Loh, Yuen Peng and Chan, Chee Seng (2019) Getting to know low-light images with the Exclusively Dark dataset. Computer Vision and Image Understanding, 178. pp. 30-42. ISSN 1077-3142, DOI https://doi.org/10.1016/j.cviu.2018.10.010.
Full text not available from this repository.Abstract
Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset.
| Item Type: | Article |
|---|---|
| Funders: | Fundamental Research Grant Scheme: Ministry of Education Malaysia under Grant FRGS/1/2018/ICT02/UM/02/2, Postgraduate Research Fund: University of Malaya under Grant PG002-2016A, NVIDIA Corporation (United States) |
| Uncontrolled Keywords: | Light; Object detection; Object recognition |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Jan 2019 03:28 |
| Last Modified: | 11 Jan 2019 03:28 |
| URI: | http://eprints.um.edu.my/id/eprint/19951 |
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