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Box Filter

The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform

Enhua Wu – 1st expert on this subject based on the ideXlab platform

  • Constant Time Weighted Median Filtering for Stereo Matching and Beyond
    2013 IEEE International Conference on Computer Vision, 2013
    Co-Authors: Kaiming He, Enhua Wu

    Abstract:

    Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median Filtering for disparity refinement. We discover that with this refinement, even the simple Box Filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median Filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median Filter. This makes the simple combination “Box aggregation + weighted median” an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median Filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.

Jiyeon Choi – 2nd expert on this subject based on the ideXlab platform

  • Selection of cost-effective Green Stormwater Infrastructure (GSI) applicable in highly impervious urban catchments
    KSCE Journal of Civil Engineering, 2018
    Co-Authors: Jiyeon Choi, Marla C. Maniquiz-redillas, Jungsun Hong

    Abstract:

    Urban areas such as roads, and parking lots, present different sets of problem, vehicular Nonpoint source (NPS) loadings derived from vehicular use are very high, while the roof have low NPS pollutant loadings. Therefore, this study was conducted to monitor the actual stormwater runoff characteristics of various landuse types and the treatment efficiency of the constructed GSI systems applied. The data used to calculate the pollutant concentrations were gathered from a total of 172 storm events during the five year monitoring period on a paved road, parking lot and roof landuse site. Based on the results, the road and parking lot landuses have characteristics of large amount of stormwater runoff, high peak flow and runoff of high pollutant concentration due to the vehicular activities. Applicable facilities include pretreatment facilities, such as infiltration trench, subsurface flow (SSF) and hybrid constructed wetland and tree Box Filter which have SA/CA ratios within 1∼2% were appropriate for facilities effective for reducing pollutants including infiltration and filtration functions. Meanwhile, the roof landuse contains low pollutants in comparison to other land uses, so, bioretention, rain garden, and free water surface (FWS) constructed wetland which have SA/CA ratio within 5% were appropriate to enable processing and recycling of large amount of stormwater runoff that have infiltration and retention function. Therefore, costeffective GSI design must not only depend on treated runoff quantity but also quality of the treated runoff for landuse.

  • development of tree Box Filter lid system for treating road runoff
    Journal of Wetlands Research, 2013
    Co-Authors: Jiyeon Choi

    Abstract:

    Abstract The aim of this study was to develop a tree Box Filter system, an example of Low Impact Development technology, for treating stormwater runoff from road. Monitoring of storm events was performed between June 2011 and November 2012 to evaluate the system performance during wet day. Based on the results, all runoff volume generated by rainfall less than 2 mm was stored in the system. The minimum volume reduction of 20% was observed in the system for rainfall greater than 20 mm. The greatest removal efficiency was exhibited by the system for total heavy metals ranging from 70 to 73% while satisfactory removal efficiency was exhibited by the system for particulate matters, organic matters and nutrients ranging from 60 to 68%. The system showed greater pollutant removal efficiency of 67 to 83% for rainfall less than 10 mm compared to rainfall greater than 10 mm which has 39 to 75% pollutant removal efficiency. The system exhibited less pollutant reduction for rainfall greater than 10 mm due to the decreased retention capacity of the system for increased rainfall. Overall, the system has proved to be an option for stormwater management that can be recommended for on-site application. Similar system may be designed based on several factors such as rainfall depth, facility size and pollutant removal efficiency.

Kaiming He – 3rd expert on this subject based on the ideXlab platform

  • Constant Time Weighted Median Filtering for Stereo Matching and Beyond
    2013 IEEE International Conference on Computer Vision, 2013
    Co-Authors: Kaiming He, Enhua Wu

    Abstract:

    Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median Filtering for disparity refinement. We discover that with this refinement, even the simple Box Filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median Filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median Filter. This makes the simple combination “Box aggregation + weighted median” an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median Filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.