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    Method of acquiring neighboring disparity vectors for multi-texture and multi-depth video
    2.
    发明授权
    Method of acquiring neighboring disparity vectors for multi-texture and multi-depth video 有权

    公开(公告)号：US09883200B2

    公开(公告)日：2018-01-30

    申请号：US15316009

    申请日：2015-04-30

    Applicant: Beijing University of Technology

    Inventor： Kebin Jia ,  Zuocheng Zhou ,  Pengyu Liu ,  Yibai Wang

    IPC: H04N19/52 ,  H04N19/597 ,  H04N19/146 ,  H04N19/172

    CPC classification number: H04N19/52 ,  H04N19/146 ,  H04N19/172 ,  H04N19/597

    Abstract: The present disclosure relates to a method of acquiring neighboring disparity vectors for multi-texture and multi-depth video. The method belongs to the area of 3D-HEVC video coding technology. The method includes changing the standard associated with a disparity vector that is first searched as a final disparity vector. By deleting location which is minimum searched in candidate space and time location of the coding unit next to current coding unit to divide candidate space and time location of the coding unit into groups, the method takes searched disparity vector that is combined based on the proportion of adoption rate as final disparity vector. The method improves coding quality and at the same time maintaining origin fast algorithm efficiency. The embodiments of the present disclosure improve coding quality at least 0.05% and at the same time maintain origin fast algorithm efficiency while the decoding time is decreased to 97.1%.

    A Method for Fast 3D Video Coding for HEVC
    3.
    发明申请
    A Method for Fast 3D Video Coding for HEVC 有权
    一种用于HEVC的快速3D视频编码方法

    公开(公告)号：US20160353129A1

    公开(公告)日：2016-12-01

    申请号：US14889559

    申请日：2014-12-06

    Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

    Inventor： Kebin Jia ,  Huan Dou

    IPC: H04N19/597 ,  H04N19/176 ,  H04N19/182 ,  H04N19/147

    CPC classification number: H04N19/597 ,  H04N19/14 ,  H04N19/147 ,  H04N19/159 ,  H04N19/176 ,  H04N19/182 ,  H04N19/186

    Abstract: The present disclosure relates to the technical field of video coding. Implementations herein provide methods for fast 3D video coding for high efficiency video coding HEVC. The methods speed up the view synthesis process during the rate distortion optimization for depth coding based on texture flatness. The implementations include extracting coding information from textures, analyzing luminance regularity among pixels from flat texture regions based on statistical method, judging the flat texture regions using the luminance regularity for depth maps and terminating the flat texture block's view synthesis process when processing rate distortion optimization. Compared to original pixel-by-pixel rendering methods, the implementations reduce coding time without causing significant performance loss.

    Abstract translation: 本公开涉及视频编码的技术领域。 本文的实现提供了用于高效率视频编码HEVC的快速3D视频编码的方法。 该方法在基于纹理平坦度的深度编码速率失真优化中加快了视图合成过程。 这些实现包括从纹理提取编码信息，基于统计方法分析来自平坦纹理区域的像素之间的亮度规律性，使用深度图的亮度规则来判断平面纹理区域，并且在处理速率失真优化时终止平面纹理块的视图合成处理。 与原始的逐像素渲染方法相比，这些实现减少了编码时间，而不会导致显着的性能损失。

    Bioluminescence tomography reconstruction based on multitasking bayesian compressed sensing
    4.
    发明授权
    Bioluminescence tomography reconstruction based on multitasking bayesian compressed sensing 有权

    公开(公告)号：US09672639B1

    公开(公告)日：2017-06-06

    申请号：US15108394

    申请日：2015-07-21

    Applicant: Beijing University of Technology

    Inventor： Jinchao Feng ,  Kebin Jia ,  Huijun Wei

    IPC: G06T11/00 ,  A61B5/00 ,  G06T15/50

    CPC classification number: G06T11/005 ,  A61B5/0071 ,  A61B5/0073 ,  A61B5/7225 ,  A61B5/7267 ,  A61B2503/40 ,  A61B2576/00 ,  G06T15/506 ,  G06T2210/41

    Abstract: Implementations of the present disclosure relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.

