3D video watermarking is far from the maturity of watermarking algorithms dedicated to audio, image or 2D video. In fact, little work has been proposed for anaglyph 3D videos. Such methods have not been robust against malicious attacks such as compression and collusion. The latter one is a very dangerous attack that can be applied on video content and can easily remove an embedded signature to obtain original content. This paper proposes an efficient approach for anaglyph 3D videos which can resist this malicious attack thanks to the use of a static multi-sprite generated from the different sets of frames which compose the original video. First, a sprite is generated for every set of 25 frames. Then every sprite is marked using a hybrid scheme: the discrete wavelet transform based algorithm and the middle significant bit technique, which improve signature invisibility and enhance robustness. Finally, a marked anaglyph 3D video is generated from every marked sprite. The use of multi-sprites as an embedding target provides robustness against collusion attacks and a high quality of marked video reconstruction compared with static mosaics. The experimental results show good robustness against collusion, compression and other attacks such as geometric manipulation and temporal attacks. Besides, the proposed technique presents a high level of invisibility.
Dealing with different head poses is one of the most challenging issues in the area of face recognition. Recently, 3D images have been used for this purpose as they can gather more information from the head area. Kinect was used for capturing 3D images in our research. Iterative Closest Point (ICP) algorithm has been used in many researches to align a rotated pointcloud with its corresponding reference. However it has many variables that can improve its performance. So an improved version of ICP has been introduced in our research and its performance in terms of accuracy and speed has been evaluated. While it can have many applications, we have used it for increasing the performance of posed face recognition. We applied our proposed algorithm on a local database and concluded that it can significantly improve the recognition rate of 3D posed face recognition compared with using original raw posed image. Results of executing the proposed algorithm on a public database also indicate an improvement with respect to other recently proposed methods.
D2D user equipment (DUE) multiplex wireless resources of non-orthogonal cellular user equipment (CUE), which can solve the problem of spectrum resource shortage. However, these interferences not only affect the throughput of DUE and CUE, but also undermine their fairness in receiving services. In order to ensure the fairness of CUE and DUE in quality of service and the fairness in using spectrum resources, a D2D wireless resource allocation scheme based on overall fairness is proposed. First of all, it allows multiple D2D users to multiplex the resource of a CUE which increases the access rate of D2D pairs and the throughput of the marginal users; Then it clusters and reconstructs multiple D2D pairs based on graph coloring theory and the reconstructed cluster is taken as a unit; Finally, maximizing the weight value of each D2D reconstructed cluster and its matching cellular users on a resource block (RB) k. Simulation results show that the proposed algorithm can significantly increase the overall fairness and the throughput of the marginal users, access rate of D2D pairs compared with other algorithms.
In this paper, a novel, incompressible fluid simulation framework based on the divergence-free Smoothed Particle Hydrodynamics model is presented. The novel SPH model combines a system of non-linear density constraint conditions and the divergence-free velocity field condition to enforce fluid incompressibility. In the new framework, the position of particles is firstly modified by solving the density constraints, and the distribution of particles is adjusted to keep the density of fluid in a relatively constant state. Then the divergence-free state increases the stability significantly and reduces the number of solver iterations. Compared to the modern Smoothed Particle Hydrodynamic (SPH) solvers, the new SPH framework allows better incompressibility and similar convergence. Finally, the method of pre-computed smoothing kernel functions is used to accelerate the proposed SPH model. It can effectively improve the real-time performance of the algorithm while maintaining sufficient accuracy.
In this present study a perfect outcome of skin lesion in the computerized image analysis is used to segment the abnormal layers on the skin. The dermatologist finds difficult for easy identification of skin lesion. A computational tool should be developed to assist the dermatologist for diagnosis. This paper reports the differentiation of segmentation with various techniques. The review is made with related works to the current proposed method as a comparative study with plenty of fundamental steps like image acquisition, pre-processing and segmentation. In this work, the asymmetric pattern extractions from the dermoscopic images are segmented by the HSV segmentation to find the contour image. An automatic segregation of RGB–HSV is incorporated in the masked threshold on the proposed system which segments the lesion. The techniques involved in each stage are perfectly explained. From the state of RGB input and handling of pre-processing and segmentation were evaluated effectively. The outcome of this result is compared with other segmentation techniques to improve the result. The proposed performance measures between Ground Truth image and Segmented Image provides best-offered values of accuracy up to 96% for PH2 dataset and 95% for ISIC 2016 Dataset.
