EAGER: Edge-Aided imaGe undERstanding System
Pages 408 - 412
Abstract
Image understanding is a fundamental task for many multimedia and computer vision applications, such as self-driving, multimedia retrieval, and augmented reality, etc. In this paper, we demonstrate that edge detection could aid image understanding tasks such as semantic segmentation, optical flow estimation, and object proposal generation. Based on our recent research efforts on edge detection, we develop a robust and efficient Edge-Aided imaGe undERstanding system named as EAGER. EAGER is built on a compact and efficient edge detection module, which is constructed with a bi-directional cascade network, multi-scale feature enhancement, and layer-specific training supervision, respectively. Based on detected edges, EAGER achieves accurate semantic segment, optical flow estimation, as well as object bounding-box proposal generation for user-uploaded images and videos.
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Index Terms
- EAGER: Edge-Aided imaGe undERstanding System
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Information & Contributors
Information
Published In
June 2019
427 pages
ISBN:9781450367653
DOI:10.1145/3323873
- General Chairs:
- Abdulmotaleb El Saddik,
- Alberto Del Bimbo,
- Zhongfei Zhang,
- Program Chairs:
- Alexander Hauptmann,
- K. Selcuk Candan,
- Marco Bertini,
- Lexing Xie,
- Xiao-Yong Wei
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Published: 05 June 2019
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