In the past decade, great efforts have been made to extend linear discriminant analysis for higher-order data classification, generally referred to as multilinear discriminant analysis (MDA). ...Existing examples include general tensor discriminant analysis (GTDA) and discriminant analysis with tensor representation (DATER). Both the two methods attempt to resolve the problem of tensor mode dependency by iterative approximation. GTDA is known to be the first MDA method that converges over iterations. However, its performance relies highly on the tuning of the parameter in the scatter difference criterion. Although DATER usually results in better classification performance, it does not converge, yet the number of iterations executed has a direct impact on DATER's performance. In this paper, we propose a closed-form solution to the scatter difference objective in GTDA, namely, direct GTDA (DGTDA) which also gets rid of parameter tuning. We demonstrate that DGTDA outperforms GTDA in terms of both efficiency and accuracy. In addition, we propose constrained multilinear discriminant analysis (CMDA) that learns the optimal tensor subspace by iteratively maximizing the scatter ratio criterion. We prove both theoretically and experimentally that the value of the scatter ratio criterion in CMDA approaches its extreme value, if it exists, with bounded error, leading to superior and more stable performance in comparison to DATER.
Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability. However, as a result of purely heuristic ...distortion definitions and numerically solvable equations in statistical models, there are no closed-form solutions for JPEG steganography. The absence of closed-form expression to model JPEG steganography is the main limitation on understanding single image and pool steganalysis behavior. In this study, building upon our previously proposed framework for spatial steganography, we develop a statistical framework for JPEG steganography in which the cover and the hidden message are modeled by multivariate Gaussian distribution. Based on this statistical model, we propose a novel quantized Gaussian JPEG steganography that is able to accomplish embedding using any costs defined in spatial or discrete cosine transform (DCT) domain as well as residual variances. We conduct our experiments using a popular database with different compression qualities to determine the effectiveness of the proposed model. The experimental results show that the proposed model improves the security of previous works and outperforms the state-of-the-art JPEG steganography algorithms. Furthermore, we extend the closed-form expression of single image steganalysis error to pool steganalysis for an omniscience optimal detector. We employ the derived expression to approximate the empirical results of pool steganalysis based on the empirical detection error of single image steganalysis. The practical advantage of the approximation is that even though it is derived based on the adopted statistical model, it is accurate regardless of payload, embedding domain, embedding method, and steganalysis feature as long as the pooling strategy is optimal. In addition to approximation of the error, we employ the proposed model to make predictions about the variance behavior of pool steganalysis error. We mathematically show that the variance increases as the pool size increases in small payloads. The same behavior is observed in experimental results which re-validates our analytical model. We conclude that although pooling improves detector's performance, it makes the detector less stable in low payloads and high pool sizes.
In this paper, we introduce a method to estimate the object’s pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature ...Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester’s equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester’s equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester’s equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.
In recent years, video surveillance has become essential for security applications used to monitor many organizations and locations, and it is therefore important to ensure the reliability of these ...surveillance videos. Unfortunately, surveillance videos can be forged with little effort by deleting an object from a video scene while leaving no visible traces. A fundamental challenge in video security is to determine whether or not an object has been removed from a video. This task is particularly challenging due to the lack of ground truth bases that can be used to verify the originality and integrity of video contents. In this paper, we propose a novel approach based on sequential and patch analyses to detect object removal forgery and to localize forged regions in videos. Sequential analysis is performed by modeling video sequences as stochastic processes, where changes in the parameters of these processes are used to detect a video forgery. Patch analysis is performed by modeling video sequences as a mixture model of normal and anomalous patches, with the aim to separate these patches by identifying the distribution of each patch. We localize forged regions by visualizing the movement of removed objects using anomalous patches. We conduct our experiments at both pixel and video levels to determine the effectiveness and efficiency of our approach to detection of video forgery. The experimental results show that our approach achieves excellent detection performance with low-computational complexity and leads to robust results for compressed and low-resolution videos.
•Introduces a method to stop a gene drive's spread through population at unknown locus.•Demonstrates method's success through Graphical Markov model and computer simulations.•Addresses questions for ...future prevention of undesired gene drive spread.