As the rate of the video is increasing on the internet, the security of video data is considered as a critical issue. In case of video surveillance application, the multimedia video streams require the video to be transmitted in a more secure way to its corresponding monitoring site. The multimedia video security is improved in this paper using a compression based encryption module. 2D Discrete Wavelet Transforms method is used for the compression process. The wavelet transform eliminates the low visual information, and it is scrambled and rotated for the encryption process. The encryption is handled using a series of the permutation–diffusion framework that helps in encrypting each video frame for possible secured transmission. The diffusion is carried out using Logistic chaotic maps, and the permutated blocks are encrypted with block-wise encryption. The experimental results show that the proposed video security framework achieves an improved performance against the existing method in terms of PSNR, SSIM, key size, and compression ratio, error rate.
In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.
In order to solve the problem of node energy consumption in wireless sensor networks, an energy-efficient tracking cluster structure is proposed. The structure of the tracking cluster is determined by the cooperation between the auxiliary node and the cluster head node, and avoids the redundant nodes participating in the tracking. In order to balance the energy consumption of cluster head nodes, the method predict the position of target in next time by making auxiliary nodes track algorithm, then according to the prediction results, the nodes near prediction position are woken up in advance to reduce the energy consumption in the whole net. In the process of tracking, the loss recovery mechanism is adopted to solve the target loss phenomenon, and the continuous tracking of the target is completed. Finally, experiments are carried out with the improved particle filter algorithm. The simulation results show that the proposed algorithm can reduce the energy consumption of the nodes under the condition that the tracking accuracy is satisfied. Make the whole network energy consumption more balanced.
In an induction melting furnace, magnetizers are placed outside the coil region regularly, which prevents leakage of magnetic flux and, therefore, increases the induction melting efficiency of the furnace. In the present paper, the effects of the geometric and structural parameters of magnetizers on the magnetic flux intensity distributed in the material region of an induction melting furnace are investigated. A method for determining resistivity and relative permeability of lamination material of magnetizers in harmonic magnetic field simulation is established. The results indicate that magnetizer height has the strongest influence on the magnetic field distribution formed at the induction coil end. A skin effect and a vector vortex due to magnetic induction were formed at the left and the right sides of the magnetizers, respectively, while a non-magnetized zone was formed in the middle part of the coil. The leakage of the magnetic field at the coil end could be reduced by using an L-type magnetizer. The increased ratio along the circumferential direction of magnetizer improved the magnetic flux intensity in the material field located in the magnetizer gaps, while at the same ratio, increasing the number of magnetizers helped significantly in terms of improving magnetic flux intensity.
Thanks to the rapid growth of internet and the advanced development of 3D technology, 3D images and videos are proliferated over the networks. However, this causes several insecurity problems, and protecting this type of media has become a main challenge for many researchers. 3D watermarking is considered as an efficient solution for 3D data protection. In fact, it consists in embedding a secret key into a 3D content to protect it and in trying to extract it after any attack applied on marked 3D data. Anaglyph is the most popular and economical method among different 3D visualization methods. For this reason, it has become used for many 3D applications. Hence, 3D anaglyph watermarking presents an important research area, and several techniques have been proposed in order to protect this type of media. In this survey paper, the existing anaglyph 3D images and videos watermarking techniques are discussed. This discussion shows that the anaglyph video watermarking field is still not mature and new techniques should be proposed to improve the invisibility/robustness trade-off. In addition, based on the study of anaglyph generation methods, it is concluded that signature can be embedded during the generation stage.
This research work proposes a novel asymmetric scheme by utilizing the hybrid phase truncated fractional Fourier and Gyrator transform with the secure enhancement by adding Kronecker product as a key. In this cryptosystem, the encryption keys constitute Hybrid Phase Mask along with the Optical Vortex Phase Mask and Kronecker product as the other keys. The Hybrid Phase Mask is formed by the combination of a secondary image and random phase mask. The phase truncated parts during encryption process are reserved as decryption keys with the inverse Kronecker product. The proposed method comprises of more obscure keys for upgraded security and to defend different attacks. In support of the technique proposed, the results under the effect of various attacks are presented. The efficiency and robustness of the presented cryptosystem has been examined and demonstrated by simulation in MATLAB (R2014a) with different performance parameters.
This study presents a geometric feature based automatic facial expression recognition system. The proposed system utilises the facial landmark points to determine the relative distances between the facial features in order to capture deformities caused by the movement of facial muscles due to different expressions. Three feature sets are generated by using landmark coordinates, relative distances between the facial points and a combination of both. Discriminating power of each feature set is determined by training different classification models for classifying an image into six basic emotions or neutral state. The proposed system is validated on two publically available facial expression databases. Experimental results show good accuracy of 95.5% for MUG database on the combined features by using ensemble neural network.
