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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

  • Pan, H., Zhou, Q., & Latecki, L. (2021). SGUNET: Semantic guided UNET for thyroid nodule segmentation. Proceedings - International Symposium on Biomedical Imaging, 2021-April, 630-634. doi: 10.1109/ISBI48211.2021.9434051.

  • 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.

  • Wu, X., Zhang, S., Zhou, Q., Yang, Z., Zhao, C., & Latecki, L. (2021). Entropy Minimization Versus Diversity Maximization for Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2021.3110109.

  • Qiang, Y., Zhou, Q., Shi, H., Jin, X., Ou, W., & Latecki, L. (2021). BASNet: Improving semantic segmentation via boundary-assistant symmetrical network. Proceedings of SPIE - The International Society for Optical Engineering, 11884. doi: 10.1117/12.2601128.

  • 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.

  • Jia, Q., Fan, X., Yu, M., Liu, Y., Wang, D., & Latecki, L. (2020). Coupling Deep Textural and Shape Features for Sketch Recognition. MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia, 421-429. doi: 10.1145/3394171.3413810.

  • Abouelaziz, I., Chetouani, A., Hassouni, M., Latecki, L., & Cherifi, H. (2020). Combination of Handcrafted and Deep Learning-Based Features for 3d Mesh Quality Assessment. Proceedings - International Conference on Image Processing, ICIP, 2020-October, 171-175. doi: 10.1109/ICIP40778.2020.9191092.

  • Li, S., Zhou, Q., Liu, J., Wang, J., Fan, Y., Wu, X., & Latecki, L. (2020). DCM: A Dense-Attention Context Module for Semantic Segmentation. Proceedings - International Conference on Image Processing, ICIP, 2020-October, 1431-1435. doi: 10.1109/ICIP40778.2020.9190675.

  • 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.

  • Abouelaziz, I., Chetouani, A., Hassouni, M.E., Latecki, L., & Cherifi, H. (2019). Mesh Visual Quality based on the combination of convolutional neural networks. 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019. doi: 10.1109/IPTA.2019.8936129.

  • Wang, Y., Zhou, Q., Liu, J., Xiong, J., Gao, G., Wu, X., & Latecki, L. (2019). Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation. Proceedings - International Conference on Image Processing, ICIP, 2019-September, 1860-1864. doi: 10.1109/ICIP.2019.8803154.

  • Bai, S., Tang, P., Torr, P., & Latecki, L. (2019). Re-ranking via metric fusion for object retrieval and person re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 740-749. doi: 10.1109/CVPR.2019.00083.

  • Bai, S., Bai, X., Tian, Q., & Latecki, L. (2019). Regularized diffusion process on bidirectional context for object retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5), 1213-1226. doi: 10.1109/TPAMI.2018.2828815.

  • Li, C., Wang, X., Liu, W., Latecki, L., Wang, B., & Huang, J. (2019). Weakly supervised mitosis detection in breast histopathology images using concentric loss. Medical Image Analysis, 53, 165-178. doi: 10.1016/j.media.2019.01.013.

  • Zhou, Q., Yang, W., Gao, G., Ou, W., Lu, H., Chen, J., & Latecki, L. (2019). Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web, 22(2), 555-570. doi: 10.1007/s11280-018-0556-3.

  • Fan, H., Chu, P., Latecki, L., & Ling, H. (2019). Scene parsing via dense recurrent neural networks with attentional selection. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 1816-1825. doi: 10.1109/WACV.2019.00198.

  • Zhu, Z., Rao, C., Bai, S., & Latecki, L. (2019). Training convolutional neural network from multi-domain contour images for 3D shape retrieval. Pattern Recognition Letters, 119, 41-48. doi: 10.1016/j.patrec.2017.08.028.

  • Hanif, S., Li, C., Alazzawe, A., & Latecki, L. (2019). Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11672 LNAI, 42-55. doi: 10.1007/978-3-030-29894-4_4.

  • Bai, S., Zhou, Z., Wang, J., Bai, X., Latecki, L., & Tian, Q. (2019). Automatic ensemble diffusion for 3d shape and image retrieval. IEEE Transactions on Image Processing, 28(1), 88-101. doi: 10.1109/TIP.2018.2863028.

