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Carsten Rother among 9 most influential CV scholars in Europe
Carsten Rother was selected by aminer to be among the 9 most influential Computer Vision researchers in Europe. See: AI 2000 Computer Vision Most Influential Scholars. https://www.aminer.org/ai2000/cv
Continue readingOur excellent cluster “Structures” – where ML is used to find structures in data and the physical world – got funded by DFG
STRUCTURES: A unifying approach to emergent phenomena in the physical world, mathematics, and complex data.
Continue readingThree papers accepted to ACCV on: Geometric Image Synthesis; Deep Object Co-segmentation; 6D Object Pose Estimation
papers will soon appear on our publication page.
Continue readingLearning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
See our arxiv paper https://arxiv.org/abs/1508.04546
Continue readingCVPR 16 pose estimation code now online
You can download the code of our CVPR 16 paper on pose estimation and camera localization here.
Continue readingProseminar Aufgabenstellungen der Bildanalyse und Mustererkennung
Sommersemester 2015 Das Proseminar befasst sich in diesem Semester mit Object Recognition and Scene Understanding Welche Ideen gibt es dazu? Wie ist der mathematische / algorithmische Hintergrund? Kontakt TUD-email Holger.Heidrich, Raum 2039 Termin Freitag, 2. DS, 09:20-10:50 Uhr, APB E008 ACHTUNG: Das Proseminar wurde auf den Hauptseminartermin verlegt! Vortragstermine sind dort eingetragen. Montag, 4.DS, 13:00, […]
Continue readingCVLD Colloquium
This is a weekly event which will be held 3pm-4pm every Tuesday at room 2024, CVLD.20.12.2016Manuel Paternoster's diploma defence"Deep Convolutional Neural Networks for Object Coordinate Regression"12.12.2016(13:00-16:00)Ideas and Future PlansEverybody should present their thoughts in 10-15 minutes.20.09.2016Diploma thesis defense - Friedrich Trauzettel : "Scene Flow with Extended Feature Matching"09.08.2016Manuel Paternoster's intermediate diploma presentation: Deep Convolutional Neural […]
Continue readingEfficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation
See our arXiv paper http://arxiv.org/abs/1511.05067
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