S
Stefan Winkler
Researcher at National University of Singapore
Publications - 180
Citations - 9297
Stefan Winkler is an academic researcher from National University of Singapore. The author has contributed to research in topics: Video quality & Sky. The author has an hindex of 41, co-authored 178 publications receiving 8049 citations. Previous affiliations of Stefan Winkler include Agency for Science, Technology and Research & École Normale Supérieure.
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Detecting Blurred Ground-based Sky/Cloud Images
TL;DR: In this paper, the authors proposed an efficient framework that can identify the blurred sky/cloud images using a static external marker, which has a detection accuracy of 94% using a single image.
Journal ArticleDOI
Generating near-infrared facial expression datasets with dimensional affect labels
Calvin Chen,Stefan Winkler +1 more
TL;DR: Two complementary data augmentation methods are presented to create NIR image datasets with dimensional emotion labels from existing categorical and/or visible-light datasets, and show that these generated NIR datasets are comparable to existing datasets in terms of data quality and baseline prediction performance.
Proceedings ArticleDOI
Investigating the generalizability of EEG-based cognitive load estimation across visualizations
Viral Parekh,Maneesh Bilalpur,C. V. Jawahar,Shravan Kumar,Stefan Winkler,Ramanathan Subramanian +5 more
TL;DR: In this article, the authors examine if EEG-based cognitive load estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the n-back task.
Journal ArticleDOI
A Data-Driven Approach for Accurate Rainfall Prediction
TL;DR: An optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction, which significantly reduces the false alarm rates.
Proceedings ArticleDOI
HelipadCat: Categorised Helipad Image Dataset and Detection Method
Jonas Bitoun,Stefan Winkler +1 more
TL;DR: In this article, a dataset of aerial images of helipads, together with a method to identify and locate such helicopter bases from the air, is presented, including a classification by visual helipad shape and features, which makes available to the research community.