Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

L. Minciullo, F. Manhardt, K. Yoshikawa, S. Meier, F. Tombari, N. Kobori
DB-GAN: Boosting Object Recognition Under Strong Lighting Conditions
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2021.
The first two authors contributed equally to this paper.

Driven by deep learning, object recognition has recently made a tremendous leap forward. Nonetheless, its accuracy often still suffers from several sources of variation that can be found in real-world images. Some of the most challenging variations are induced by changing lighting conditions. This paper presents a novel approach for tackling brightness variation in the domain of 2D object detection and 6D object pose estimation. Existing works aiming at improving robustness towards different lighting conditions are often grounded on classical computer vision contrast normalisation techniques or the acquisition of large amounts of annotated data in order to achieve invariance during training. While the former cannot generalise well to a wide range of illumination conditions, the latter is neither practical nor scalable. Hence, We propose the usage of Generative Adversarial Networks in order to learn how to normalise the illumination of an input image. Thereby, the generator is explicitly designed to normalise illumination in images so to enhance the object recognition performance. Extensive evaluations demonstrate that leveraging the generated data can significantly enhance the detection performance, outperforming all other state-of-the-art methods. We further constitute a natural extension focusing on white balance variations and introduce a new dataset for evaluation.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif