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Computation-Less, Bias-Free, and Ultra-Broadband Photodetector for On-Chip Edge Extraction. [PDF]

Adv Mater
Fu J, Jiang H, Liu X, Wang F, Peng Y, Wang QJ, Gao W, Wei X, Qiu CW.   +8 more
europepmc   +1 more source

Unmixing Convolutional Features for Crisp Edge Detection [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: feature mixing in edge classification and side mixing during fusing side predictions ...
Gui-Song Xia, Xianwei Zheng, Nan Xue
exaly   +5 more sources

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Edge feature analysis by a vectorized feature extractor and in multiple edges

Proceedings of 13th International Conference on Pattern Recognition, 1996
In this paper we propose a method to generalize a 1-D edge feature extractor to 2-D and clarifies its properties for the edges located closely each other (i.e., multiple edges). In a previous paper (1995) we stated necessity of a measure which can discriminate a clear edge with small edge height from a noisy edge with large edge height. Then we claimed
Takahiro Sugiyama, Keiichi Abe
openaire   +2 more sources

Edge feature analysis by a vectorized feature extractor for multiple edges

Systems and Computers in Japan, 1997
In this paper we propose a method to generalize a one-dimensional edge feature extractor to two dimensions and clarify its properties for edges close to each other (i.e., multiple edges). Previously we stated the necessity of a measure that can discriminate a clear edge with small edge height from a noisy edge with large edge height.
Takahiro Sugiyama, Keiichi Abe
openaire   +2 more sources

Edge detection in the feature space

Image and Vision Computing, 2011
To build a consistent image representation model which can process the non-Gaussian distribution data, a novel edge detection method (KPCA-SCF) based on the kernel method is proposed. KPCA-SCF combines kernel principal component analysis and kernel subspace classification proposed in this paper to extract edge features. KPCA-SCF was tested and compared
Zhengchun Lin, Jinshan Jiang, Zhiyan Wang   +2 more
openaire   +2 more sources