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Accelerating convolutional neural network training using ProMoD backpropagation algorithm IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Ahmet Gürhanlı
Convolutional neural networks (CNNs) play an important role in image recognition applications. Fast training of image recognition systems is a crucial point, because the system should be trained for each new image class. These networks are trained using lengthy calculations. Focus of engineering is on obtaining a fast, but stable optimisation method. Momentum technique which is used in backpropagation
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Structure preservation in content-aware image retargeting using multi-operator IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Ankit Garg; Ashish Negi
The evolution of image retargeting technique demands the exploitation of multi-operators since they are capable of preserving the structure and salient objects of the image. However, these multi-operators are mostly based on seam carving with scaling or cropping operators which lead to significant distortions in the retargeted image. This study proposes a new multi-operator scheme which has improved
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Boundary detection using unbiased sparseness-constrained colour-opponent response and superpixel contrast IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Gang Wang; Yong-guang Chen; Min Gao; Suo-chang Yang; Fu-qiang Feng; Bernard De Baets
Boundaries play a crucial role in various image-based tasks, but many existing non-learning-based boundary detection methods underperform in recognising authentic boundaries from a complex background. In this study, the authors address this problem using the sparseness-constrained colour-opponent response and the superpixel contrast. First, building on the biologically inspired colour-opponency mechanism
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Ring oscillator as confusion – diffusion agent: a complete TRNG drove image security IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Rethinam Sivaraman; Sundararaman Rajagopalan; John Bosco Balaguru Rayappan; Rengarajan Amirtharajan
The utility of true random number generators (TRNGs) is not only restricted to session key generation, nonce generation, OTP generation etc. in cryptography. In the proposed work, two ring oscillator (RO) based TRNG structures adopting identical and non-identical ring of inverters have alone been employed for confusion (scrambling) and diffusion (intensity variation) processes for encrypting the greyscale
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Approach for shadow detection and removal using machine learning techniques IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Mohankumar Shilpa; Madigondanahalli Thimmaiah Gopalakrishna; Chikkaguddaiah Naveena
In this work, the authors have proposed a method for shadow detection and removal from videos by utilising methods of machine learning. From literature, various algorithms on shadow detection and removal have been accounted with advantages and disadvantages. Here some algorithms have a need for manual alignment and predefined explicit parameters, but fail to give precise outcome in different lighting
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Image super-resolution based on conditional generative adversarial network IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Hongxia Gao; Zhanhong Chen; Binyang Huang; Jiahe Chen; Zhifu Li
Generative adversarial network (GAN) is one of the most prevalent generative models that can synthesise realistic high-frequency details. However, a mismatch between the input and the output may arise when GAN is directly applied to image super-resolution. To alleviate this issue, the authors adopted a conditional GAN (cGAN) in this study. The cGAN discriminator attempted to guess whether the unknown
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Blood flow imaging of high frame rate two-dimensional vector in cardiovascular ultrasound detection IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Cungang Wu; Chao Huang
Traditional cardiovascular ultrasound detection using focused ultrasound can cause a decrease in frame rate, which affects the results of the diagnosis. In order to improve the effect of cardiovascular ultrasound detection, this study used ultrasound vector blood flow imaging technology to improve the image frame rate and introduce planar high frame rate imaging technology. Simultaneously, this work
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Feedback evaluations to promote image captioning IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Jun He; Yijia Zhao; Bo Sun; Lejun Yu
Image captioning can be treated as a policy gradient problem. A retrieval model to obtain the discriminability score to distinguish between two images, given the caption for one of them, has been proposed previously; the discriminability score and one of the image captioning evaluation metrics were optimised using policy gradient. Based on this, two methods to evaluate the caption and caption-generating
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Adaptive iterative global image denoising method based on SVD IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Yepeng Liu; Xuemei Li; Qiang Guo; Caiming Zhang
Based on the image self-similarity and singular value decomposition (SVD) techniques, the authors propose an iterative adaptive global denoising method. For the structural differences between image patches, they adaptively determine the size of the search window. In each window, a similar image patch matrix is constructed based on the multi-scale similarity measure. In order to ensure the speed of
