar Signal Process. 136, 13–21 (1989).
Davies, E.R.:Machine Vision: Theory, Algorithms, Practicalities, 2nd ed., Academic Press, New York. (1997).
Marham, K. C.:Clutter metrics for target detection systems. IEEE Trans. Aerosp. Electron. Syst. 30, 81–91 (1994).
Vijaya, K.:A tutorial survey of composite filter designs for optical correlators. Appl. Opt. 31, 4773–4798 (1992).
Flannery, D. Loomis, J. and Milkovich, M.:Transform-ratio ternary phase-amplitude filter formation for improved correlation discrimination. Appl. Opt. 27, 4079–4083 (1988).
http://sites.google.com/site/orenusv/home/research-1/usvvision
Cybenko, George. : Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems. 2, 303–314, (1989).
Gundogdu, Erhan and Solmaz, Berkan : MARVEL: A Large-Scale Image Dataset for Maritime Vessels. Computer Vision – ACCV 2016. 4, 165–180, (2016).
Zhang, Mabel M and Choi, Jean and Daniilidis, Kostas and Wolf, Michael T and Kanan, Christopher. : Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2, 10–16 (2015).
Howard, Andrew G and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig : Mobilenets: Efficient convolutional neural networks for mobile vision applications. Mathematics of Control, Signals, and Systems. (2017).
Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali : You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2, 779–788, (2016).
Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua : Deep learning. MIT press Cambridge(2016).
Hinton, G and Sejnowski, T : A Theoretical Framework for Back-Propagation.
Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E : Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2, 1097–1105, (2012).
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