ORCID Profile
0000-0003-2046-3363
Current Organisation
University of Oxford
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 17-12-2019
DOI: 10.1007/S11263-019-01280-3
Abstract: Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50 K correspondences are processed. This has important implications for real-time applications. What’s more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement.
Publisher: Springer Science and Business Media LLC
Date: 29-11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: IEEE
Date: 05-2020
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Image segmentation has been explored for many years and still remains a crucial vision problem. Some efficient or accurate segmentation algorithms have been widely used in many vision applications. However, it is difficult to design a both efficient and accurate image segmenter. In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation. Starting with the SLIC superpixels, we train a fully convolutional network to learn the feature embedding space for each superpixel. The learned feature embedding corresponds to a similarity measure that measures the similarity between two adjacent superpixels. With the deep similarities, we can directly merge the superpixels into large segments. The evaluation results on BSDS500 and PASCAL Context demonstrate that our approach achieves a good trade-off between efficiency and effectiveness. Specifically, our DEL algorithm can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 07-2017
Publisher: Springer Science and Business Media LLC
Date: 18-06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jiawang Bian.