The mutagenic chain reaction (MCR) is a genetic tool to use a CRISPR–Cas construct to introduce a homing endonuclease, allowing gene drive to influence whole populations in a minimal number of generations (Esvelt et al., 2014; Gantz and Bier, 2015; Gantz and Bier, 2016). The question arises: if an active genetic terror event is released into a population, could we prevent the total spread of the undesired allele (Gantz, et al., 2015; Webber et al., 2015)? Thus far, effective protection methods require knowledge of the terror locus (Grunwald et al., 2019). Here we introduce a novel approach, an autocatalytic-Protection for an Unknown Locus (a-PUL), whose aim is to spread through a population and arrest and decrease an active terror event’s spread without any prior knowledge of the terror-modified locus, thus allowing later natural selection and ERACR drives to restore the normal locus (Hammond et al., 2017). a-PUL, using a mutagenic chain reaction, includes (i) a segment encoding a non-Cas9 endonuclease capable of homology-directed repair suggested as Type II endonuclease Cpf1 (Cas12a), (ii) a ubiquitously-expressed gene encoding a gRNA (gRNA1) with a U4AU4 3′-overhang specific to Cpf1 and with crRNA specific to some desired genomic sequence of non-coding DNA, (iii) a ubiquitously-expressed gene encoding two gRNAs (gRNA2/gRNA3) both with tracrRNA specific to Cas9 and crRNA specific to two distinct sites of the Cas9 locus, and (iv) homology arms flanking the Cpf1/gRNA1/gRNA2/gRNA3 cassette that are identical to the region surrounding the target cut directed by gRNA1 (Khan, 2016; Zetsche et al., 2015). We demonstrate the proof-of-concept and efficacy of our protection construct through a Graphical Markov model and computer simulation.
This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent ...trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory representation when only partial trajectory information is available due to occlusion. It is achieved by a hypothesis testing-based method applied to curvature data computed from trajectories. The subtrajectories are then represented by their principal component analysis (PCA) coefficients for optimally compact representation. Different techniques are integrated to index and retrieve subtrajectories, including PCA, spectral clustering, and string matching. We assume a query by example mechanism where an example trajectory is presented to the system and the search system returns a ranked list of most similar items in the dataset. Experiments based on datasets obtained from University of California at Irvine's KDD archives and Columbia University's DVMM group demonstrate the superiority of our proposed PCA-based approaches in terms of indexing and retrieval times and precision recall ratios, when compared to other techniques in the literature
In digital image steganography, the statistical model of an image is essential for hiding data in less detectable regions and achieving better security. This has been addressed in the literature ...where different cost-based and statistical model-based approaches were proposed. However, due to the usage of heuristically defined distortions and non-constrained message models, resulting in numerically solvable equations, there is no closed-form expression for security as a function of payload. The closed-form expression is crucial for a better insight into image steganography problem and also improving performance of batch steganography algorithms. Here, we develop a statistical framework for image steganography in which the cover and the stego messages are modeled as multivariate Gaussian random variables. We propose a novel Gaussian embedding model by maximizing the detection error of the most common optimal detectors within the adopted statistical model. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that improves empirical results of current cost-based and statistical model-based approaches. This methodology and its presented solution, by reason of assuming a continuous hidden message, remains the same for any embedding scenario. Afterward, the closed-form detection error is derived within the adopted model for image steganography and it is extended to batch steganography. Thus, we introduce Adaptive Batch size Image Merging steganographer, AdaBIM , and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.
Compressive Sensing of Sparse Tensors Friedland, Shmuel; Li, Qun; Schonfeld, Dan
IEEE transactions on image processing,
2014-Oct., 2014-Oct, 2014-10-00, 20141001, Letnik:
23, Številka:
10
Journal Article
Recenzirano
Compressive sensing (CS) has triggered an enormous research activity since its first appearance. CS exploits the signal's sparsity or compressibility in a particular domain and integrates data ...compression and acquisition, thus allowing exact reconstruction through relatively few nonadaptive linear measurements. While conventional CS theory relies on data representation in the form of vectors, many data types in various applications, such as color imaging, video sequences, and multisensor networks, are intrinsically represented by higher order tensors. Application of CS to higher order data representation is typically performed by conversion of the data to very long vectors that must be measured using very large sampling matrices, thus imposing a huge computational and memory burden. In this paper, we propose generalized tensor compressive sensing (GTCS)-a unified framework for CS of higher order tensors, which preserves the intrinsic structure of tensor data with reduced computational complexity at reconstruction. GTCS offers an efficient means for representation of multidimensional data by providing simultaneous acquisition and compression from all tensor modes. In addition, we propound two reconstruction procedures, a serial method and a parallelizable method. We then compare the performance of the proposed method with Kronecker compressive sensing (KCS) and multiway compressive sensing (MWCS). We demonstrate experimentally that GTCS outperforms KCS and MWCS in terms of both reconstruction accuracy (within a range of compression ratios) and processing speed. The major disadvantage of our methods (and of MWCS as well) is that the compression ratios may be worse than that offered by KCS.
Introduction of New Associate Editors Schonfeld, Dan
IEEE transactions on circuits and systems for video technology,
2/2015, Letnik:
25, Številka:
2
Journal Article
Editor-in-Chief Message Schonfeld, Dan
IEEE transactions on circuits and systems for video technology,
2015-Feb., 2015-2-00, Letnik:
25, Številka:
2
Journal Article
Recenzirano
Odprti dostop
It has been one year since i assumed my current role as Editor-in-Chief (EIC) of the IEEE Transactions on Circuits and Systems for Video Technology (CSVT). It has been an exciting year for me and the ...CSVT community and I would like to take a moment to recap some of the major milestones for the transactions over the past year.
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