Publication Date: 2007
IEEE Transactions on Consumer Electronics (00983063)53(3)pp. 1117-1124
A new motion compensated deinterlacing method using forward and backward motion estimation is proposed in this paper. Bi-directional motion estimation is performed using two previous and two subsequent fields. The motion estimator uses pre-filtering prior to motion estimation for the current and the subsequent two fields. The motion estimator finds a single optimal matching block in the same or opposite parity reference fields. Motion compensation is done according to the amount of vertical motion within the reference fields to achieve the highest vertical resolution improvement. A novel technique to prevent the appearance of visual artifacts in the presence of fast-moving objects is proposed. Experimental results show that the proposed method performs better than the conventional deinterlacing methods, based on objective and subjective criteria. © 2007 IEEE.
Block matching has been widely used for block motion estimation; however most of the block matching algorithms impose heavy computational load to the system, and require much time for execution. This problem prevents using them in time critical applications. In this paper, a new approach to block matching technique is presented, which has small computational complexity as well as high accuracy. The main assumption of the algorithm is that, all the pixels of a block move equally by a linear motion. Experimental results show the feasibility and effectiveness of the proposed algorithm. © 2008 IEEE.
Publication Date: 2025
Machine Learning (15730565)114(3)
Many machine learning algorithms use Euclidean distance as a common metric to calculate similarities between data. However, Euclidean distance is not valid when data lie on a manifold with non-zero curvatures. Therefore, we propose a new non-parametric approach that uses curvatures to calculate distances. Curvature is an appealing feature for this purpose since it is not altered by isometries. In this paper, we propose two formulas for measuring distances on a manifold with constant curvature, and their validities are proven using the theorems of differential geometry. Utilizing these formulas, an algorithm is developed to measure the distance between a point and the center of a class. In the proposed algorithm geodesies are divided into equal linear segments, assuming that the curvature remains constant within each segment. This assumption is shown to be valid in many data spaces experimentally. Observed data near each segment are used to estimate curvatures and calculate distances within each segment. Finally, the total distance is computed by summing up the non-Euclidean lengths of all segments. The proposed method is a supervised version of k-means, named non-Euclidean centers. The correctness of the proposed method is validated using the Riemann tensor and its related theorems in differential geometry. Furthermore, experimental results show that our method performs well in real-world data classification applications. The space of symmetric positive definite matrices, which is often endowed with non-Euclidean metrics that induce some curvature, is used for input data representations. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2025.
Publication Date: 2011
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743)6594(PART 2)pp. 98-107
N-grams are the basic features commonly used in sequence-based malicious code detection methods in computer virology research. The empirical results from previous works suggest that, while short length n-grams are easier to extract, the characteristics of the underlying executables are better represented in lengthier n-grams. However, by increasing the length of an n-gram, the feature space grows in an exponential manner and much space and computational resources are demanded. And therefore, feature selection has turned to be the most challenging step in establishing an accurate detection system based on byte n-grams. In this paper we propose an efficient feature extraction method where in order to gain more information; both adjacent and non-adjacent bi-grams are used. Additionally, we present a novel boosting feature selection method based on genetic algorithm. Our experimental results indicate that the proposed detection system detects virus programs far more accurately than the best earlier known methods. © 2011 Springer-Verlag.
In this paper, a new robust digital image watermarking algorithm based on Joint DWT-DCT Transformation is proposed. The imperceptibility is provided as well as higher robustness against common signal processing attacks. A binary watermarked image is embedded in certain sub-bands of a 3-level DWT transformed of a host image. Then, DCT transform of each selected DWT sub-band is computed and the PN-sequences of the watermark bits are embedded in the coefficients of the corresponding DCT middle frequencies. In extraction stages, the watermarked image, which maybe attacked, is first preprocessed by sharpening and Laplassian of Gaussian filters. Then, the same approach as the embedding process is used to extract the DCT middle frequencies of each sub-band. Finally, correlation between mid-band coefficients and PN-sequences is calculated to determine watermarked bits. Experimental results show that the proposed method improved the performance of the watermarking algorithms which are based on the joint of DWT-DCT. © 2008 IEEE.
This paper presents a new robust digital image watermarking technique based on Discrete Cosine Transform (DCT) and neural network. The neural network is Full Counter propagation Neural Network (FCNN). FCNN has been used to simulate the perceptual and visual characteristics of the original image. The perceptual features of the original image have been used to determine the highest changeable threshold values of DCT coefficients. The highest changeable threshold values have been used to embed the watermark in DCT coefficients of the original image. The watermark is a binary image. The pixel values of this image are inserted as zero and one values in the DCT coefficients of the image. The implementation results have shown that this watermarking algorithm has an acceptable robustness versus different kinds of watermarking attacks. © 2008 IEEE.
