Signal Image and Video Processing

Publisher: Springer Verlag

Description

  • Impact factor
    1.02
  • 5-year impact
    0.00
  • Cited half-life
    3.40
  • Immediacy index
    0.12
  • Eigenfactor
    0.00
  • Article influence
    0.00
  • Other titles
    Signal, image and video processing (Online), SIViP
  • ISSN
    1863-1703
  • OCLC
    130401260
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as arXiv.org
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, an improved contour-fitting adaptive snake, namely a rectification-conducted adaptive snake (RCA-snake) is proposed for segmenting complex-boundary objects from the textured background. Based on the snaxels’ initialization, the RCA-snake comprises two steps. Initially, edge-conducted evolution (ECE) is employed for adaptations of model coefficients that accommodate ECE itself to the characteristics of salient edges for enhancing curve fitting in tracking. Following ECE, direction-induced rectification evolution corrects the boundary-unmatched snake fragments by handling the initializations of their snake-force direction and tensile-force weighting. The two steps of the RCA-snake are coordinated to enhance control of the snake model for segmenting an object with high-curvature boundaries. Simulation results demonstrate that better object-boundary coincidence can be obtained via the RCA-snake than other snake models, such as the gradient vector flow (GVF) and improved GVF, e.g., NGVF, in segmenting a complex-boundary object from textured-background images.
    Signal Image and Video Processing 12/2014;
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    ABSTRACT: Inter-subject variability plays an important role in the performance of facial expression recognition. Therefore, several methods have been developed to bring the performance of a person-independent system closer to that of a person-dependent one. These techniques need different samples from a new person to increase the generalization ability. We have proposed a new approach to address this problem. It employs the person’s neutral samples as prior knowledge and a synthesis method based on the subspace learning to generate virtual expression samples. These samples have been incorporated in learning process to learn the style of the new person. We have also enriched the training data set by virtual samples created for each person in this set. Compared with previous studies, the results showed that our approach can perform the task of facial expression recognition effectively with better robustness for corrupted data.
    Signal Image and Video Processing 12/2014;
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    ABSTRACT: This paper presents a robust and computationally efficient genetic algorithm for color classification. It designs well-fitted color space prolate spheroids (ellipsoids) that envelop the training pixels. The ellipsoids are then used to classify unlabeled image pixels in accordance with their color, in order to partition the image. The color classification algorithm described here has very low error rates, boasts very high operational speed, and permits trading higher indecision rates for lower rates of misclassification. The performance of the color classifier developed in this paper is compared with those of the support vector machine (SVM) and the nearest-neighbor (kNN) classifiers. It has been shown that our color classifier outperforms SVM and kNN for partitioning of color images that contain several closely spaced color classes. It has higher correct classification, lower misclassification, and significantly reduced operational latency in comparison with color classifiers based on kNN and SVM.
    Signal Image and Video Processing 10/2014;
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    ABSTRACT: There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.
    Signal Image and Video Processing 10/2014;
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    ABSTRACT: Detection and elimination of the shadows of moving objects in video sequences have been one of the major challenges in tracking applications. Since moving shadows cannot be removed from foreground by motion-based background subtraction methods, they lead to confusion and error in moving object tracking. In this paper, a novel classification method based on hierarchical mixture of experts learning for detecting shadows from foreground is proposed. A hierarchical mixture of MLP experts method (HMME) with semi-supervised teacher-directed learning (SSP-HMME) is used. It contains a two-level mixture of experts (ME) system. The main superiority of this method is that it is more robust than state-of-the-art methods in all types of indoor and outdoor environments. The robustness is against the number of light sources, illumination conditions, surface orientations, object sizes, etc., and it is estimated using accuracy rates. The video set has been collected from 7 different datasets. The results of experiments in outdoor and indoor environments show the validity of the method in the improvement on the accuracy of both detection and discrimination rate for moving shadows in video sequences. The results of the experiments show the accuracy rate of 89 % in average in different indoor and outdoor environmental conditions that is about 6 % better than current state-of-the-art methods.
    Signal Image and Video Processing 10/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose a noise reduction algorithm for digital color images using a nonlinear image decomposition approach. Most existing noise reduction methods do not adequately consider spatial correlation of color noise in digital color images. Color noise components in color images captured by digital cameras are observed as irregular grains with various sizes and shapes, which are spatially randomly distributed. We use a modified multiscale bilateral decomposition to effectively separate signal and mixed-type noise components, in which a noisy input image is decomposed into a base layer and several detail layers. A base layer contains strong edges, and most of noise components are contained in detail layers. Noise components in detail layers are reduced by an adaptive thresholding function. We obtain a denoised image by combining a base layer and noise-reduced detail layers. Experimental results show the effectiveness of the proposed algorithm, in terms of both the peak signal-to-noise ratio and visual quality.