    Method of acquiring neighboring disparity vectors for multi-texture and multi-depth video
    5.
    发明申请
    Method of acquiring neighboring disparity vectors for multi-texture and multi-depth video 有权

    公开(公告)号：US20170094306A1

    公开(公告)日：2017-03-30

    申请号：US15316009

    申请日：2015-04-30

    Applicant: Beijing University of Technology

    Inventor： Kebin Jia ,  Zuocheng Zhou ,  Pengyu Liu ,  Yibai Wang

    IPC: H04N19/52 ,  H04N19/146 ,  H04N19/597

    CPC classification number: H04N19/52 ,  H04N19/146 ,  H04N19/172 ,  H04N19/597

    Abstract: The present disclosure relates to a method of acquiring neighboring disparity vectors for multi-texture and multi-depth video (e.g., 3D-HEVC video). The method belongs to the area of 3D-HEVC video coding technology. The method includes changing the standard associated with a disparity vector that is first searched as a final disparity vector. By deleting location which is minimum searched in candidate space and time location of the coding unit next to current coding unit to divide candidate space and time location of the coding unit into groups, the method takes searched disparity vector that is combined based on the proportion of adoption rate as final disparity vector. The method improves coding quality and at the same time maintaining origin fast algorithm efficiency. The embodiments of the present disclosure improve coding quality at least 0.05% and at the same time maintain origin fast algorithm efficiency while the decoding time is decreased to 97.1%.

    Near-infrared spectroscopy tomography reconstruction method based on neural network
    6.
    发明授权
    Near-infrared spectroscopy tomography reconstruction method based on neural network 有权

    公开(公告)号：US12089917B2

    公开(公告)日：2024-09-17

    申请号：US16620860

    申请日：2018-12-21

    Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

    Inventor： Jinchao Feng ,  Kebin Jia ,  Qiuwan Sun ,  Zhe Li ,  Zhonghua Sun

    IPC: A61B5/00 ,  G06N3/084 ,  G06N5/046 ,  G06N20/00 ,  G06T11/00

    CPC classification number: A61B5/0073 ,  A61B5/0075 ,  G06N3/084 ,  G06N5/046 ,  G06N20/00 ,  G06T11/006 ,  G06T2210/41

    Abstract: The present disclosure disclose a near-infrared spectroscopy tomography reconstruction method based on neural network which belongs to the field of medical image processing. In the Boltzmann radiation transmission equation, transmission process of light is regarded as absorption and scattering process of photons in medium, and interaction between light and tissue is determined by absorption coefficient, scattering coefficient and phase function of the response scattering distribution. In the transmission, only the particle property of light is taken into account, not the fluctuation of light. Therefore, polarization and interference phenomena related to the fluctuation of light are not considered, and only the energy transmission of light is tracked. The reconstruction method based on BP neural network is used to reconstruct the distribution of optical absorption coefficient, reconstruction results of absorption coefficient distribution can be obtained by calculation. This method can not only reconstruct the absorption coefficient distribution accurately, but also has high computational efficiency.

    Bioluminescence tomography reconstruction based on multitasking Bayesian compressed sensing
    7.
    发明申请
    Bioluminescence tomography reconstruction based on multitasking Bayesian compressed sensing 有权

    公开(公告)号：US20170148193A1

    公开(公告)日：2017-05-25

    申请号：US15108394

    申请日：2015-07-21

    Applicant: Beijing University of Technology

    Inventor： Jinchao Feng ,  Kebin Jia ,  Huijun Wei

    IPC: G06T11/00 ,  G06T15/50 ,  A61B5/00

    CPC classification number: G06T11/005 ,  A61B5/0071 ,  A61B5/0073 ,  A61B5/7225 ,  A61B5/7267 ,  A61B2503/40 ,  A61B2576/00 ,  G06T15/506 ,  G06T2210/41

    Abstract: Implementations of the present disclosure relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.

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