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Minimum class variance broad learning system for hyperspectral image classification IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Peng Chen
A new machine learning method named as a broad learning system (BLS) has been proposed recently. The advantage of simple, fast, and good generalisation ability make it attracting extensive attention. In this study, by introducing BLS to solving hyperspectral image (HSI) classification, a minimum class variance BLS (MCVBLS) was proposed. Firstly, in order to get spectral–spatial representation of original
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Class-aware single image to 3D object translational autoencoder IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Ceren Guzel Turhan; Hasan Sakir Bilge
The performances of generative adversarial network (GAN) and autoencoder (AE) models on images have been gathering a great deal of interest in terms of transferring them to three-dimensional (3D) domain. In this study, single image to object reconstruction problem was focused by presenting a novel 2D-to-3D AE model inspired by the recent improvements. To benefit from middle-level features, a model
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Occlusion-handling tracker based on discriminative correlation filters IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Yue Xie; Hanling Zhang; Lijun Li
Visual object tracking (VOT) based on discriminative correlation filters (DCF) has received great attention due to its higher computational efficiency and better robustness. However, DCF-based methods suffer from the problem of model contamination. The tracker will drift into the background due to the uncertainties brought by shifting among peaks, which will further lead to the issues of model degradation
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Mutual information guided 3D ResNet for self-supervised video representation learning IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Fei Xue; Hongbing Ji; Wenbo Zhang
In this work, the authors propose a novel self-supervised learning method based on mutual information to learn representations from the videos without manual annotation. Different video clips sampled from the same video usually have coherence in the temporal domain. To guide the network to learn such temporal coherence, they maximise the mutual information between global features extracted from different
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Efficient segmentation of lumbar intervertebral disc from MR images IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Leena Silvoster M; Retnaswami Mathusoothana S. Kumar
Segmentation of spine Magnetic Resonance Images (MRIs) has become an indispensable process in the diagnosis of lumbar disc degeneration, causing low back pain. Over the last decade of years, computer-directed diagnosis of disease, as well as computer-guided spine surgery, is based on the two-dimensional (2D) analysis of mid-sagittal slice of MRI. This work proposes an automatic strategy to extract
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Eigenstructure involving the histogram for image thresholding IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Salah Ameer
The idea of the proposed image thresholding scheme is simply to consider the histogram as a 2D plot rather than a 1D function. The data can now be represented as a two-row matrix. The first row is simply the grey levels of the image and the second row is the corresponding histogram values. Multiplying this matrix by its transpose will result in a power-type matrix of size 2 × 2. The best threshold
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Analysis of the correlation between infrared thermal sequence images of nostril area and respiratory rate IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Bo-Lin Jian; Min-Wei Huang; Shin-Hsiung Lee; Her-Terng Yau
In this study, a thermal imaging instrument was used to obtain facial thermal image information which was then used to calculate the number of breaths taken. However, small movements were inevitable and the first issue addressed was the means by which image calibration and region selection was to be made. To this end, thermal image sequence data calibration was done using technology that resolved small
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Denoising real bursts with squeeze-and-excitation residual network IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Hanlin Tan; Huaxin Xiao; Shiming Lai; Yu Liu; Maojun Zhang
The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, utilising similarities (or priors) within and across an image dataset helps recover clean images. As the noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this study, the authors propose a deep residual model with squeeze-and-excitation (SE) modules
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Siamese convolutional neural network-based approach towards universal image forensics IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Aniruddha Mazumdar; Prabin Kumar Bora
This study proposes a novel deep learning-based method which can detect different types of image editing operations carried out on images. Unlike most of the existing methods, which can only detect the editing operations considered in the training stage, the proposed method can generalise to manipulations not seen in the training stage. The method is based on the classification of image pairs as either
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Toward a general model for reflection recovery and single image enhancement IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Meng Chang; Qi Li; Zhuang He; Huajun Feng; Zhihai Xu