  • Fan, Y., Lai, Y., Li, C., Li, N., Ma, Z., Zhou, J., Latecki, L., & Su, K. (2019). Efficient local search for minimum dominating sets in large graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11447 LNCS, 211-228. doi: 10.1007/978-3-030-18579-4_13.

  • Rao, C., Fan, Y., Su, K., & Latecki, L. (2019). Common object discovery as local search for maximum weight cliques in a global object similarity graph. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11414 LNCS, 219-233. doi: 10.1007/978-3-030-14085-4_18.

  • Abouelaziz, I., Chetouani, A., Hassouni, M., Latecki, L., & Cherifi, H. (2018). Convolutional neural network for blind mesh visual quality assessment using 3D visual saliency. Proceedings - International Conference on Image Processing, ICIP, 3533-3537. doi: 10.1109/ICIP.2018.8451763.

  • Yang, W., Zhou, Q., Lu, J., Wu, X., Zhang, S., & Latecki, L. (2018). Dense Deconvolutional Network for Semantic Segmentation. Proceedings - International Conference on Image Processing, ICIP, 1573-1577. doi: 10.1109/ICIP.2018.8451256.

  • Fan, Y., Ma, Z., Su, K., Li, C., Rao, C., Liu, R., & Latecki, L. (2018). Efficient local search for maximum weight cliques in large graphs. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2017-November, 1099-1104. doi: 10.1109/ICTAI.2017.00168.

  • Li, C., Wang, X., Liu, W., & Latecki, L. (2018). DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks. Medical Image Analysis, 45, 121-133. doi: 10.1016/j.media.2017.12.002.

  • Bai, S., Zhou, Z., Wang, J., Bai, X., Latecki, L., & Tian, Q. (2017). Ensemble Diffusion for Retrieval. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 774-783. doi: 10.1109/ICCV.2017.90.

  • Deng, Z. & Latecki, L. (2017). Amodal detection of 3D objects: Inferring 3D bounding boxes from 2D ones in RGB-depth images. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 398-406. doi: 10.1109/CVPR.2017.50.

  • Bai, S., Bai, X., Zhou, Z., Zhang, Z., Tian, Q., & Latecki, L. (2017). GIFT: Towards Scalable 3D Shape Retrieval. IEEE Transactions on Multimedia, 19(6), 1257-1271. doi: 10.1109/TMM.2017.2652071.

  • Zhou, Q., Zhang, C., Yu, W., Fan, Y., Zhu, H., Wu, X., Ou, W., Zhu, W., & Latecki, L. (2017). Face recognition via fast dense correspondence. Multimedia Tools and Applications, 1-19. doi: 10.1007/s11042-017-4569-1.

  • Li, N. & Latecki, L. (2017). Enhanced Affinity Inference Based Recommender Systems. Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016, 597-601. doi: 10.1109/WI.2016.0103.

  • Yang, W., Zhou, Q., Fan, Y., Gao, G., Wu, S., Ou, W., Lu, H., Cheng, J., & Latecki, L. (2017). Deep context convolutional neural networks for semantic segmentation. Communications in Computer and Information Science, 771, 696-704. doi: 10.1007/978-981-10-7299-4_58.

  • Fan, Y., Li, N., Li, C., Ma, Z., Latecki, L., & Su, K. (2017). Restart and random walk in local search for Maximum vertexweight cliques with evaluations in clustering aggregation. IJCAI International Joint Conference on Artificial Intelligence, 622-630. doi: 10.24963/ijcai.2017/87.

  • Li, N. & Latecki, L. (2017). Affinity learning for mixed data clustering. IJCAI International Joint Conference on Artificial Intelligence, 2173-2179. doi: 10.24963/ijcai.2017/302.

  • Bai, S., Bai, X., Tian, Q., & Latecki, L. (2017). Regularized diffusion process for visual retrieval. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 3967-3973.

  • Deng, Z., Todorovic, S., & Latecki, L.J. Unsupervised object region proposals for RGB-D indoor scenes. COMPUTER VISION and IMAGE UNDERSTANDING, 154, 127-136. 10.1016/j.cviu.2016.07.005

  • Bai, S., Bai, X., Latecki, L., & Tian, Q. (2017). Multidimensional scaling on multiple input distance matrices. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 1281-1287.