Publication Date: 2025
IEEE Access (21693536)13pp. 44607-44619
Alzheimer's disease (AD) presents a significant global health challenge, necessitating accurate and early prediction methods for effective intervention and treatment planning. In this work, a novel approach to meta-learning for the prediction of AD is proposed, which leverages the combined power of neural processes (NPs) and transformer architectures. We introduce a framework that integrates NPs with a transformer encoder to model the complex temporal dependencies inherent in longitudinal health data, where our model learns to capture subtle patterns and variations indicative of disease progression. The novelty of our approach lies in the fusion of NPs, renowned for their ability to model stochastic processes, with transformer architectures, known for their ability to capture long-range dependencies. This combination enables our model to effectively adapt to individual patient trajectories and generalize across diverse populations, enhancing its predictive performance and robustness. We trained our proposed model with the Alzheimer's Disease Prediction Of Longitudinal Evolution dataset (TADPOLE), which contains three classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. The experimental results demonstrate that the proposed model enhances the prediction of these models in terms of mAUC, Recall, and Precision by 0.937 - 0.014, 0.920 - 0.010, and 0.923 - 0.009, respectively. These findings prove the efficacy of the proposed framework in accurately predicting the progression of AD, highlighting its potential for early detection and personalized treatment strategies. © 2013 IEEE.
Publication Date: 2014
Multimedia Systems (14321882)20(2)pp. 215-226
In this paper a data hiding method is proposed based on the combination of a secret sharing technique and a novel steganography method using integer wavelet transform. In this method in encoding phase, first a secret image is shared into n shares, using a secret sharing technique. Then, the shares and Fletcher-16 checksum of shares are hidden into n cover images using proposed wavelet based steganography method. In decoding phase, t out of n stego images are required to recover the secret image. In this phase, first t shares and their checksums are extracted from t stego images. Then, by using the Lagrange interpolation the secret image is revealed from the t shares. The proposed method is stable against serious attacks, including RS and supervisory training steganalysis methods, it has the lowest detection rate under global feature extraction classifier examination compared to the state-of-the-art techniques. Experimental results on a set of benchmarks showed that this method outperforms conventional methods in offering a high secure and robust mechanism for joining secret image sharing and steganography. © 2013 Springer-Verlag Berlin Heidelberg.
In this paper, a new steganography algorithm that combines two different steganography methods, namely Matrix Pattern (MP) and Least Significant Bit (LSB), is presented for RGB images. These two methods use the spatial domain of images for hiding secret messages; however, they differ from each other, fundamentally. The MP method is an algorithm which, firstly, divides the "Cover-Image" into non-overlapping B×B blocks. Then, it hides the data in the 4th through 7th bit layers of the blue layer of the "Cover-Image", by generating unique tixt2 matrix patterns for each character in each block. The LSB method is an algorithm that hides data in the least significant bit of the "Cover-Image" pixels, which has the least visible effect on the transparency of the "Stego-Image". In the proposed algorithm, the first three bit layers, and the 4th to 7th bit layers of the blue layer of the RGB "Cover-Image" is used for hiding the "Message", with LSB and MP methods, respectively. This algorithm has two entrances for the "Message"; one of them can be only text, "Text Message", which is hidden with the MP method. The other one, "Binary Message", can be any digital media, and is hidden with the LSB method. Our simulation and evaluation results show that this new method has a better capacity than the LSB and MP methods, by more than 1.265 and 4.77 times, correspondingly. Our results also indicate that the final "Stego-Image" has a high quality PSNR. © 2016 IEEE.
Publication Date: 2010
Journal of Circuits, Systems and Computers (17936454)19(2)pp. 451-477
In this paper, a novel watermarking technique based on parametric slant-Hadamard transform is presented. Our approach embeds a pseudo-random sequence of real numbers in a selected set of the parametric slant-Hadamard transform coefficients. By exploiting statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. The presented method is capable of increasing the flexibility of the watermarking scheme, where the changes in parameter set help to improve fidelity and robustness against a number of attacks. Experimental results show that the proposed technique is secure and indeed highly robust to these attacks. © 2010 World Scientific Publishing Company.
