Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a\nsubstantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D\nscanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and\nclustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is\nperformed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the\nsimulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of\nthe proposed simplification method of 3D point cloud....
Camera calibration via bundle adjustment is a well-established standard procedure in\nsingle-medium photogrammetry. When using standard software and applying the collinearity\nequations in multimedia photogrammetry, the effects of refractive interfaces are compensated in\nan implicit form, hence by the usual parameters of interior orientation. This contribution analyses\ndifferent calibration strategies for planar bundle-invariant interfaces. To evaluate the effects of\nimplicitly modelling the refractive effects within bundle adjustment, synthetic error-free datasets\nare simulated. The behaviour of interior, exterior, and relative orientation parameters is analysed\nusing synthetic datasets free of underwater imaging effects. A shift of the camera positions of 0.2%\nof the acquisition distance along the optical axis can be observed. The relative orientation of a\nstereo camera shows systematic effects when the angle of convergence varies. The stereo baseline.................
In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large\ncollection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video\nretrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the\nfeature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that\nredundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts\naverage pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the\nlarge-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the\nproposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods....
The protection of database systems content using digital watermarking is nowadays an emerging research direction in information\nsecurity. In the literature, many solutions have been proposed either for copyright protection and ownership proofing or integrity\nchecking and tamper localization. Nevertheless, most of them are distortion embedding based as they introduce permanent errors\ninto the cover data during the encoding process, which inevitably affect data quality and usability. Since such distortions are not\ntolerated in many applications, including banking, medical, and military data, reversible watermarking, primarily designed for\nmultimedia content, has been extended to relational databases. In this article, we propose a novel prediction-error expansion\nbased on reversible watermarking strategy, which not only detects and localizes malicious modifications but also recovers back the\noriginal data at watermark detection. Theeffectiveness of the proposed method is proved through rigorous theoretical analysis and\ndetailed experiments....
In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become\na very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through\nalgorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers\nanalyzed the difference in learnersâ?? attention. The study found no significant difference in attention in different media, but\nlearners using text media had the highest attention value. Later, the researchers studied the attention of learners with different\nlearning styles and found that active and reflective learnersâ?? attention exhibited significant differences when using video media\nto learn....
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