IET Image Processing
Volume 14, Issue 2, 07 February 2020
Volumes & issues:
Volume 14, Issue 2
07 February 2020
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- Author(s): Antoni Buades ; Jose-Luis Lisani ; Ana Belen Petro ; Catalina Sbert
- Source: IET Image Processing, Volume 14, Issue 2, p. 211 –219
- DOI: 10.1049/iet-ipr.2019.0814
- Type: Article
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The authors present a method for the enhancement of backlit images, i.e. images in which the main source of light is behind the photography subject. These images contain, simultaneously, very dark and very bright regions. In this situation, a single tone mapping function is unable to enhance the whole image. They propose the use of several such tone mappings, some of them enhancing the dark regions while others enhancing the bright regions, and then the combination of all these results using an image fusion algorithm. Qualitative and quantitative results confirm the validity of the proposed method.
- Author(s): Jun Ye and Xian Zhang
- Source: IET Image Processing, Volume 14, Issue 2, p. 220 –230
- DOI: 10.1049/iet-ipr.2019.0803
- Type: Article
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Hyperspectral images (HSIs) restoration is an important preprocessing step. The spectral vectors in HSI can be separated into different classification based on the land-covers, which means the spectral space can be regarded as the union of several low-rank subspaces. Subspace low-rank representation (SLRR) is powerful in exploring the inner low-rank structure and has been applied for HSI restoration. However, the traditional SLRR only seek for the rank-minimum representation under a given dictionary, which may treat the structured sparse noise as inherent low-rank components. In addition, the SLRR framework cannot make full use of the spatial information. In this study, a framework named subspace representation with low-rank constraint and spatial-spectral total variation is proposed for HSI restoration. In which, an artificial rank constraint is introduced to control the rank of the representation result, which can improve the removal of the structured sparse noise and exploit the intrinsic structure of spectral space more effectively. Meanwhile, the spatial-spectral total variation regularisation is applied to enhance the spatial and spectral smoothness. Several experiments conducted in simulated and real HSI datasets demonstrate that the proposed method can achieve a state-of-the-art performance both in visual quality and quantitative assessments.
- Author(s): Shifei Ding ; Songhui Shi ; Weikuan Jia
- Source: IET Image Processing, Volume 14, Issue 2, p. 231 –235
- DOI: 10.1049/iet-ipr.2018.5977
- Type: Article
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Fingerprint classification is one of the core steps of fingerprint recognition and directly relates to the accuracy of recognition. For this reason, a fingerprint classification method based on Twin Support Vector Machine (TWSVM) is studied. First, the Gabor filter is used to extract texture features from fingerprint images. Second, a multi-class model based on TWSVM is constructed by using the ‘one-versus-all’ strategy and the binary tree method, respectively. The quantum particle swarm optimisation algorithm is used to optimise the parameters in the model. Then the fingerprints are divided into five categories using the optimised model. Finally, the classification model is evaluated using fingerprint images from the NIST-4 database. The experimental results show that applying the TWSVM to fingerprint classification can get good classification results.
- Author(s): Chengfeng Jian and Junjie Li
- Source: IET Image Processing, Volume 14, Issue 2, p. 236 –244
- DOI: 10.1049/iet-ipr.2019.1068
- Type: Article
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Focus on the field of dynamic gesture recognition, there is a problem of the action, is difficult to recognise when multiple fingertips move in a small range (rotation, grabbing), the authors proposed a method to get the recognition results with high robustness in real-time. Firstly, they proposed the concavity kernel accumulation algorithm (CKA) to cluster corners in an image. Secondly, they deem CKA as a region proposal generator and combined it with convolutional neural network to detect fingertips. Thirdly, they proposed the global nearest neighbour point matching algorithm to match fingertips from two frames. Finally, the long short-term memory is used in multi-trajectory recognition to get the results of gesture recognition. Experiments show that their method could recognise multi-trajectory gestures accurately, furthermore, it can run in real time (20 FPS) without graphics processing unit (GPU).
