Markus Enzweiler Dissertation Sample; Markus Enzweiler Dissertation Outline

Markus Enzweiler's 40 research works with 6,637 citations and 7,075 reads, including: CNN-based synthesis of realistic high-resolution LiDAR data.

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All Conference and Workshop Publications. C. B. Rist, M. Enzweiler, and D. M. Gavrila. Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation. IEEE.

Markus Enzweiler received the MSc degree in computer science from the University of Ulm, Germany, in 2005. In 2002 and 2003 he was a visiting student researcher at the Centre for Vision Research at York University, Toronto, Canada. He received the PhD degree in computer science from the University of Heidelberg, Germany in 2011. Since 2010, he has been working with Mercedes-Benz Research.

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By Markus Enzweiler. Abstract. This thesis addresses the problem of recognizing pedestrians in video images acquired from a moving camera in real-world cluttered environments. Instead of focusing on the development of novel feature primitives or pattern classifiers, we follow an orthogonal direction and develop feature- and classifier-independent compound techniques which integrate.

Hello Mr. Enzweiler. Please introduce yourself briefly to the readers. My name is Markus Enzweiler and I am currently working on environment perception for self-driving cars in Stuttgart-Vaihingen in a joint effort between Daimler and Bosch. I am a computer scientist and I have studied in Bonn, Toronto, Ulm, and Heidelberg, from where I graduated with a PhD degree in 2011. During my studies I.

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The IEEE ITS Best Dissertation Award is given annually for the best dissertation in any ITS area that is innovative and relevant to practice. This award is established to encourage doctoral research that combines theory and practice, makes in-depth technical contributions, or is interdisciplinary in nature, having the potential to contribute to the ITSS and broaden the ITS topic areas from.

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Ramos, Timo Rehfeld, Markus Enzweiler, Ro-drigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The Cityscapes dataset for seman-tic urban scene understanding. In CVPR, 2016. (3)Jonathan Long, Evan Shelhamer, and Trevor Dar-rell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. (4)Daniel Zoran, Phillip Isola, Dilip.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification.

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