The Experts below are selected from a list of 975 Experts worldwide ranked by ideXlab platform
Silvio Savarese - One of the best experts on this subject based on the ideXlab platform.
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Mobile object detection through client-Server based vote transfer
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012Co-Authors: Shyam Sunder Kumar, Silvio SavareseAbstract:Mobile platforms such as smart-phones and tablet computers have attained the technological capacity to perform tasks beyond their intended purposes. The steady increase of processing power has enticed researchers to attempt increasingly challenging tasks on mobile devices with appropriate modifications over their stationary counterparts. In this work we present a novel multi-frame object detection application for the mobile platform that is capable of object localization. Our work leverages the hough forest based object detector introduced by Gall et al. in [10]. In our experiments, we demonstrate that our novel, multi-frame generalization of [10] notably improves the detection performance. We test the performance of the technique in variable resolutions, the applicability to several object categories and different datasets. We implement the multi-frame detector on a mobile platform through a novel client-Server Framework that presents a sound and viable environment for the multi-frame detector. Finally, we study implementations of both single and multi-frame object detectors based on this client-Server Framework on a mobile device running the android OS.
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CVPR - Mobile object detection through client-Server based vote transfer
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012Co-Authors: Shyam Sunder Kumar, Silvio SavareseAbstract:Mobile platforms such as smart-phones and tablet computers have attained the technological capacity to perform tasks beyond their intended purposes. The steady increase of processing power has enticed researchers to attempt increasingly challenging tasks on mobile devices with appropriate modifications over their stationary counterparts. In this work we present a novel multi-frame object detection application for the mobile platform that is capable of object localization. Our work leverages the hough forest based object detector introduced by Gall et al. in [10]. In our experiments, we demonstrate that our novel, multi-frame generalization of [10] notably improves the detection performance. We test the performance of the technique in variable resolutions, the applicability to several object categories and different datasets. We implement the multi-frame detector on a mobile platform through a novel client-Server Framework that presents a sound and viable environment for the multi-frame detector. Finally, we study implementations of both single and multi-frame object detectors based on this client-Server Framework on a mobile device running the android OS.
Huiru Peng - One of the best experts on this subject based on the ideXlab platform.
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snphub an easy to set up web Server Framework for exploring large scale genomic variation data in the post genomic era with applications in wheat
GigaScience, 2020Co-Authors: Wenxi Wang, Zihao Wang, Xintong Li, Zhongfu Ni, Zhaorong Hu, Huiru PengAbstract:BACKGROUND The cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used Framework for setting up user-friendly web Servers to explore new genomic variation data in diverse species. RESULTS Here, we present SnpHub, a Shiny/R-based Server Framework for retrieving, analysing, and visualizing large-scale genomic variation data that can be easily set up on any Linux Server. After a pre-building process based on the provided VCF files and genome annotation files, the local Server allows users to interactively access single-nucleotide polymorphisms and small insertions/deletions with annotation information by locus or gene and to define sample sets through a web page. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by detected sequence variations. CONCLUSIONS SnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in recent studies. SnpHub and its tutorial are available at http://guoweilong.github.io/SnpHub/. The wheat-SnpHub-portal website can be accessed at http://wheat.cau.edu.cn/Wheat_SnpHub_Portal/.
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snphub an easy to set up web Server Framework for exploring large scale genomic variation data in the post genomic era with applications in wheat
bioRxiv, 2020Co-Authors: Wenxi Wang, Zihao Wang, Xintong Li, Zhongfu Ni, Zhaorong Hu, Huiru PengAbstract:Background: The cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used Framework for setting up user-friendly web Servers for exploring new genomic variation data in diverse species. Results: Here, we present SnpHub, a Shiny/R-based Server Framework for retrieving, analysing and visualizing the large-scale genomic variation data that be easily set up on any Linux Server. After a pre-building process based on the provided VCF files and genome annotation files, the local Server allows users to interactively access SNPs/INDELs and annotation information by locus or gene and for user-defined sample sets through a webpage. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by SNPs/INDELs. Conclusions: SnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in present studies. SnpHub and its tutorial are available as http://guoweilong.github.io/SnpHub/.
Patrick A Kupelian - One of the best experts on this subject based on the ideXlab platform.
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a client Server Framework for 3d remote visualization of radiotherapy treatment space
Frontiers in Oncology, 2013Co-Authors: Anand P Santhanam, Patrick A KupelianAbstract:Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While patient positioning is conducted by trained radiation therapists, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a Framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi 3D-camera Framework was used for acquiring the 3D treatment space. A client Server Framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the Server side to enable hardware flexibility on the client side. On the Server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi GPU system. The rendered 3D images were then encoded using a GPU based H.264 encoding for streaming. Results showed that for a stereo-image size of 1280x960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mega bits per second (Mbps) network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments.