- Author(s): Achraf Djerida ; Zhonghua Zhao ; Jiankang Zhao
- Source: IET Image Processing, Volume 14, Issue 2, p. 245 –255
- DOI: 10.1049/iet-ipr.2018.6095
- Type: Article
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This study presents a foreground detection method capable of robustly estimating the background under the presence of dynamic effects. The key contribution of this study is the use of the dynamic principal component analysis to model the serial correlation between successive frames and construct a robust pixel-based background model. The frames are normalised in hue, saturation and value colour space to reduce the effect of illumination changes. To restrict the background model, kernel density estimation is used to identify the distribution of the background time-lagged data matrix and then confidence interval limits are used to determine the corresponding detection thresholds. The foreground is detected using background subtraction. This method is tested on several common sequences such as CDnet 2014, ETSI 2014 and MULTIVISION 2013. The authors also hold comparisons based on quantitative metrics with several state-of-the-art methods. Experimental results show that their method outperforms some state-of-the-art methods and has comparable performance with some depth-based methods.
- Author(s): Hyunguk Choi ; Kin Choong Yow ; Moongu Jeon
- Source: IET Image Processing, Volume 14, Issue 2, p. 256 –266
- DOI: 10.1049/iet-ipr.2019.0334
- Type: Article
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Multi-target tracking in a non-overlapping camera network is an active research field, and one of the important problems in it is the person re-identification problem. In this study, the authors propose an approach to improve the performance of the backbone model in the person re-identification. Their approach focuses on training a fusion model with a shallow model and making hard triplets with relationship matrices quickly and efficiently. The proposed approach is simple, but it improves the performance of the backbone. In addition, the hard triplet mining in their process is much faster than the conventional approach. Experimental evaluation shows that the proposed approach can improve the performances of the backbone model. The proposed approach improves rank-1 and mean average precision (mAP) performance by more than 12.54 and 15.44%, respectively, over the backbone models in the Market1501 and DukeMTMC-reID dataset. The approach also achieves competitive performances compared with state-of-the-art approaches.
- Author(s): Ziwei Wei ; Yulong Qiao ; Xiao Lu
- Source: IET Image Processing, Volume 14, Issue 2, p. 267 –278
- DOI: 10.1049/iet-ipr.2018.6629
- Type: Article
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In this study, the authors present a novel level set method for infrared image segmentation. Local region-based models can fit intensity inhomogeneity partly but they are sensitive to local window scale. To deal with it, they embed an heat diffusion process in conventional level set evolution and convert heat to a part of data term in level set energy function. Besides, bias field model can extract the local intensity clustering property of the image. Therefore, the proposed method can deal with the interference of intensity inhomogeneity and complex background if appropriate seeded pixels are selected. Finally, the energy functional is minimised by a combinatorial optimal algorithm in a graph model to get a global optimal solution and accelerate the level set evolution implementation. The experiments show that the proposed method is robust to parameter setting, noise, and initial contour position. The comparisons on a large quantity of infrared image datasets with standard level set methods also demonstrate the efficiency of the proposed method.
- Author(s): Pramaditya Wicaksono ; Muhammad Afif Fauzan ; Septian Galih Widhi Asta
- Source: IET Image Processing, Volume 14, Issue 2, p. 279 –288
- DOI: 10.1049/iet-ipr.2018.6044
- Type: Article
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Sentinel-2A accuracy for benthic habitat composition mapping was tested and compared to ALOS AVNIR-2. Aerial image acquired using custom-made unmanned aerial vehicle was used to train and validate the model. The mapping was conducted regardless of the benthic class and at individual benthic class. Benthic habitat class spatial distribution was obtained using the combination of image segmentation and classification tree analysis. The aerial image was interpreted based on the percentage of the constructed and non-constructed classes. The constructed class includes coral reefs, dead coral, seagrass, and macroalgae, while non-constructed class covers carbonate sand, rock, and rubble. Sentinel-2A produced higher accuracy (92%) than ALOS AVNIR-2 (78%) for benthic habitat spatial distribution mapping. However, in the empirical modelling of benthic habitat composition, ALOS AVNIR-2 (SE 23–24%) produced slightly better accuracy than Sentinel-2A (SE 23–27%). Several factors affected the low accuracy, which include the sub-pixel mixing of benthic habitat and constructed class, the delay between dates of acquisition, and radiometric quality of the images. Since the fundamental relationship between reflectance value and the percentage of the constructed class has been justified and consistent, given more experiments it has the potential to predict benthic habitat composition with higher accuracy in the future.
