Ivica Kopriva | Pattern Recognition | Best Researcher Award

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Dr. Ivica Kopriva | Pattern Recognition | Best Researcher Award

Rudjer Boskovich Institute | Croatia

Dr. Ivica Kopriva is a distinguished Senior Scientist at the Ruđer Bošković Institute, Croatia, internationally recognized for his pioneering contributions to signal processing, machine learning, and blind source separation. Dr. Ivica Kopriva’s interdisciplinary research integrates statistical learning, low-rank sparse modeling, and nonlinear decomposition methods to address complex challenges in biomedical imaging, remote sensing, and hyperspectral data analysis. His highly influential publications, including “Multi-view Low-Rank Sparse Subspace Clustering” (Pattern Recognition, 2018) and “l₀-Motivated Low-Rank Sparse Subspace Clustering” (IEEE Transactions on Cybernetics, 2018), have significantly advanced subspace clustering and unsupervised learning, with the former ranked among the top 1% of highly cited papers in Engineering. Dr. Ivica Kopriva’s research further encompasses image co-segmentation, tumor detection, and signal demixing, contributing to innovations in medical imaging and AI-based diagnostics. With over 2,546 citations, an h-index of 21, and an i10-index of 50, Dr. Ivica Kopriva’s scholarly impact extends across multiple disciplines of computational and biomedical sciences. His exceptional achievements have been recognized through numerous awards, including the State Award of the Republic of Croatia for Scientific Achievement (2009), multiple Director’s Awards for Scientific Excellence (2010–2021), and MICCAI Outstanding Reviewer Awards. As a Senior Member of IEEE and OSA and an affiliated faculty member at Virginia Commonwealth University, Dr. Ivica Kopriva continues to contribute extensively to international scientific communities, shaping the global research landscape in computational imaging, data-driven signal analysis, and intelligent biomedical systems.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Brbić, M., & Kopriva, I. (2018). Multi-view low-rank sparse subspace clustering. Pattern Recognition, 73, 247–258.

  • Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning. Springer Berlin Heidelberg.

  • Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., & Chen, X. (2015). Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing, 24(12), 5854–5867.

  • Brbić, M., & Kopriva, I. (2020). l₀-Motivated low-rank sparse subspace clustering. IEEE Transactions on Cybernetics, 50(4), 1711–1725.

  • Tolić, D., Antulov-Fantulin, N., & Kopriva, I. (2018). A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recognition, 82, 40–55.

Jian Nong | Computer Vision and Image Processing | Best Researcher Award

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Prof. Dr. Jian Nong | Computer Vision and Image Processing | Best Researcher Award

Associate Professor at Wuzhou University | China

Prof. Dr. Jian Nong is a distinguished Associate Professor at the School of Artificial Intelligence, Wuzhou University, with expertise spanning computer vision, deep learning, and high-performance computing. He earned his Ph.D. in Computer Technology and Its Applications from the Macau University of Science and Technology, where he cultivated a strong research foundation in intelligent computing and visual information analysis. His academic pursuits center on visual object tracking, multi-modal data fusion, sentiment analysis, and GPU-based parallel processing. Prof. Dr. Nong has authored several influential papers in reputed international journals and conferences, including “Robust Tracking via Rethinking Prediction Head” (Image and Vision Computing, 2025), “Dual-stream Multi-modal Interactive Vision-language Tracking” (ACM, 2024), “SentiRank: A Novel Approach to Sentiment Leader Identification in Social Networks Based on the D-TFRank Model” (Electronics, 2025), and “Efficient Parallel Processing of R-Tree on GPUs” (Mathematics, 2024). His research outcomes contribute substantially to advancing intelligent vision systems, data-driven decision-making, and high-efficiency computing architectures. As the head of a research and teaching team supporting the Digital Xijiang River Project, he integrates academic research with applied innovation to address regional and industrial digitalization challenges. His ongoing research projects include the development of deep reinforcement learning-based recommendation methods for multi-objective optimization in complex shipping environments and object tracking algorithms leveraging multi-cue information. With over a decade of experience and more than ten scholarly publications, Prof. Dr. Jian Nong continues to play a pivotal role in bridging artificial intelligence theory and practical application, fostering the next generation of intelligent computing systems and contributing to the growth of AI-driven technologies on a global scale.

Profile: Orcid

Featured Publications

  1. Nong, J., Qi, Y., Mo, Z., Wang, J., & Liang, Y. (2025). Robust tracking via rethinking prediction head. Image and Vision Computing, 152, 105780.

  2. Huang, J., Lan, B., Nong, J., Pang, G., & Hao, F. (2025). SentiRank: A novel approach to sentiment leader identification in social networks based on the D-TFRank model. Electronics, 14(14), 2751.

  3. Mo, Z., Zhang, G., Nong, J., Zhong, B., & Li, Z. (2024, December 3). Dual-stream multi-modal interactive vision-language tracking. In Proceedings of the ACM Conference.

  4. Nong, J., He, X., Chen, J., & Liang, Y. (2024). Efficient parallel processing of R-Tree on GPUs. Mathematics, 12(13), 2115.