In image analysis and pattern recognition activity, one of the most salient characteristics is texture. The global region of images in spatial domain has an enhanced processing effect with the help of co-occurrence matrix and in the frequency domain for the admirable performance such as multi-scale, multi-direction local information is obtained from Gabor wavelet. The consolidation of gray-level co-occurrence matrix and Gabor wavelet is utilized to fabric image feature texture eradication. In classification phase, random decision forest (RDFs) Classifier is applied to classify the input fabric image into defective or non-defective. RDFs are a novel and outfit machine learning strategy which fuses the element choice. Nevertheless, RDFs exhibit a lot of advantages when compared with other modeling approaches within the category. The main advantages are, RDFs can handle both the continuous and discrete variables, RDFs does not overfit as a classifier, and run quick and productively when taking care of expansive datasets.
The secret sharing scheme is a security tool that provides reliability and robustness for multi-user authentication systems. This work focuses on improving the security and efficiency of counting-based secret sharing by remodeling its phases. The research presents refining the shares generation phase as well as proposing different secret reconstruction models based on new shares distribution method. This work addressed solving published defects in the original counting-based secret sharing scheme providing optimized efficient methodology. The research further proposed utilizing steganography for practicality purposes. We adopted image-based steganography to hide generated shares preserving the improved security of the scheme enhancing the humanized remembrance usability. The study compared several applicable steganography methods for hiding shares analyzing their distortion, security, and capacity. This facilitating work of image-based steganography with the improved counting-based secret sharing is showing promising results opening research direction for future attractive contributions to follow-on.
The robustness of dense-visual-odometry is still a challenging problem if moving objects appear in the scene. In this paper, we propose a form of dense-visual-odometry to handle a highly dynamic environment by using RGB-D data. Firstly, to find dynamic objects, we propose a multi-frame based residual computing model, which takes a far time difference frame into consideration to achieve the temporal consistency motion segmentation. Then the proposed method combines a scene clustering model and a nonparametric statistical model to obtain weighted cluster-wise residuals, as the weight describes how importantly a cluster residual is considered. Afterward, the motion segmentation labeling and clusters’ weights are added to the energy function optimization of dense-visual-odometry to reduce the influence of moving objects. Finally, the experimental results demonstrate that the proposed method has better performance than the state-of-the-art methods on many challenging sequences from a benchmark dataset, especially on highly dynamic sequences.
3D point clouds, a new primitive representation for objects, are spreading among thousands of people through internet software. Thus, the privacy preserving problem of the 3D point cloud should be widely concerned by more and more people. To ensure the safe transmission and use of point cloud, two schemes of encryption have been proposed by using chaotic cat mapping in this paper. The two encryption schemes are tested by using various types of 3D point clouds. In addition, these proposed encryption algorithms are analyzed through key space, sensibility, statistical and encryption time analysis. These analysis results show that the two proposed schemes can resist the common existing cipher attacks and are effective encryption methods for 3D point cloud encryption. At the same time, the two promising encryption algorithms can guarantee the security of the 3D point cloud model transmitted on the Internet.
Conventional X-ray is commonly used for pelvic fracture follow-ups, but has a precision of only ± 5 mm. Implantation of tantalum beads together with RSA has shown high precision but not applicable in clinical practice. CT scan has been shown a suitable substitute for RSA to follow the metal markers. We aimed to assess whether implantation of metal markers could be avoided using CT scan and merging of bone surface anatomy. A human cadaveric pelvis marked with 0.8 mm tantalum beads was fixed over the symphysis and the right SI-joint. Left hemi-pelvis was subsequently distracted using plastic spacers. Sequential CT exams was conducted and data were analyzed using Sectra ® (Sectra AB), CTMA package. Examinations were repeated after 2 weeks. Bone registration showed better precision than registration based on tantalum beads. However, only the difference in angular changes was statistically significant ( p = 0.008). The confidence interval of the repeatability was ± 0.5 mm for translation and ± 0.5° for rotation. This new non-invasive technique showed good precision and repeatability and might be a future option in clinical practice for post-operative follow-ups of patients with pelvic fractures.
Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach.
Traditional methods are ineffective in predicting the three-dimensional path of a six-degree-of-freedom (SDOF) manipulator. In view of the above situation, this paper proposes a three-dimensional space path prediction simulation method for a SDOF manipulator. The structure of the SDOF manipulator is analyzed, and the kinematics model of the manipulator is constructed. The kinematics model of the manipulator is solved by the forward and reverse kinematics solutions. According to the inverse kinematics solutions, the method of automatic optimization of multiple solutions is obtained, which can effectively improve the performance of the manipulator. The collision is avoided in the three-dimensional motion of a SDOF manipulator. On the basis of the kinematics of the manipulator, the Cartesian space is used to predict the path trajectory. The average operation time of the path planning cycle and the deviation of the relative smooth trajectory are compared through the three-dimensional path prediction distance and the straight line distance of the SDOF manipulator. The experimental results show that the average operation time of the period of the path prediction between the two is close, and the deviation of the three-dimensional space path prediction distance of the SDOF manipulator is better than that of the straight line distance. It has certain application performance.
