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  • Association for Computing Machinery (ACM)  (3)
  • Computer Science  (3)
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  • Association for Computing Machinery (ACM)  (3)
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  • Computer Science  (3)
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  • 1
    Online Resource
    Online Resource
    360-Degree VR Video Watermarking Based on Spherical Wavelet Transform
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 17, No. 1 ( 2021-02-28), p. 1-23
    Details
    In: ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 17, No. 1 ( 2021-02-28), p. 1-23
    Abstract: Similar to conventional video, the increasingly popular 360  virtual reality (VR) video requires copyright protection mechanisms. The classic approach for copyright protection is the introduction of a digital watermark into the video sequence. Due to the nature of spherical panorama, traditional watermarking schemes that are dedicated to planar media cannot work efficiently for 360  VR video. In this article, we propose a spherical wavelet watermarking scheme to accommodate 360  VR video. With our scheme, the watermark is first embedded into the spherical wavelet transform domain of the 360  VR video. The spherical geometry of the 360  VR video is used as the host space for the watermark so that the proposed watermarking scheme is compatible with the multiple projection formats of 360  VR video. Second, the just noticeable difference model, suitable for head-mounted displays (HMDs), is used to control the imperceptibility of the watermark on the viewport. Third, besides detecting the watermark from the spherical projection, the proposed watermarking scheme also supports detecting watermarks robustly from the viewport projection. The watermark in the spherical domain can protect not only the 360  VR video but also its corresponding viewports. The experimental results show that the embedded watermarks are reliably extracted both from the spherical and the viewport projections of the 360  VR video, and the robustness of the proposed scheme to various copyright attacks is significantly better than that of the competing planar-domain approaches when detecting the watermark from viewport projection.
    Type of Medium: Online Resource
    ISSN: 1551-6857 , 1551-6865
    URL: Article
    DOI: 10.1145/3425605
    RVK:
    ST 325
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2182650-X
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Expression Robust 3D Facial Landmarking via Progressive Coarse-to-Fine Tuning
    Association for Computing Machinery (ACM) ; 2019
    In:  ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 15, No. 1 ( 2019-02-28), p. 1-23
    Details
    In: ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 15, No. 1 ( 2019-02-28), p. 1-23
    Abstract: Facial landmarking is a fundamental task in automatic machine-based face analysis. The majority of existing techniques for such a problem are based on 2D images; however, they suffer from illumination and pose variations that may largely degrade landmarking performance. The emergence of 3D data theoretically provides an alternative to overcome these weaknesses in the 2D domain. This article proposes a novel approach to 3D facial landmarking, which combines both the advantages of feature-based methods as well as model-based ones in a progressive three-stage coarse-to-fine manner (initial, intermediate, and fine stages). For the initial stage, a few fiducial landmarks (i.e., the nose tip and two inner eye corners) are robustly detected through curvature analysis, and these points are further exploited to initialize the subsequent stage. For the intermediate stage, a statistical model is learned in the feature space of three normal components of the facial point-cloud rather than the smooth original coordinates, namely Active Normal Model (ANM). For the fine stage, cascaded regression is employed to locally refine the landmarks according to their geometry attributes. The proposed approach can accurately localize dozens of fiducial points on each 3D face scan, greatly surpassing the feature-based ones, and it also improves the state of the art of the model-based ones in two aspects: sensitivity to initialization and deficiency in discrimination. The proposed method is evaluated on the BU-3DFE, Bosphorus, and BU-4DFE databases, and competitive results are achieved in comparison with counterparts in the literature, clearly demonstrating its effectiveness.
    Type of Medium: Online Resource
    ISSN: 1551-6857 , 1551-6865
    URL: Article
    DOI: 10.1145/3282833
    RVK:
    ST 325
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2019
    detail.hit.zdb_id: 2182650-X
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Texture and Geometry Scattering Representation-Based Facial Expression Recognition in 2D+3D Videos
    Association for Computing Machinery (ACM) ; 2018
    In:  ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 14, No. 1s ( 2018-03-31), p. 1-23
    Details
    In: ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 14, No. 1s ( 2018-03-31), p. 1-23
    Abstract: Facial Expression Recognition (FER) is one of the most important topics in the domain of computer vision and pattern recognition, and it has attracted increasing attention for its scientific challenges and application potentials. In this article, we propose a novel and effective approach to FER using multi-model two-dimensional (2D) and 3D videos, which encodes both static and dynamic clues by scattering convolution network. First, a shape-based detection method is introduced to locate the start and the end of an expression in videos; segment its onset, apex, and offset states; and sample the important frames for emotion analysis. Second, the frames in Apex of 2D videos are represented by scattering, conveying static texture details. Those of 3D videos are processed in a similar way, but to highlight static shape details, several geometric maps in terms of multiple order differential quantities, i.e., Normal Maps and Shape Index Maps, are generated as the input of scattering, instead of original smooth facial surfaces. Third, the average of neighboring samples centred at each key texture frame or shape map in Onset is computed, and the scattering features extracted from all the average samples of 2D and 3D videos are then concatenated to capture dynamic texture and shape cues, respectively. Finally, Multiple Kernel Learning is adopted to combine the features in the 2D and 3D modalities and compute similarities to predict the expression label. Thanks to the scattering descriptor, the proposed approach not only encodes distinct local texture and shape variations of different expressions as by several milestone operators, such as SIFT, HOG, and so on, but also captures subtle information hidden in high frequencies in both channels, which is quite crucial to better distinguish expressions that are easily confused. The validation is conducted on the BU-4DFE and BP-4D databa ses, and the accuracies reached are very competitive, indicating its competency for this issue.
    Type of Medium: Online Resource
    ISSN: 1551-6857 , 1551-6865
    URL: Article
    DOI: 10.1145/3131345
    RVK:
    ST 325
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2018
    detail.hit.zdb_id: 2182650-X
    Location Call Number Limitation Availability
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    Online Resource
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