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Deep Learning Related Projects

B. Zha, M. T. Koroglu and A. Yilmaz. Trajectory Mining for Localization using Recurrent Neural Network. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, 2019, pp. 1-4.

J. Wei, M. T. Koroglu, B. Zha and A. Yilmaz. Pedestrian localization on topological maps with neural machine translation network. 2019 IEEE SENSORS, Montreal,Canada, 2019, pp. 1-4.

M. T. Koroglu, A. Yilmaz and C. J. Saul, "A Deep Learning Strategy for Stride Detection," 2018 IEEE SENSORS, New Delhi, 2018, pp. 1-4.

J. Wan, A. Yilmaz and L. Yan. October 2019. PPD: Pyramid Patch Descriptor via Convolutional Neural Network. Photogrammetric Engineering and Remote Sensing. Volume 85, Number 9, September 2019, pp. 673-686. DOI: 10.14358/PERS.85.9.673


J. Wan, A. Yilmaz, L. Yan. December 2018. DCF-BoW: Build Match Graph Using Bag of Deep Convolutional Features for Structure from Motion. IEEE Geoscience and Remote Sensing Letters. Vol. 15, No: 12, pp. 1847-1852. DOI: 10.1109/LGRS.2018.2864116


J. Wan and A. Yilmaz. June 2018. DCF: A method for creating image relation table using a deep convolutional network. Acta Geodaetica et Cartographica Sinica (AGCS) Machine Vision Special Issue. Vol. 47. No 6. pp. 882-891.

N. Gard and A. Yilmaz. April 2019. A Spacetime Model for One-shot Active Contour Extraction Scheme for Human Detection in Image Sequences. Elsevier Journal of Applied Mathematics and Computation 

Z. Koppanyi, D. Iwaszczuk, B. Zha, C. Saul, C. Toth and A. Yilmaz. August 2019. Multi-Modal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks. In Multi-Modal Scene Understanding. Edited by Bodo, M. Yang and Vittorio. Elsevier. ISBN: 9780128173589

D. Iwaszczuk, Z. Koppanyi, N. A. Gard, B. Zha, C. Toth, A. Yilmaz. 10/2018. Semantic Labeling of Structural Elements in Buildings by Fusing RGB and Depth Images in an Encoder-Decoder CNN Framework. ISPRS TCI Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications. Karlsruhe, Germany


J.H. Lee, A. Yilmaz, R. Denning, T. Aldemir. November 2018. Use of dynamic event trees and deep learning for real-time emergency planning in power plant operation. Nuclear Technology Journal, Special Issue on Big Data Analytics for Nuclear Power. DOI: 10.1080/00295450.2018.1541394 

B. Zha, A. Yilmaz, T. Aldemir. 11/2019. Off-site Dose Prediction for Decision Making Using Recurrent Neural. ANS Winter Meeting. Washington DC.

J. Lee, T. Aldemir, A. Yilmaz and R. Denning. 09/2018. Development of an Online Operator Tool to Support Real-Time Emergency Planning Based on the Use of Dynamic Event Trees and Deep Learning. Probabilistic Safety Assessment and Management (PSAM). Los Angeles California.

O Nina, W Garcia, S Clouse, A Yilmaz. 2018. MTLE: A Multitask Learning Encoder of Visual Feature Representations for Video and Movie Description. arXiv preprint arXiv:1809.07257

O. Nina, W. Garcia, S. Clause and A. Yilmaz. 12/ 2018. A Multitask Learning Encoder-Decoders Framework for Generating Movie and Video Captioning. Neural Information Processing Systems Workshop on AI for Social Good. Montreal, Canada.


B. Zha. M. Bai, H. Sezen and A. Yilmaz. 09/2019. Deep Convolutional Neural Networks For Classification In Comprehensive Structural Health Monitoring. 12th International Workshop on Structural Health Monitoring. San Jose, CA.

C. Xiao and A. Yilmaz, "A unique target representation and voting mechanism for visual tracking," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017, pp. 410-414.


Xiao and A. Yilmaz. 06/2017. Visual Tracking Utilizing Object Concept from Deep Learning Network. ISPRS Annals of Photogrammetry and Remote Sensing Spatial Information Science, IV-1-W1, 125-132, Hannover, Germany

Gard, N. A., Chen, J., Tang, P., and Yilmaz, A.: DEEP LEARNING AND ANTHROPOMETRIC PLANE BASED WORKFLOW MONITORING BY DETECTING AND TRACKING WORKERS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 149-154,, 2018.

J. Hu, W. Song, W. Zhang, Y. Zhao, and A. Yilmaz, “Deep learning for use in lumber classification tasks,” Wood Science and Technology, vol. 53, pp. 505–517, Mar 2019.