Publication Date: 2011
Scientific Research and Essays (19922248)6(10)pp. 2119-2128
Digital image watermarking is one of the most important techniques for copyright protection. The robustness and imperceptibility are the basic requirements of digital image watermarking that are contradictory. The key factor that affects both the robustness and imperceptibility is the watermark strength. This paper presents a new method to determine the watermark strength using Reinforcement Learning (RL) in Discrete Cosine Transform (DCT) domain. Thus, finding the watermark strength was formulated as an RL problem. In our study, the defined reinforcement function has two contradictory aspects, the one with positive aspect is with respect to the similarities between the host and watermarked image and the other with negative aspect is with respect to the robustness of the watermark. Therefore, a novel adaptive methodology is introduced to estimate watermark strength to ameliorate both imperceptibility and robustness at the same time. The experimental results show that the proposed RL algorithm for watermark strength estimation improves simultaneously the robustness and imperceptibility of the watermarking scheme. © 2011 Academic Journals.
Publication Date: 2013
Information Sciences (0020-0255)233pp. 109-125
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning. © 2013 Elsevier Inc. All rights reserved.
Publication Date: 2012
Journal of Heuristics (13811231)18(5)pp. 795-819
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms. © Springer Science+Business Media, LLC 2012. © Springer Science+Business Media, LLC 2012.
Background estimation is one of the most challenging phases in extracting foreground objects from video sequences. In this paper we present a background modeling approach that uses the similarity of frames to extract background areas from the video sequence. We use a window over the frames history and compute the similarity between the selected frames of this window as a similarity window. The properties of similarity window depend on the characteristics of the scene and can be adjusted parametrically. Our primary results show that if proper parameters are chosen, this method can give a good approximation of the background model. ©2008 IEEE.
In this paper we propose a new deinterlacing algorithm using motion compensation and directional interpolation. To limit the propagation error that is a major drawback of conventional motion compensated methods, motion estimation is performed using original lines only, for same and opposite parity fields. In addition, a threshold value is used during the search to recognize situations where the motion estimator fails to find an optimal matching block. Enhanced edge-based line average with median filtering is used in these situations. Experimental results show that the proposed method performs better than the traditional motion compensated method, based on objective and subjective criteria. © 2006 IEEE.
Publication Date: 2017
International Journal of Remote Sensing (13665901)38(12)pp. 3608-3634
This article proposes a new algorithm for hyperspectral image classification. The proposed method is a spectral–spatial method based on wavelet transforms, kernel minimum noise fraction (KMNF) and spatial–spectral Schroedinger eigenmaps (SSSE). To overcome the computation complexity, one-dimensional discrete wavelet transform (1D-DWT) is applied in spectral domain. To reduce noise, KMNF coefficients are extracted in wavelet space. To solve time-consuming problem, 2D-DWT coefficients are employed in spatial space. Hence, the combination of 1D-DWT, KMNF, and 2D-DWT is suggested to create SSSE features. The classification is carried out by a Support Vector Machine (SVM) classifier. Experimental results show that classification accuracy and time consumption are effectively improved compared to the state-of-the art reported spectral–spatial SVM-based methods. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2019
Multimedia Tools and Applications (13807501)78(22)pp. 31319-31345
A novel class-dependent joint weighting method is proposed to mine the key skeletal joints for human action recognition. Existing deep learning methods or those based on hand-crafted features may not adequately capture the relevant joints of different actions which are important to recognize the actions. In the proposed method, for each class of human actions, each joint is weighted according to its temporal variations and its inherent ability in extension or flexion. These weights can be used as a prior knowledge in skeletal joints-based methods. Here, a novel human action recognition algorithm is also proposed in order to use these weights in two different ways. First, for each frame of a skeletal sequence, the histogram of 3D joints is weighted according to the contribution of joints in the corresponding class of human action. Second, a weighted motion energy function is defined to dynamically divide the temporal pyramid of actions. Experimental results on three benchmark datasets show the efficiency of proposed weighting method, especially when occlusion occurs. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2011
Journal of Circuits, Systems and Computers (17936454)20(5)pp. 801-819
In this paper, an adaptive digital image watermarking technique using fuzzy gradient on DCT domain is presented. In our approach, the image is divided into separate blocks and the DCT is applied on each block individually. Then, the watermark is inserted in the transform domain and the inverse transform is carried out. We increase the robustness of the watermark by increasing the watermark strength. However, this reduces the fidelity of the watermarking scheme. This is because the fidelity and robustness of watermarking are generally in conflict with each other. To improve the fidelity, a new fuzzy-based method is introduced. In this method, a fuzzy gradient-based mask is generated from the host image. Then, as a post-processing stage, the generated mask is combined with the watermarked image. Experimental results show that the proposed technique has high fidelity as well as high robustness against a variety of attacks. © 2011 World Scientific Publishing Company.