    Signal Image and Video Processing 10/2014;
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    ABSTRACT: Fall detection is one of the most important health care issues for elderly people at home, which can lead to severe injuries. With the advances and conveniences in computer vision in the last few decades, computer vision-based methods provide a promising way for detecting falls. In this paper, we propose a novel vision-based fall detection method for monitoring elderly people in house care environment. The foreground human silhouette is extracted via background modeling and tracked throughout the video sequence. The human body is represented with ellipse fitting, and the silhouette motion is modeled by an integrated normalized motion energy image computed over a short-term video sequence. Then, the shape deformation quantified from the fitted silhouettes is used as the features to distinguish different postures of the human. Finally, different postures are classified via a multi-class support vector machine and a context-free grammar-based method that provides longer range temporal constraints can verify the detected falls. Extensive experiments show that the proposed method has achieved a reliable result compared with other common methods.
    Signal Image and Video Processing 09/2014; 8(6).
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    ABSTRACT: Ship tracking plays a key role in inland waterway closed circuit television (CCTV) video surveillance. Although much success has been demonstrated in the construction of effective appearance model, numerous issues remain to be addressed due to factors such as pose and illumination change, partial or full occlusion, abrupt scale variation and motion blur. In this paper, we firstly inherit the intrinsical merits of subspace representation which demonstrates robustness to partial or full occlusion, pose and illumination variation. A very sparse measurement matrix is adopted to extract the features for the appearance model. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background. Secondly, in order to increase the randomness of the random projection matrix and further reduce memory load, we develop our ship appearance model based on fern features in the compressed domain. Thirdly, we track the scale by enhancing the tracker with a mechanism of feedback. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm achieves favorable performance in terms of efficiency and accuracy.
    Signal Image and Video Processing 09/2014; 8(6).
  • Signal Image and Video Processing 09/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction. The 2-D discrete wavelet transform that can express the variance in frequency and direction of textures, and the contourlet transform that represents boundaries even more accurately are applied in our algorithm. The whole segmentation algorithm contains three stages. First, an adaptive color quantization scheme is utilized to obtain a coarse image representation. Then, the tiny regions are combined based on color information. Third, the proposed energy transform function is used as a criterion for image segmentation. The motivation of the proposed method is to obtain the complete and significant objects in the image. Ultimately, according to our experiments on the Berkeley segmentation database, our techniques have more reasonable and robust results than other two widely adopted image segmentation algorithms, and our method with contourlet transform has better performance than wavelet transform.
    Signal Image and Video Processing 07/2014;
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    ABSTRACT: In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.
    Signal Image and Video Processing 07/2014;
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    ABSTRACT: A novel instantaneous frequency-based time–frequency representation is proposed for the analysis of multicomponent signals. The concept of frequency translation is innovatively combined with the empirical mode decomposition algorithm to formulate an iterative procedure, referred to as the iterative empirical mode decomposition, to separate the components present in a signal at a suitably selected frequency resolution. The instantaneous frequency and amplitude estimated on the separated components are used to form the new time–frequency representation. The iterative empirical mode decomposition is assessed for component resolvability, and the performance of the aforementioned time–frequency representation is compared with several other time–frequency representations based on visual inspection and using objective criteria. The Hilbert spectrum formed using the iterative empirical mode decomposition not only provides high concentration of energy about the components’ instantaneous frequencies at high signal-to-noise ratio, but also good resolution while keeping the interference terms at a minimum.
    Signal Image and Video Processing 07/2014;
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    ABSTRACT: Nonnegative matrix factorization (NMF) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields. Over the years, several algorithms have been proposed to improve the convergence of iterative algorithms in NMF, such as the multiplicative, the projected gradient, the second-order algorithms and recently the projected conjugate gradient algorithms. However, most of these procedures suffer from either slow convergence or numerical instability. In this paper, we propose a projected hybrid conjugate gradient algorithm which avoids the slow convergence problem by using orthogonal searching direction at each step, which ensures that is a descent direction, also avoids jamming that occurs in other conjugate gradient methods such as Fletcher–Reeves. We presented experimental results on both synthetic and real-world datasets that demonstrate the superiority of our algorithm, both in terms of better approximations as well as computational efficiency.
    Signal Image and Video Processing 06/2014;