Images often suffer from low visual quality due to poor imaging conditions such as low light or hazy weather. The haze imaging model is widely used in contrast enhancement in daylight condition with haze, while the retinex model is universal for low-light conditions. Although their forms and applications are different, they can be unified into a more general form through the proposed observation. Based
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Multi-exposure image fusion via a pyramidal integration of the phase congruency of input images with the intensity-based maps IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Alireza Asadi; Mehdi Ezoji
The most important part of the common algorithms for multi-exposure image fusion (MEF) is the selection of features and metrics that are appropriate for weight map extraction. This study presents a structure-based multi-exposure image fusion by employing the phase congruency (PC) of the input image. The main idea behind PC-based analysis is that the locations of image key attributes are at points where
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Video summarisation with visual and semantic cues IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Binwei Xu; Haoran Liang; Ronghua Liang
Video summarisation greatly improves the efficiency of people browsing videos and saves storage space. A good video summary should satisfy human visual interestingness and preserve the theme of the original video at the semantic level. Unlike many existing methods that consider only visual features to generate video summaries, this study proposes a method that combines visual and semantic cues to extract
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Advanced framework for highly secure and cloud-based storage of colour images IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Nithya Chidambaram; Pethuru Raj; Karruppuswamy Thenmozhi; Rengarajan Amirtharajan
The evergrowing virtualised information technology infrastructure is powered by cloud-centric technology around the world. Cloud-based multimedia storage has become an essential aspect for users and business behemoths. However, as per a survey of Norton, around 3800 breaches have been publicly disclosed with 4.1 billion numbers of records exposed in 2019, which is a 54% rise when compared to 2018.
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Recognition of distorted QR codes with one missing position detection pattern IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Jianfen Huang; Liyan Li; Xiao Wang; Baoli Lu; Yuliang Liu
Quick response (QR) codes are widely used in many fields. Various QR code recognition approaches have been proposed to improve the accuracy of decoding QR code. However, the recognition of distorted QR codes with one missing position detection pattern (PDP) remains a problem. In this study, based on the vector relationship and the structural features, the authors introduce a new method for decoding
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Multi-scale patches based image denoising using weighted nuclear norm minimisation IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Yuli Fu; Junwei Xu; Youjun Xiang; Zhen Chen; Tao Zhu; Lei Cai; Weihong He
As a prior knowledge, non-local self-similarity (NSS) has been widely utilised in ill-posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch-based methods that only explore NSS in the same scale, a multi-scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi-scale strategy
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Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Saadeddine Laaroussi; Aziz Baataoui; Akram Halli; Khalid Satori
Image mosaicking is a combination of algorithms that use two or several images to create a single image. The resulting mosaic is a representation of a scene of the used images with a larger field of vision. However, since dynamic objects can exist in the overlap regions of these images, ghosting and parallax effects appear, therefore poor results are obtained. To overcome these unwanted effects and
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Target distance measurement method using monocular vision IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Mao Jiafa; Huang Wei; Sheng Weiguo
Most existing machine vision-based location methods mainly focus on the spatial positioning schemes using one or two cameras along with non-vision sensors. To achieve an accurate location, both schemes require processing a large amount of data. In this study, the authors propose a novel method, which requires much less amount of data to be processed for measuring target distance using monocular vision
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Multi-modal image fusion based on saliency guided in NSCT domain IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Shiying Wang; Yan Shen
Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate
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Colour image enhancement with brightness preservation and edge sharpening using a heat conduction matrix IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Ferzan Katırcıoğlu
In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM)
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Multiscale matters for part segmentation of instruments in robotic surgery IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Wenhao He; Haitao Song; Yue Guo; Guibin Bian; Yuejie Sun; Xiaowei Zhou; Xiaonan Wang
A challenging aspect of instrument segmentation in robotic surgery is to distinguish different parts of the same instrument. Parts with similar textures are common in a practical instrument and are difficult to distinguish. In this work, the authors introduce an end-to-end recurrent model that comprises a multiscale semantic segmentation network and a refinement model. Specifically, the semantic segmentation