Publication Date: 2022
Computers and Industrial Engineering (03608352)172
Electronic vehicles (EVs) are receiving increasing attention to addressing global warming challenges since fossil fuel is replaced with fuel cell technology. Hence, new challenges arise as demands have increased for using EVs. One of these challenges is the long waiting time of charging EVs spent in queues, especially during peak hours. So, in this study, we aim to propose an efficient method for the electric vehicle charging scheduling problem (EVCSP), which an actual charging station inspires. The most important constraint in this problem is balancing power consumption between charging lines, leading to a limited number of devices that can be charged simultaneously. Also, in this problem, EVs may have interrelationships with each other during the scheduling procedure. So, the estimation of distribution algorithm (EDA) as a competent method in handling the possible relations among decision variables is applied in our proposed hybrid EDA-based solving method. Our proposed method comprises two EDAs, a Markov network-based EDA and a Mallows model-based EDA. It achieves an appropriate schedule and charging line assignment simultaneously while minimizing the total tardiness considering problem constraints. We compared our method with a constraint programming (CP) model and the state-of-art meta-heuristic methods in terms of the objective function value by simulation on a benchmark dataset. Results from the experimental study show significant improvement in solving the introduced EVCSPs. © 2022
Publication Date: 1992
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (15206149)5pp. 309-312
This paper introduces an efficient algorithm that jointly estimates differential time delays and frequency offsets between two signals is introduced. The approach is a two-step procedure. First, the differential frequency offsets are estimated from measurement of the autocorrelation functions of the received and transmitted signals. The time delays are estimated from estimates of the higher-order statistics of the two signals involved. The major advantage of the approach is its remarkably reduced computational complexity over traditional approaches. The experimental results indicate that the algorithm performs better than the traditional methods in most cases of interest in spite of its reduced computational complexity. © 1992 IEEE.
Publication Date: 2017
Intelligent Data Analysis (1088467X)21(2)pp. 427-441
Affective video retrieval systems seek to retrieve video contents concerning their impact on viewers' emotions. These systems typically apply a multimodal approach that fuses information from different modalities to specify the affect category. The main drawback of existing information fusion methods exploited in affective video retrieval systems is that they consider all modalities equally important; hence they ignore conflicts among modalities. In order to address this drawback, a new information fusion method is proposed based on the Dempster-Shafer theory of evidence. This proposed method assigns different weights to modalities based on their correlation and their level of confidence. Experiments are run on the video clips of DEAP dataset. Results indicate that the proposed method outperforms existing evidential information fusion methods significantly. © 2017 - IOS Press and the authors. All rights reserved.
Publication Date: 2012
Artificial Organs (15251594)36(7)pp. 616-628
This article presents an image processing approach dedicated for a blind mobility aid facilitated through visual intracortical electrical stimulation. The method examines a display framework based on the distances related to a scene. The distances of objects to the walker are measured using a size perspective method which uses only one camera without any occlusion effect. The method extracts the information of the closest object to the camera and transfers a sense of distance to a blind walker. The proposed image processing method can estimate the distances of objects within 7.5m of the walker, and alert the presence of the closest object to the person. This new method offers the advantages of information reduction and scene understanding suitable for visual prosthesis. © 2012, the Authors. Artificial Organs © 2012, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
Publication Date: 2024
Journal of Signal Processing Systems (19398115)96(12)pp. 763-777
Deep learning models often lack robustness against adversarial attacks, which may deceive classifiers and limit their use in safety-critical applications, such as pedestrian detection. The robustness of pedestrian detection methods, remains underexplored, and current defenses often prove unsuccessful due to their reliance on deep learning architectures. This paper introduces a pedestrian detection approach specifically designed to enhance robustness against adversarial attacks. Our method’s resilience stems from three key elements: First, it employs a novel hand-crafted feature extraction method that are less susceptible to minor perturbations compared to the irrelevant and vulnerable features extracted by deep learning models. Second, our non-deep model lacks gradients, thereby rendering many gradient-based adversarial attacks, such as FGSM and PGD attacks, ineffective. Third, employing a novel ant colony optimization technique with a tailored evaluation function, which selects resilient feature subsets. Extensive experiments demonstrate that our approach maintains comparable detection accuracy to state-of-the-art methods on clean data while exhibiting robustness against adversarial attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2019
