The Experts below are selected from a list of 1374 Experts worldwide ranked by ideXlab platform
Yehching Chung - One of the best experts on this subject based on the ideXlab platform.
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Efficient compositing methods for the sort-lastsparse parallel volume rendering system on distributed memory multicomputers, The
2015Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with Bounding Rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with Bounding Rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with Bounding Rectangle and run-length encoding method has the best performance among the four methods. 1
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efficient compositing methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
The Journal of Supercomputing, 2001Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, one can achieve a very good performance improvement in the rendering phase by increasing the number of processors. This is because each processor can render images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors exceeds a threshold, the image compositing time becomes a bottleneck. In this paper, we propose three compositing methods to efficiently reduce the compositing time in parallel volume rendering. They are the binary-swap with Bounding Rectangle (BSBR) method, the binary-swap with run-length encoding and static load-balancing (BSLC) method, and the binary-swap with Bounding Rectangle and run-length encoding (BSBRC) method. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the BSBRC method has the best performance among these four methods.
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efficient compositing methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
International Conference on Parallel Processing, 1999Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with Bounding Rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with Bounding Rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with Bounding Rectangle and run-length encoding method has the best performance among the four methods.
Donlin Yang - One of the best experts on this subject based on the ideXlab platform.
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Efficient compositing methods for the sort-lastsparse parallel volume rendering system on distributed memory multicomputers, The
2015Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with Bounding Rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with Bounding Rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with Bounding Rectangle and run-length encoding method has the best performance among the four methods. 1
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Efficient compositing methods for the sort-last-sparse parallelvolume rendering system on distributed memory multicomputers (Conference)
Institute of Electrical and Electronics Engineers, 2012Co-Authors: Donlin YangAbstract:[[abstract]]In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with Bounding Rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with Bounding Rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with Bounding Rectangle and run-length encoding method has the best performance among the four methods[[fileno]]2030220030040[[department]]資訊工程學
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efficient compositing methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
The Journal of Supercomputing, 2001Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, one can achieve a very good performance improvement in the rendering phase by increasing the number of processors. This is because each processor can render images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors exceeds a threshold, the image compositing time becomes a bottleneck. In this paper, we propose three compositing methods to efficiently reduce the compositing time in parallel volume rendering. They are the binary-swap with Bounding Rectangle (BSBR) method, the binary-swap with run-length encoding and static load-balancing (BSLC) method, and the binary-swap with Bounding Rectangle and run-length encoding (BSBRC) method. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the BSBRC method has the best performance among these four methods.
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efficient compositing methods for the sort last sparse parallel volume rendering system on distributed memory multicomputers
International Conference on Parallel Processing, 1999Co-Authors: Donlin Yang, Yehching ChungAbstract:In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with Bounding Rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with Bounding Rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with Bounding Rectangle and run-length encoding method has the best performance among the four methods.
Ayman Habib - One of the best experts on this subject based on the ideXlab platform.
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automatic representation and reconstruction of dbm from lidar data using recursive minimum Bounding Rectangle
Isprs Journal of Photogrammetry and Remote Sensing, 2014Co-Authors: Eunju Kwak, Ayman HabibAbstract:Abstract Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of Rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of Rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data.
Taiichi Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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kiwifruit recognition at nighttime using artificial lighting based on machine vision
International Journal of Agricultural and Biological Engineering, 2015Co-Authors: Fu Longsheng, Yoshinori Gejima, Su Shuai, Cui Yongjie, Taiichi KobayashiAbstract:Most researches involved so far in kiwifruit harvesting robot suggest the scenario of harvesting in daytime for taking advantage of sunlight. A robot operating at nighttime can overcome the problem of low work efficiency and would help to minimize fruit damage. In addition, artificial lights can be used to ensure constant illumination instead of the variable natural sunlight for image capturing. This paper aims to study the kiwifruit recognition at nighttime using artificial lighting based on machine vision. Firstly, an RGB camera was placed underneath the canopy so that clusters of kiwifruits could be included in the images. Next, the images were segmented using an R-G color model. Finally, a group of image processing conventional methods, such as Canny operator were applied to detect the fruits. The image processing results showed that this capturing method could reduce the background noise and overcome any target overlapping. The experimental results showed that the optimal artificial lighting ranged approximately between 30-50 lx. The developed algorithm detected 88.3% of the fruits successfully. Keywords: Elliptic Hough transform, image capturing method, Kiwifruit, minimal Bounding Rectangle, optimal illumination intensity DOI: 10.3965/j.ijabe.20150804.1576 Citation: Fu L S, Wang B, Cui Y J, Su S, Gejima Y, Kobayashi T. Kiwifruit recognition at nighttime using artificial lighting based on machine vision. Int J Agric & Biol Eng, 2015; 8(4): 52－59.
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kiwifruit recognition at nighttime using artificial lighting based on machine vision
International Journal of Agricultural and Biological Engineering, 2015Co-Authors: Fu Longsheng, Yoshinori Gejima, Su Shuai, Cui Yongjie, Taiichi KobayashiAbstract:Most researches involved so far in kiwifruit harvesting robot suggest the scenario of harvesting in daytime for taking advantage of sunlight. A robot operating at nighttime can overcome the problem of low work efficiency and would help to minimize fruit damage. In addition, artificial lights can be used to ensure constant illumination instead of the variable natural sunlight for image capturing. This paper aims to study the kiwifruit recognition at nighttime using artificial lighting based on machine vision. Firstly, an RGB camera was placed underneath the canopy so that clusters of kiwifruits could be included in the images. Next, the images were segmented using an R-G color model. Finally, a group of image processing conventional methods, such as Canny operator were applied to detect the fruits. The image processing results showed that this capturing method could reduce the background noise and overcome any target overlapping. The experimental results showed that the optimal artificial lighting ranged approximately between 30-50 lx. The developed algorithm detected 88.3% of the fruits successfully. Keywords: Elliptic Hough transform, image capturing method, Kiwifruit, minimal Bounding Rectangle, optimal illumination intensity DOI: 10.3965/j.ijabe.20150804.1576 Citation: Fu L S, Wang B, Cui Y J, Su S, Gejima Y, Kobayashi T. Kiwifruit recognition at nighttime using artificial lighting based on machine vision. Int J Agric & Biol Eng, 2015; 8(4): 52－59.
Eunju Kwak - One of the best experts on this subject based on the ideXlab platform.
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automatic representation and reconstruction of dbm from lidar data using recursive minimum Bounding Rectangle
Isprs Journal of Photogrammetry and Remote Sensing, 2014Co-Authors: Eunju Kwak, Ayman HabibAbstract:Abstract Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of Rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of Rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data.