- Author(s): Nail Alaoui ; Amel Baha Houda Adamou-Mitiche ; Lahcène Mitiche
- Source: IET Image Processing, Volume 14, Issue 2, p. 289 –296
- DOI: 10.1049/iet-ipr.2019.0566
- Type: Article
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This study presents a new approach for recovering an image perturbed by salt and pepper noise (SPN) using a hybrid genetic algorithm (HGA) at all densities, called effective HGA (EHGA). The main contribution of the proposed algorithm is combining the genetic algorithm with image denoising methods that are integrated into the population to achieve rapid convergence. The idea is to evolve a group of individuals into a number of iterations using crossover and mutation operators. This approach evolves a set of images rather than a set of parameters from the filters. Experimental results of simulation on different images using peak signal-to-noise ratio, structural similarity index metric, image enhancement factor and Universal Quality Index show that the proposed algorithm outperforms other methods in removing the SPN qualitatively and quantitatively if the noise density is moderate and high. EHGA also preserves important features such as texture and corners of the image.
- Author(s): Himanshu Kumar ; Sumana Gupta ; Venkatesh K. Subramanian
- Source: IET Image Processing, Volume 14, Issue 2, p. 297 –309
- DOI: 10.1049/iet-ipr.2019.0577
- Type: Article
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Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF.
- Author(s): Claudia-Victoria López-Torres ; Sebastián Salazar Colores ; Kevin Kells ; Jesús-Carlos Pedraza-Ortega ; Juan-Manuel Ramos-Arreguin
- Source: IET Image Processing, Volume 14, Issue 2, p. 310 –317
- DOI: 10.1049/iet-ipr.2019.0854
- Type: Article
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Wavelet transform profilometry is a three-dimensional (3D) reconstruction method based on the structured light technique of fringe pattern projection, widely used because it is a non-invasive, high-performance 3D reconstruction method. The presence of shadows created by the object in the image capture process is an obstacle in obtaining accurate 3D reconstructions, as they add noise to the phase data, leading to artefacts in object reconstruction, even when using robust phase-unwrapping algorithms. Since shadows present diverse intensities and shapes, detecting and eliminating their effects are challenging tasks. This work presents a novel method to detect shadow regions and reduce their effects in 3D reconstruction. The proposed method uses coloured fringe patterns to detect the shadows and mathematical morphology to condition the outlines of the shadow regions. The shadow outline information is used to interpolate the background-plane fringe pattern onto the captured scene, where the shadows are detected. The mean squared error (MSE) of the reconstructed objects is reduced to 25% of the MSE without shadow removal, on an average, when using the Bioucas phase-unwrapping method. When using the Ghiglia phase-unwrapping method, the MSE reduction is to 8.3%, on an average, of the MSE in the shadow case.
