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Shape Reconstruction by Learning Differentiable Surface Representations.

, , , , and . CVPR, page 4715-4724. Computer Vision Foundation / IEEE, (2020)

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Good features to correlate for visual tracking ; Korelasyon ile görsel takip için iyi öznitelikler.. Middle East Technical University, Çankaya, Ankara, Turkey, (2017)base-search.net (ftmetuankair:oai:https://open.metu.edu.tr:11511/26941).Shape Reconstruction by Learning Differentiable Surface Representations., , , , and . CVPR, page 4715-4724. Computer Vision Foundation / IEEE, (2020)Multiple model adaptive visual tracking with correlation filters., and . ICIP, page 661-665. IEEE, (2015)Nestle: Interest point extraction via nested circles., and . SIU, page 1-4. IEEE, (2012)Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance., , , , , and . CVPR Workshops, page 1-9. IEEE Computer Society, (2015)Digital computation of fractional Fourier and linear canonical transforms and sparse image representation., , , , and . APSIPA, page 111-117. IEEE, (2017)GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping., , , , , and . ICCV, page 8738-8747. IEEE, (2019)Object classification in infrared images using deep representations., , and . ICIP, page 1066-1070. IEEE, (2016)Ensemble Of adaptive correlation filters for robust visual tracking., , and . AVSS, page 15-22. IEEE Computer Society, (2016)The Sixth Visual Object Tracking VOT2018 Challenge Results., , , , , , , , , and 145 other author(s). ECCV Workshops (1), volume 11129 of Lecture Notes in Computer Science, page 3-53. Springer, (2018)