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Human activity recognition using improved dynamic image IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Mohammadreza Riahi; Mohammad Eslami; Seyed Hamid Safavi; Farah Torkamani Azar
In action recognition, the dynamic image (DI) approach is recently proposed to code a video signal to a still image. Since DI descriptor is strongly dependent on first frames, it cannot extract dynamics that do not occur in the first frames or even long dynamics. On the other hand, most of the video frames are not informative for the task of action recognition. Therefore, the authors' intuition is
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Active contours driven by modified LoG energy term and optimised penalty term for image segmentation IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Soumen Biswas; Ranjay Hazra
An active contour model to segment the images is proposed by combining local binary fitting (LBF) energy function and modified Laplacian of Gaussian (MLoG) energy function. A MLoG energy function based on a new boundary indicator function or edge stop function (ESF) is introduced to smoothen the homogeneous regions and enhance the edge information of objects. Also, MLoG energy term with LBF energy
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F2PNet: font-to-painting translation by adversarial learning IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Guanzhao Li; Jianwei Zhang; Danni Chen
For Chinese font images, when all their strokes are replaced by pattern elements such as flowers and birds, they become flower–bird character paintings, which are traditional Chinese art treasures. The generation of flower–bird painting requires professional painters’ great efforts. How to automatically generate these paintings from font images? There is a huge gap between the font domain and the painting
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Novel breast cancer classification framework based on deep learning IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Wessam M. Salama; Azza M. Elbagoury; Moustafa H. Aly
AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the
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Image dehazing with uneven illumination prior by dense residual channel attention network IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Shibai Yin; Jin Xin; Yibin Wang; Anup Basu
Existing dehazing methods based on convolutional neural networks estimate the transmission map by treating channel-wise features equally, which lacks flexibility in handling different types of haze information, leading to the poor representational ability of the network. Besides, the scene lights are predicted by an even illumination prior which does not work for a real situation. To solve these problems
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Robust segmentation of the colour image by fusing the SDD clustering results from different colour spaces IET Image Process. (IF 1.995) Pub Date : 2020-11-30 Zhenzhou Wang
Segmentation of the colour image is challenging because the colour information is lost after being projected into three channels of the colour space. Many state-of-the-art colour image segmentation methods are based on monochrome segmentation in one channel of the colour space. However, the optimal performance of a segmentation method usually could not be achieved in a single colour space due to the
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Hybrid deep learning and machine learning approach for passive image forensic IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Abhishek Thakur; Neeru Jindal
Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination
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Development of an intelligent CAD system for mass detection in mammographic images IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Theofilos Andreadis; Christodoulos Emmanouilidis; Stefanos Goumas; Dimitrios Koulouriotis
Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer-aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses
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Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Emre Dandil; Ali Biçer
Brain tumours have increased rapidly in recent years as in other tumour types. Therefore, early and accurate diagnosis of brain tumour is vital for treatment. Magnetic resonance imaging (MRI) and histopathological assessments are the most common methods used in the detection of brain tumours. The research studies on non-invasive imaging methods such as MRI and magnetic resonance spectroscopy (MRS)
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Unified deep learning approach for prediction of Parkinson's disease IET Image Process. (IF 1.995) Pub Date : 2020-10-15 James Wingate; Ilianna Kollia; Luc Bidaut; Stefanos Kollias
The study presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by deep convolutional and recurrent neural networks when trained with medical images, such as magnetic resonance images and dopamine transporters scans. Internal representations of the trained DNNs constitute the
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Deep learning-based automated detection of human knee joint's synovial fluid from magnetic resonance images with transfer learning IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Imran Iqbal; Ghazala Shahzad; Nida Rafiq; Ghulam Mustafa; Jinwen Ma
As an analytic tool in medicine, particularly in radiology, deep learning is gaining much attention and opening a new way for disease diagnosis. Nonetheless, it is rather challenging to acquire large-scale detailed labelled datasets in the field of medical imaging. In fact, transfer learning provides a possible way to resolve this issue to a certain extent such that the parameter learning of a neural