Expert Systems with Applications (0957-4174)119pp. 476-490
Influence maximization is an important issue in social network analysis domain which concerns finding the most influential nodes. Determining the influential nodes is made with respect to information diffusion models. Most of the existing models only contain trust relationships while distrust exist in social networks as well. There exist some drawbacks in limited studies where distrust relationship is involved. The most outstanding drawback is the lack of assessment on the validity of the schemes presented on how influence propagates through distrust relationships in comparison with real word propagation in social networks. In this paper, two schemes are proposed, where based on each, some new models are proposed in two classes: cascade-based and threshold-based. All models of concern here are evaluated in comparison with the benchmark models through two real data sets, the Epinions and Bitcoin OTC. Results obtained indicate the superiority of one of the proposed schemes: when a distrusted user performs an action or adopts an opinion, the target users may tend not to do it. © 2018 Elsevier Ltd
Publication Date: 2018
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (21681163)6(2)pp. 170-181
Purpose: Coronary Computed Tomography Angiography (CCTA) is a promising alternative for high accuracy detection of a wide range of coronary artery diseases. To achieve the anatomical and pathological features of intramuscular coronary arteries with minimal user interaction, we need an automated coronary artery centerline extraction algorithm. Method: This article presents a fully automatic coronary artery centerline tracking algorithm. First, a complex continuous wavelet transform with the Gaussian kernels is used to reduce noise effect. Then, a multiple hypothesis tracking approach is applied to segment 3-D vessel structures. Finally, the tracking procedure is completed by applying a newly presented branch searching approach based on region growing algorithm and a mathematical morphology operation. Results: The performance of the presented method is measured on the publicly available Rotterdam Coronary Artery Algorithm Evaluation Framework. The extraction ability of the algorithm computed by overlap measures averaged over 32 data-sets including overall overlap, overlap until the first error and overlap with the clinically relevant part of the vessel were OV = 85.2%, OF = 75.7% and OT = 98.5%, respectively. Also the average accuracy measurement was 0.26 mm which shows high extraction accuracy with respect to mean voxel size 0.32 × 0.32 × 0.4 mm3. Conclusion: The average coronary artery extraction time was about 8 min per data-set. The experiment results show that our newly developed algorithm achieved high efficiency in coronary artery centerlines tracking for CCTA images. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2016
Computer Methods and Programs in Biomedicine (01692607)132pp. 11-20
Background and objective: Manual assessment of sperm morphology is subjective and error prone so developing automatic methods is vital for a more accurate assessment. The first step in automatic evaluation of sperm morphology is sperm head detection and segmentation. In this paper a complete framework for automatic sperm head detection and segmentation is presented. Methods: After an initial thresholding step, the histogram of the Hue channel of HSV color space is used, in addition to size criterion, to discriminate sperm heads in microscopic images. To achieve an improved segmentation of sperm heads, an edge-based active contour method is used. Also a novel tail point detection method is proposed to refine the segmentation by locating and removing the midpiece from the segmented head. An algorithm is also proposed to separate the acrosome and nucleus using morphological operations. Dice coefficient is used to evaluate the segmentation performance. The proposed methods are evaluated using a publicly available dataset. Results: The proposed method has achieved segmentation accuracy of 0.92 for sperm heads, 0.84 for acrosomes and 0.87 for nuclei, with the standard deviation of 0.05, which significantly outperforms the current state-of-the-art. Also our tail detection method achieved true detection rate of 96%. Conclusions: In this paper we presented a complete framework for sperm detection and segmentation which is totally automatic. It is shown that using active contours can improve the segmentation results of sperm heads. Our proposed algorithms for tail detection and midpiece removal further improved the segmentation results. The results indicate that our method achieved higher Dice coefficients with less dispersion compared to the existing solutions. © 2016 Elsevier Ireland Ltd.
Publication Date: 2013
Computers in Biology and Medicine (00104825)43(5)pp. 587-593
Automatic measurement and quantification of blood vessels' features and detection of vessel landmarks are key steps in the computer-aided diagnosis and diseases monitoring. This work proposes a novel and robust method for detecting vessel landmarks, i.e. bifurcation and crossovers, and measurement of different features, i.e. vessel orientation and vessel diameter as well as bifurcation angle, from the detected vessel network using simple and efficient local vessel pattern operator. The proposed method is applied to the publicly available DRIVE, STARE and ARIA databases and compared with existing state-of-the-art approaches. It shows higher accuracy in detection of vessel landmark and estimation of vessel features. © 2013 Elsevier Ltd.
