Longin Jan Latecki

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Longin Jan Latecki

  • College of Science and Technology

    • Computer and Information Sciences

      • Professor

Biography

My research goal: development of intelligent systems with the focus on cognitively motivated geometric processing of spatial information obtained from visual input (e.g., cameras and Kinect like sensors)

Courses Taught

Number

Name

Level

CIS 2166

Mathematical Concepts in Computing II

Undergraduate

Selected Publications

Recent

  • Wu, X., Zhang, S., Zhou, Q., Yang, Z., Zhao, C., & Latecki, L.J. (2023). Entropy Minimization Versus Diversity Maximization for Domain Adaptation. IEEE Trans Neural Netw Learn Syst, 34(6), 2896-2907. United States. 10.1109/TNNLS.2021.3110109

  • Pedronette, D. & Latecki, L. (2021). Rank-based self-training for graph convolutional networks. Information Processing and Management, 58(2). doi: 10.1016/j.ipm.2020.102443.

  • Abouelaziz, I., Chetouani, A., Hassouni, M., Cherifi, H., & Latecki, L. (2021). Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment. IEEE Access, 9, 108200-108211. doi: 10.1109/ACCESS.2021.3094663.

  • Abouelaziz, I., Chetouani, A., Hassouni, M.E., Latecki, L., & Cherifi, H. (2020). 3D visual saliency and convolutional neural network for blind mesh quality assessment. Neural Computing and Applications, 32(21), 16589-16603. doi: 10.1007/s00521-019-04521-1.

  • Zhou, Q., Wang, Y., Fan, Y., Wu, X., Zhang, S., Kang, B., & Latecki, L. (2020). AGLNet: Towards real-time semantic segmentation of self-driving images via attention-guided lightweight network. Applied Soft Computing Journal, 96. doi: 10.1016/j.asoc.2020.106682.

  • Zhou, Q., Cheng, J., Lu, H., Fan, Y., Zhang, S., Wu, X., Zheng, B., Ou, W., & Latecki, L. (2020). Learning adaptive contrast combinations for visual saliency detection. Multimedia Tools and Applications, 79(21-22), 14419-14447. doi: 10.1007/s11042-018-6770-2.

  • Abouelaziz, I., Chetouani, A., Hassouni, M.E., Latecki, L., & Cherifi, H. (2020). No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling. Pattern Recognition, 100. doi: 10.1016/j.patcog.2019.107174.