2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-1.3
Paper Title SCALE-INVARIANT SALIENT EDGE DETECTION
Authors Gang Hu, Conner Saeli, State University of New York @ Buffalo State, United States
SessionMLR-APPL-IP-1: Machine learning for image processing 1
LocationArea E
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we propose a Scale-Invariant Salient Edge Detection framework (SISED) using Hadamard-Product operation. Current HED-like edge detection approaches fuse multiple side outputs to produce the final edge map, which contains noise and unwanted edge details. Scale-invariant salience provides strong and convincing evidence for precisely describing object contour. The normalized Hadamard-Product is able to find and extract scale-invariant features by multiplying a set of side outputs. The computed Scale-Invariant Salient Edge (SISE) maps capture the hierarchical structure of contour details and can be utilized to improve the detection accuracy. The experimental results show SISED reaches state-of-the-art performance.