- Author(s): Xusheng Fang ; Zhenbing Liu ; Mingchang Xu
- Source: IET Image Processing, Volume 14, Issue 2, p. 318 –326
- DOI: 10.1049/iet-ipr.2019.0617
- Type: Article
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Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases. Structural magnetic resonance imaging (MRI) would provide abundant information on the anatomical structure of human organs. Fluorodeoxy-glucose positron emission tomography (PET) obtains the metabolic activity of the brain. Previous studies have demonstrated that multi-modality images could contribute to improve diagnosis of AD. However, these methods need to extract the handcrafted features that demand domain specific knowledge and image processing stage is time consuming. In order to tackle these problems, in this study, the authors propose a novel framework that ensembles three state-of-the-art deep convolutional neural networks (DCNNs) with multi-modality images for AD classification. In detail, they extract some slices from each subject of each modality, and every DCNN generates a probabilistic score for the input slices. Furthermore, a ‘dropout’ mechanism is introduced to discard low discrimination slices of the category probabilities. Then average reserved slices of each subject are acquired as a new feature. Finally, they train the Adaboost ensemble classifier based on single decision tree classifier with the MRI and PET probabilistic scores of each DCNN. Evaluations on Alzheimer's Disease Neuroimaging Initiative database show that the proposed algorithm has better performance compared to existing method, the algorithm proposed in this study significantly improved the classification accuracy.
- Author(s): Hao-Tian Wu ; Qi Huang ; Yiu-ming Cheung ; Lingling Xu ; Shaohua Tang
- Source: IET Image Processing, Volume 14, Issue 2, p. 327 –336
- DOI: 10.1049/iet-ipr.2019.0423
- Type: Article
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Contrast enhancement (CE) of medical images is helpful to bring out the unclear content in the interested regions. Recently, reversible CE has been proposed so that the original version of a contrast-changed image can be exactly recovered. This property can be used to save storage space or facilitate the archiving system. To enhance the regions of interest (ROI) without introducing visual distortions, the technique of image segmentation (e.g. using Otsu's method) has been used to obtain the background before conducting the CE process. To segment the ROI more accurately, an interactive algorithm called GrabCut is employed in the proposed scheme. In addition, a new preprocessing strategy is adopted to preserve the image quality through the CE process. Consequently, the content in the selected regions can be better brought out while the reversibility of the CE process is achieved. The experimental results on 30 chest radiograph images and 20 magnetic resonance images have demonstrated the efficacy of the proposed scheme for reversible CE. The evaluation results are provided to show the better performances of the proposed method in achieving CE effects and preserving image quality.
- Author(s): Thiyagarajan Jayaraman and Gowri Shankar Chinnusamy
- Source: IET Image Processing, Volume 14, Issue 2, p. 337 –347
- DOI: 10.1049/iet-ipr.2018.6005
- Type: Article
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This study proposes an enhanced video block matching four-dimensional (V-BM4D) denoising algorithm for highly correlated rain streaks pattern video sequence in high-efficiency video coding (HEVC) encoder. Removal of structural rain streaks from high definition (HD) video sequences are challenging issues in real-time video application. Temporal information of original video sequences is distorted among the successive video frames due to the presence of natural degradation parameters such as fog, smog and rain streaks. Rain streaks are highly correlated structural noise pattern, which affects the nature of video frame compared to other natural degradations. Existing HEVC encoder is employed with in-loop filtering to protect the temporal information of original video sequences from rain streaks pattern. However, in-loop filtering is unable to remove the highly directional oriented rain pattern from video frames. To retain temporal information among the successive video frames, enhanced block matching denoising block is adopted in HEVC coder. The proposed enhanced VBM4D- based in-loop filtering eliminates the various level of rain streaks from the HD video sequences by temporal information prediction in 4D platform. Experimental results demonstrate that the proposed denoising algorithm yields better peak signal to noise ratio value and provides better bit rate saving for various HEVC configuration.
- Author(s): Kazim Yildiz
- Source: IET Image Processing, Volume 14, Issue 2, p. 348 –353
- DOI: 10.1049/iet-ipr.2019.0907
- Type: Article
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Wool and mohair fibres are both animal-based fibres and having circular scales on their microscopic images from the longitudinal view. Although they look very similar in their microscopic view, they show different physical/chemical properties which determine their usage area. Thus, in textile industry, they need to be separated carefully from each other. The separation of wool/mohair fibres is an important issue and can be performed with human eye by using the microscopic images, that is not time/cost effective and not objective. The novelty of the presented study is to design an objective, easy, rapid, time and cost-effective method in order to separate wool fibre from mohair fibre by using a texture analysis based identification method. For this purpose, microscopic images of both wool and mohair fibres were preprocessed as the texture images. Local binary pattern-based feature extraction process and deep learning were separately used to get determinative information from the fibres. In order to identify the samples, the classification based method was completed. Experimental results indicated that an accurate texture analysis for this kind of animal fibres is possible to identify wool and mohair fibres by using deep learning and machine learning with 99.8% and 90.25% accuracy rates, respectively.
