Prof. Dr. Michal Haindl is a leading Czech researcher recognized internationally for his extensive contributions to pattern recognition, texture analysis, material appearance modeling, and computational imaging, with a prolific body of work spanning more than two decades and over 130 publications. As a pioneer in advanced texture modeling, he has developed influential methods such as Bidirectional Texture Function (BTF) models, multispectral and 3D causal random field texture representations, anisotropic BRDF modeling, and rotationally invariant textural features that have shaped modern approaches in computer vision. His research addresses fundamental challenges in texture fidelity, similarity criteria, segmentation, scale and illumination invariance, and material recognition, providing robust frameworks widely applied in remote sensing, biomedical imaging, cultural heritage restoration, forestry classification, and disease detection. Prof. Dr. Haindl has also significantly advanced unsupervised learning and benchmarking for image segmentation, contributing datasets, evaluation metrics, and criteria that have become reference standards in the field. His work on medical imaging—including mammogram enhancement, melanoma recognition, and disease survival modeling—reflects his interdisciplinary impact across health analytics and AI-driven diagnostic support. Additionally, he has contributed to computational methods for evaluating physical and rendered materials, transfer learning for texture models, and structural detection in archeology. Through sustained innovation, extensive collaborations, and consistent publication in high-impact journals and conferences, Prof. Dr. Michal Haindl has established himself as a foundational figure in texture-based pattern recognition and material appearance research, continuously driving forward the scientific understanding and practical applications of computational vision.
Profiles: Orcid | Scopus
Featured Publications
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Haindl, M., & Mikes, S. (2023). Optimal activation function for anisotropic BRDF modeling. Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 1–9.
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Mikes, S., & Haindl, M. (2022). Texture segmentation benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1–15.
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Vacha, P., & Haindl, M. (2023). Texture recognition under scale and illumination variations. Journal of Information and Telecommunication, 7(4), 1–14.
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Remes, V., & Haindl, M. (2019). Bark recognition using novel rotationally invariant multispectral textural features. Pattern Recognition Letters, 128, 1–8.
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Haindl, M. (2022). Bidirectional texture function modeling. In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging (pp. 1–15). Springer.