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Smart feature extraction and classification of hyperspectral images based on convolutional neural networks IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Maissa Hamouda; Karim Saheb Ettabaa; Med Salim Bouhlel
Hyperspectral satellite imagery (HSI) is an advanced technology for object detection because it provides a large amount of information. Thus, the classification of HSIs is very complicated, so the methods of reducing spectral or spatial information generally degrade the quality of classification. In order to solve this problem and guarantee faster and more efficient processing, we propose a smart feature
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Evaluation of nano-filler dispersion quality in polymeric films with binary feature characteristics and fractal analysis IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Kazim Yildiz; Zehra Yildiz
This study investigates the use of binary features and fractal dimension analysis to evaluate the dispersion quality of nanofillers in thin polymeric films by using light microscopy images. For this purpose, polymeric films were cast with the inclusion of various montmorillonite (MMT) nanofiller amounts. Then the light microscopy images were captured from the polymeric films then preprocessed for the
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Combination of multi-scale and residual learning in deep CNN for image denoising IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Haiying Xia; Fuyu Zhu; Haisheng Li; Shuxiang Song; Xiangwei Mou
To better restore a clean image from a noise observation under high noise levels, the authors propose an image denoising network based on the combination of multi-scale and residual learning. Instead of using filters with different large sizes in traditional multi-scale schemes, they arrange multi-layer convolutions with the filters of the same size to speed up the model. Some dilated convolutions
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Where to look: a collection of methods forMAV heading correction in underground tunnels IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Christoforos Kanellakis; Sina Sharif Mansouri; Miguel Castaño; Petros Karvelis; Dariusz Kominiak; G. Nikolakopoulos
Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified
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Automated fish cage net inspection using image processing techniques IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Stavros Paspalakis; Konstantia Moirogiorgou; Nikos Papandroulakis; George Giakos; Michalis Zervakis
Fish-cage dysfunction in aquaculture installations can trigger significant negative consequences affecting the operational costs. Low oxygen levels, due to excessive fooling's, leads to decrease growth performance, and feed efficiency. Therefore, frequent periodic inspection of fish-cage nets is required, but this task can become quite expensive with the traditional means of employing professional
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Performance evaluation of single and cross-dimensional feature detection and description IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Odysseas Kechagias-Stamatis; Nabil Aouf; Mark A. Richardson
Three-dimensional (3D) local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However
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Optimised robust watermarking technique using CKGSA in DCT-SVD domain IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Roop Singh; Alaknanda Ashok; Mukesh Saraswat
Digital watermarking embeds a watermark to minimise the problem of illegal copying and disseminating multimedia contents. However, the existing techniques do not maintain the imperceptibility and robustness simultaneously. To achieve the same, this study proposes an optimised robust watermarking technique using chaotic kbest gravitational search algorithm. The chaotic kbest gravitational search algorithm
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Comparative analysis of reversible data hiding schemes IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Asha Jose; Kamalraj Subramaniam
The data hiding method embeds the data into covers such as video, audio, and images, which are used for integrity authentication, media protection, communication covert, copyright protection etc. In this work, several reversible data hiding (RDH) algorithms are analysed. Here, RDH algorithms are classified into six categories. They are histogram shifting centred RDH, code division multiplexing-based
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Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Lili Han; Shujuan Li; Pengxin Ren; Dingdan Xue
To improve the performance of the high-voltage copper contact burr image segmentation, a block cosparsity overcomplete learning transform image segmentation algorithm based on burr model is proposed in this study. In this study, k -means clustering method is used to initialise the clustering results; the authors found the algorithm is very effective for burr image processing in production process and
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Image multi-encryption architecture based on hybrid keystream sequence interspersed with Haar discrete wavelet transform IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Edwin A. Umoh; Ogechukwu N. Iloanusi; Uche A. Nnolim
A novel image multi-encryption architecture based on hybrid keystream sequence generated by a single hyperchaotic system and Haar discrete wavelet transform (HDWT) is proposed. The architecture consists of a pre-cipher stage, first encryption operation, Haar discrete wavelet decomposition stage and a second encryption operation. In the pre-cipher stage, the algorithm applies two-level pixel position