- Author(s): Karnam Silpaja Chandrasekar and Planisamy Geetha
- Source: IET Image Processing, Volume 14, Issue 2, p. 354 –365
- DOI: 10.1049/iet-ipr.2018.5555
- Type: Article
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Thresholding for segmentation is an important key step and necessary process in various applications. Estimating an accurate threshold value for a complex and coarse image is computationally expensive and lacks accuracy and stability. This study is aimed at developing a general histogram–entropy-based thresholding method, referred as our HEBT method, for fast and efficient automatic threshold value evaluation. In the proposed method, the probability density function and Shannon entropy derived from 1D bimodal histogram have been used to find the optimal threshold values automatically. The proposed method implemented with a three-frame differencing segmentation technique has been tested on real-time datasets – change detection 2012, change detection 2014, and Wallflower – to identify pedestrians and vehicles. The performance of our HEBT method has been compared with six state-of-the-art automatic thresholding methods. The experimental segmented image results confirmed that our HEBT method is more adaptable and better suited for real-time systems with severe challenging conditions of great variations. Further, the new HEBT method achieved the best segmentation results with highest values of several performance parameters, i.e. recall, precision, similarity, and f-measure. Interestingly, the computation time is the lowest for the proposed method than the state-of-the-art methods, promising its application for a fast and effective image segmentation.
- Author(s): Sethuraman Ponni alias Sathya and Srinivasan Ramakrishnan
- Source: IET Image Processing, Volume 14, Issue 2, p. 366 –375
- DOI: 10.1049/iet-ipr.2019.0341
- Type: Article
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This study is intended to protect video data and watermark from unauthorised access. The proposed methodology accentuates two new algorithms, namely structural similarity index metric–absolute difference metric (SSIM-AMD) based non-redundant frame identification (NRFI) and entropy–AMD based keyframe selection (KFS) to reduce the challenges posed by traditional discrete wavelet transform–singular value decomposition. Traditional techniques embed the entire watermark to all existing frames in the video, which is cumbersome and time-consuming. In this methodology, NRFI algorithm is applied to segregate the redundant and non-redundant frames to specific database. The KFS algorithm is used to identify suitable keyframes. DWT is applied into keyframes, which decomposes the frames into subbands. The middle band is selected for embedding. The principal component of watermark image block is embedded into identified keyframes in the video. The chaotic map is adapted to reorder the watermark block for improving the authentication level of the watermarking. The ant colony optimization (ACO) technique is adapted to select the suitable scaling factor for watermarking process. The principal component analysis technique is employed for avoiding false-positive attacks. Experimental results show the proposed methodology can withstand image processing, video processing, false-positive attacks and produces good results in terms of perceptual quality and robustness.
- Author(s): Sudhir Khare ; Manvendra Singh ; Brajesh Kumar Kaushik
- Source: IET Image Processing, Volume 14, Issue 2, p. 376 –383
- DOI: 10.1049/iet-ipr.2019.0764
- Type: Article
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Border military surveillance is one of the demanding and challenging tasks for any nation. Thermal (infrared) camera, which works on the infrared domain, provides complete visual sequences even in pitch dark night conditions. When the video is recorded from a thermal camera mounted on a vehicle, the output video is unstabilised with poor visual quality due to unintentional camera motion. These unwanted motions also introduce smear. Furthermore, there may be situations when a camera is moved intentionally to capture target. This study proposes a fast and robust algorithm for auto stabilisation of videos with smear removal while keeping the intentional motion of camera. This algorithm is developed under the framework of speeded up robust features matching. The proposed algorithm is capable of correcting both motions, i.e. translation as well as rotational. Quality improvement of up to 21 dB is achieved in the stabilised output videos.
