Shi Wei Lo’s research while affiliated with Biotechnology High Performance Computing Software Applications Institute and other places

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Publications (3)


Flood Tracking in Severe Weather
  • Conference Paper

June 2014

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30 Reads

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5 Citations

Shi Wei Lo

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Jyh Horng Wu

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Severe weather conditions greatly impair the performance of outdoor imaging. In this study, two region-based image segmentation methods, Grow Cut and Region Growing (RegGro), were applied to rain scenes. This study demonstrates that segmentation accuracy depends on fog and rain stains. In severe rainfall periods, heavy rain and fog reduced the overall image quality, and both methods yielded segmentation failure. The results show that both region-based methods are effective for segmenting objects in images captured under poor weather conditions. Both methods have unique advantages and disadvantages for fog and stain conditions. The segmentation accuracy yielded by the Grow Cut and RegGrow methods was 75% and 85%, respectively.


Video Matching by One-Dimensional PSNR Profile
  • Article
  • Publisher preview available

December 2013

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8 Reads

This paper addresses a compact framework to matching video sequences through a PSNR-based profile. This simplify video profile is suitable to matching process when apply in disordered undersea videos. As opposed to using color and motion feature across the video sequence, we use the image quality of successive frames to be a feature of videos. We employ the PSNR quality feature to be a video profile rather than the complex contend-based analysis. The experimental results show that the proposed approach permits accurate of matching video. The performance is satisfactory on determine correct video from undersea dataset.

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Video Query Using Temporal Signature and Similarity Matching

January 2013

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10 Reads

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2 Citations

Large amount of video data is stored and distributed in wide variety of application. Due to the fast video material increases, manage and query of video become more and more important. In this paper, we address a temporal signature representation and similarity model to retrieval the similar video within database by video query. Experimental results on real date are presented. The experimental results show that the statistical approach permits accurate query of video clip, in particular, the performance of the approach was found extremely satisfactory with determine all similar video in database.

Citations (2)


... The resulting maps can then be used to monitor and detect areas with high risk of flooding. In [18], two methods of segmentation of images based on regions, Grow Cut and Growing Region, were applied to images that capture certain areas during severe weather. The authors demonstrated that the segmentation accuracy of the two methods varied quite widely in fog or rain conditions, the Growing Region method giving better results. ...

Reference:

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks
Flood Tracking in Severe Weather
  • Citing Conference Paper
  • June 2014

... In two SVM classifiers are having a sliding window, to distinguish cuts and continuous changes, individually. A few highlights from each casing, and afterward utilizes the SVM to characterize the edges utilizing these highlights into three classifications: cut, progressive change, and others [4,5]. ...

Video Query Using Temporal Signature and Similarity Matching