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Image copy-move forgery detection algorithm based on ORB and novel similarity metric IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Xiuxia Tian; Guoshuai Zhou; Man Xu
Image forgery poses a serious threat in electric power, medicine and other fields. Relevant departments need to pay a great price to identify the authenticity of the image. For traditional copy-move forgery image detection, the existing methods have at least two problems: low robustness and poor matching caused by a low number of feature points. Here, a novel similarity metric combining cosine and
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Optimisation of both classifier and fusion based feature set for static American sign language recognition IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Arun C.; R. Gopikakumari
Sign language recognition becomes a popular research field in human–computer interaction. Attention on hand signal analysis helps to make easy communication among computer and human for information sharing. Major focus of the gesture recognition system is to identify and recognise various gestures, by a computer. This study introduces optimisation of both classifier and feature set for static American
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Rapid region analysis for classification IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Christoph Rasche
The authors describe and evaluate a method that detects ridges (symmetric axes) in an Euclidean distance map. The method detects ridge-pixels with a local-maxima search using only relational operations and has therefore minimal complexity. The resulting ridges exhibit a height profile that is suitable for region abstraction by means of simple parameterisation. The method is firstly evaluated on artificial
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Use of gradient and normal vectors for face recognition IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Mehmet Koc; Semih Ergin; Mehmet Bilginer Gülmezoğlu; Rifat Edizkan; Atalay Barkana
The main objective of this study is to compare face recognition accuracies in the case when the grey levels in each pixel of the face images are replaced by the gradient and the surface normal vectors. Extensive information is provided to explain the differences between the gradient and the proposed features. Some well-known face recognition methods, such as common vector approach (CVA), discriminative
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Bi-dictionary learning model for medical image reconstruction from undersampled data IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Souad Mohaoui; Abdelilah Hakim; Said Raghay
In recent years, dictionary learning has shown to be an efficient tool in recovering images from their degraded, damaged or incomplete version. Especially, for medical images that contain significant details and characteristics. In this work, the authors are interested in this unsupervised learning technique for discovering and visualising the underlying structure of a medical image. Therefore, an
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Visual saliency based global–local feature representation for skin cancer classification IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Feng Xiao; Qiuxia Wu
With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack
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Sonar image mosaic based on a new feature matching method IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Zhijie Tang; Gaoqian Ma; Jiaqi Lu; Zhen Wang; Bin Fu; Yijie Wang
Sonar images are valuable in exploring underwater environmental information. As these images are generally limited by the viewing angle, sonar image mosaicking becomes an important research topic. By combining several frames consecutively acquired while the underwater vehicle is manoeuvring, an image with a wider view can be obtained. This study presents a fast sonar image mosaicking approach consisting
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1D representation of Laplacian eigenmaps and dual k-nearest neighbours for unified video coding IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Honggui Li
This study proposes a framework of video coding based on Laplacian eigenmaps (LEM) and its related embedding and reconstruction algorithm (ERA). Firstly, a one-dimensional (1D) representation of LEM is adopted to achieve an extremely low bit per pixel (BPP). Secondly, dual k -nearest neighbours, which keeps neighbour relationships both in high-dimensional data space and low-dimensional representation
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Feature encoding with hybrid heterogeneous structure model for image classification IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Zhihang Ji; Yan Yang; Fan Wang; Lijuan Xu; Xiaopeng Hu
In the standard bag-of-visual-words model, the relationship between visual words and geometric structure information embedding in Voronoi cells is important for expressing the topology of the feature space. However, this information is usually ignored by recent works. To overcome it, the authors proposed a hybrid heterogeneous structure model (HHSM), where local hyperspheres and local structure subspaces
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Hyperspectral image classification using three-dimensional geometric moments IET Image Process. (IF 1.995) Pub Date : 2020-10-15 Brajesh Kumar
Exploitation of both spectral and spatial information in hyperspectral imagery is important for effective classification. Considering the cubical arrangement of data, the three-dimensional (3D) techniques could be effectively used to model hypespectral features. In this study, the 3D geometric moments are used to extract the rotation, scale, and translation invariant features. Unlike 2D moments, the
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