- Author(s): Xichen Yang ; Tianshu Wang ; Genlin Ji
- Source: IET Image Processing, Volume 14, Issue 2, p. 384 –396
- DOI: 10.1049/iet-ipr.2019.0750
- Type: Article
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Image quality assessment (IQA) is a meaningful research topic to meet the increasing demand of high-quality image. The degradation of image quality will cause changes in image structural information. Meanwhile, human visual system is sensitive to changes in structural information. This finding motivates us to utilise structural information for proposing IQA method which is consistent with human visual perception. Recently, IQA methods are mainly focused on individual image type, e.g. natural image or screen content image (SCI), thus, the authors proposed a novel no-reference IQA method which can be suitable for both natural image and SCI. The proposed method is based on structural information analysis. For each image, they first obtain the grey-scale fluctuation maps (GFMs) in four detection directions. After that, the grey-scale fluctuation direction map (GFD) of certain image can be acquired via its GFMs. Based on the GFMs and GFD, the structural features of each image are extracted, and then collected and transformed to feature vectors. Subsequently, the IQA model is trained by support vector regression. The experimental results on the public databases demonstrate the proposed method can predict image quality accurately for both natural image and SCI, and the performance is competitive with prevalent methods.
- Author(s): Hassan Elkamchouchi ; Wessam M. Salama ; Yasmine Abouelseoud
- Source: IET Image Processing, Volume 14, Issue 2, p. 397 –406
- DOI: 10.1049/iet-ipr.2018.5250
- Type: Article
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Two new encryption algorithms for secure video transmission are proposed in this paper. The two algorithms employ different types of chaotic maps to generate the keystream for encrypting the video frames. Both algorithms involve a substitution step and a permutation step to achieve confusion and diffusion requirements. For efficient transmission, the video file is compressed before being encrypted. In the basic implementation of both algorithms, MPEG-2 standard is used for compression. However, the algorithms are shown to be compliant with other compression techniques. In the permutation step, the effect of the block size used in the shuffling process is examined. Smaller blocks result in increasing the processing time, while reducing both the correlation between adjacent pixels and the peak-signal-to noise ratio of an encrypted frame. The use of a Feistel structure is investigated to enhance security and its negative impact on the encryption time is demonstrated. The experimental results of the two proposed schemes confirm that they represent different tradeoffs between security and computational efficiency. Both schemes are sensitive to slight variations in the encryption key as apparent from the obtained differential measures. The conducted comparative study shows the competitiveness of the proposed schemes to existing schemes in literature.
Backlit images enhancement using global tone mappings and image fusion
Hyperspectral image restoration by subspace representation with low-rank constraint and spatial-spectral total variation
Research on fingerprint classification based on twin support vector machine
Real-time multi-trajectory matching for dynamic hand gesture recognition
Background subtraction in dynamic scenes using the dynamic principal component analysis
Training approach using the shallow model and hard triplet mining for person re-identification
Heat diffusion embedded level set evolution for infrared image segmentation
Assessment of Sentinel-2A multispectral image for benthic habitat composition mapping
Effective hybrid genetic algorithm for removing salt and pepper noise
Blur parameter locus curve and its applications
Improving 3D reconstruction accuracy in wavelet transform profilometry by reducing shadow effects
Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis
Reversible contrast enhancement for medical images with background segmentation
Investigation of filtering of rain streaks affected video sequences under various quantisation parameter in HEVC encoder using an enhanced V-BM4D algorithm
Identification of wool and mohair fibres with texture feature extraction and deep learning
Highly efficient neoteric histogram–entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection
Non-redundant frame identification and keyframe selection in DWT-PCA domain for authentication of video
Fast and robust video stabilisation with preserved intentional camera motion and smear removal for infrared video
No-reference image quality assessment via structural information fluctuation
New video encryption schemes based on chaotic maps
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