An improved building detection in complex sites using the LIDAR height variation and point density
- Authors: Siddiqui, Fasahat , Teng, Shyh , Lu, Guojun , Awrangjeb, Mohammad
- Date: 2013
- Type: Text , Conference proceedings
- Relation: 2013 28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013; Wellington; New Zealand; 27th-29th November 2013; published in International Conference Image and Vision Computing New Zealand p. 471-476
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- Description: In this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
An efficient video coding technique using a novel non-parametric background model
- Authors: Chakraborty, Subrata , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
- Date: 2014
- Type: Text , Conference proceedings
- Relation: 2014 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2014; Chengdu; China; 14th-18th July 2014 p. 1-6
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- Description: Video coding technique with a background frame, extracted from mixture of Gaussian (MoG) based background modeling, provides better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. However, it suffers from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we present a novel adaptive weighted non-parametric (WNP) background modeling technique and successfully embed it into HEVC video coding standard. Being non-parametric (NP), the proposed technique naturally exhibits superior performance in dynamic background scenarios compared to MoG-based technique without a priori knowledge of video data distribution. In addition, the WNP technique significantly reduces noise-related drawbacks of existing NP techniques to provide better quality video coding with much lower computation time as demonstrated through extensive comparative studies against NP, MoG and HEVC techniques.