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A client–Server Framework for 3D remote visualization of radiotherapy treatment space
Frontiers in Oncology, 2013Co-Authors: Anand P Santhanam, Patrick A KupelianAbstract:Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While patient positioning is conducted by trained radiation therapists, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a Framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi 3D-camera Framework was used for acquiring the 3D treatment space. A client Server Framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the Server side to enable hardware flexibility on the client side. On the Server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi GPU system. The rendered 3D images were then encoded using a GPU based H.264 encoding for streaming. Results showed that for a stereo-image size of 1280x960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mega bits per second (Mbps) network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments.
Shyam Sunder Kumar - One of the best experts on this subject based on the ideXlab platform.
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Mobile object detection through client-Server based vote transfer
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012Co-Authors: Shyam Sunder Kumar, Silvio SavareseAbstract:Mobile platforms such as smart-phones and tablet computers have attained the technological capacity to perform tasks beyond their intended purposes. The steady increase of processing power has enticed researchers to attempt increasingly challenging tasks on mobile devices with appropriate modifications over their stationary counterparts. In this work we present a novel multi-frame object detection application for the mobile platform that is capable of object localization. Our work leverages the hough forest based object detector introduced by Gall et al. in [10]. In our experiments, we demonstrate that our novel, multi-frame generalization of [10] notably improves the detection performance. We test the performance of the technique in variable resolutions, the applicability to several object categories and different datasets. We implement the multi-frame detector on a mobile platform through a novel client-Server Framework that presents a sound and viable environment for the multi-frame detector. Finally, we study implementations of both single and multi-frame object detectors based on this client-Server Framework on a mobile device running the android OS.
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CVPR - Mobile object detection through client-Server based vote transfer
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012Co-Authors: Shyam Sunder Kumar, Silvio SavareseAbstract:Mobile platforms such as smart-phones and tablet computers have attained the technological capacity to perform tasks beyond their intended purposes. The steady increase of processing power has enticed researchers to attempt increasingly challenging tasks on mobile devices with appropriate modifications over their stationary counterparts. In this work we present a novel multi-frame object detection application for the mobile platform that is capable of object localization. Our work leverages the hough forest based object detector introduced by Gall et al. in [10]. In our experiments, we demonstrate that our novel, multi-frame generalization of [10] notably improves the detection performance. We test the performance of the technique in variable resolutions, the applicability to several object categories and different datasets. We implement the multi-frame detector on a mobile platform through a novel client-Server Framework that presents a sound and viable environment for the multi-frame detector. Finally, we study implementations of both single and multi-frame object detectors based on this client-Server Framework on a mobile device running the android OS.
Wenxi Wang - One of the best experts on this subject based on the ideXlab platform.
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snphub an easy to set up web Server Framework for exploring large scale genomic variation data in the post genomic era with applications in wheat
GigaScience, 2020Co-Authors: Wenxi Wang, Zihao Wang, Xintong Li, Zhongfu Ni, Zhaorong Hu, Huiru PengAbstract:BACKGROUND The cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used Framework for setting up user-friendly web Servers to explore new genomic variation data in diverse species. RESULTS Here, we present SnpHub, a Shiny/R-based Server Framework for retrieving, analysing, and visualizing large-scale genomic variation data that can be easily set up on any Linux Server. After a pre-building process based on the provided VCF files and genome annotation files, the local Server allows users to interactively access single-nucleotide polymorphisms and small insertions/deletions with annotation information by locus or gene and to define sample sets through a web page. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by detected sequence variations. CONCLUSIONS SnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in recent studies. SnpHub and its tutorial are available at http://guoweilong.github.io/SnpHub/. The wheat-SnpHub-portal website can be accessed at http://wheat.cau.edu.cn/Wheat_SnpHub_Portal/.
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snphub an easy to set up web Server Framework for exploring large scale genomic variation data in the post genomic era with applications in wheat
bioRxiv, 2020Co-Authors: Wenxi Wang, Zihao Wang, Xintong Li, Zhongfu Ni, Zhaorong Hu, Huiru PengAbstract:Background: The cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used Framework for setting up user-friendly web Servers for exploring new genomic variation data in diverse species. Results: Here, we present SnpHub, a Shiny/R-based Server Framework for retrieving, analysing and visualizing the large-scale genomic variation data that be easily set up on any Linux Server. After a pre-building process based on the provided VCF files and genome annotation files, the local Server allows users to interactively access SNPs/INDELs and annotation information by locus or gene and for user-defined sample sets through a webpage. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by SNPs/INDELs. Conclusions: SnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in present studies. SnpHub and its tutorial are available as http://guoweilong.github.io/SnpHub/.
