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A content-dependent Daltonization algorithm for colour vision deficiencies based on lightness and chroma information

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IET image processing 2021年第4期15卷 983-996页

作者： Shen, Xuming Feng, Jianhua Zhang, Xiandou Hangzhou Dianzi Univ Dept Digital Media Technol Hangzhou 310018 Peoples R China

To help CVD (colour vision deficiency) observers distinguish among different colours in digital images, this study proposes a content-dependent Daltonization algorithm based on lightness and chroma information. This improves the trade-offs in contrast, naturalness, and colour consistency deficiencies found in existing methods. Chroma remapping and brightness adjustments on image content are utilized to increase contrast and maximize the preservation of the original hue. In a quantitative study, the proposed method proved more useful for dichromats than did existing methods. The evaluation by research subjects showed that the method was superior to the current methods in balancing contrast, naturalness, and colour consistency.

关键词： Optical, image and video signal processing computer vision and image processing techniques

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An image encryption algorithm with a plaintext-related quantisation scheme

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IET image processing 2021年第9期15卷 2039-2055页

作者： Oravec, Jakub Ovsenik, Lubos Papaj, Jan Tech Univ Kosice Fac Elect Engn & Informat Dept Elect & Multimedia Commun Nemcovej 32 Kosice 04001 Slovakia

This paper describes an image encryption algorithm that utilises a plaintext-related quantisation scheme. Various plaintext-related approaches from other algorithms are presented and their properties are briefly discussed. Main advantage of the proposed solution is the achievement of a similar behaviour like that of more complex approaches with a plaintext-related technique used in a rather simple step such as quantisation. This design should result in a favourable computational complexity of the whole algorithm. The properties of the proposal are evaluated by a number of commonly used numerical parameters. Also, the statistical properties of a pseudo-random sequence that is quantised according to the plain image pixel intensities are investigated by tests from NIST 800-22 suite. Obtained results are compared to values reported in related works and they imply that the proposed solution produces encrypted images with comparable statistical properties but authors' design is faster and more efficient.

关键词： Cryptography image and video coding computer vision and image processing techniques Data security

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Multi-dimensional weighted cross-attention network in crowded scenes

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IET image processing 2021年第14期15卷 3585-3598页

作者： Xie, Yefan Zheng, Jiangbin Hou, Xuan Naqvi, Irfan Raza Xi, Yue Kuang, Nailiang Northwestern Polytech Univ Sch Comp Sci & Engn Xian 710072 Peoples R China Northwestern Polytech Univ Sch Software Xian Peoples R China Shaanxi Prov Key Lab Speech & Image Informat Proc Xian Peoples R China Air Force Engn Univ PLA Aeronaut Engn Coll Xian Peoples R China Xian Microelect Technol Inst Xian Peoples R China

Human detection in crowded scenes is one of the research components of crowd safety problem analysis, such as emergency warning and security monitoring platforms. Although the existing anchor-free methods have fast inference speed, they are not suitable for object detection in crowded scenes due to the model's inability to predict the well-fined object detection bounding boxes. This work proposes an end-to-end anchor-free network, Multi-dimensional Weighted Cross-Attention Network (MANet), which can perform real-time human detection in crowded scenes. Specifically, the Double-flow Weighted Feature Cascade Module (DW-FCM) is used in the extractor to highlight the contribution of features at different levels. The Triplet Cross Attention Module (TCAM) is used in the detector head to enhance the association dependence of multi-dimension features, further strengthening human boundary features' discrimination ability at a fine-grained level. Moreover, the strategy of Adaptively Opposite Thrust Mapping (AOTM) ground-truth annotation is proposed to achieve bias correction of erroneous mappings and reduce the iterations of useless learning of the network. These strategies effectively alleviate the defect that the existing anchor-free network cannot correctly distinguish and locate the individual human in crowded scenes. Compared with the anchor-based detection method, there is no need to set anchor parameters manually, and the detection speed can satisfy the real-time application. Finally, through extensive comparative experiments on CrowdHuman and WIDER FACE datasets, the results demonstrate that the improved strategy achieves the state-of-the-art result in the anchor-free methods.

关键词： Optical, image and video signal processing computer vision and image processing techniques Machine learning (artificial intelligence)

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Multiple extended stealth target tracking from image observation based on non-linear sub-random matrices approach

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IET RADAR SONAR AND NAVIGATION 2021年第3期15卷 274-293页

作者： Barbary, Mohamed Abd ElAzeem, Mohamed H. Alexandria Univ Dept Elect Engn Alexandria Egypt Egyptian Tech Res & Dev Ctr Cairo Egypt Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Cairo Egypt

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB-filter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters.

关键词： computer vision and image processing techniques nonlinear sub-random matrices approach nonlinear kinematic EST state extension ellipsoid recent extended tracking filters multiple ESTs image observation EST tracking Optical, image and video signal processing matrix algebra track-before-detect approach extended target states extended stealth targets Algebra Radar equipment, systems and applications MB-filter-based random matrices model approach SMC-sub-RMM-MB-TBD filter stealth target Signal detection efficient manner Filtering methods in signal processing low-observable ESTs EST-RMM-TBD scenarios tracking filters known detection profile multiBernoulli filter RMM-MB filter additional state variables Integral transforms EST-sub-RMM-TBD radar technology Interpolation and function approximation (numerical analysis) object detection ellipsoidal ET tracking image recognition radar cross-sections target tracking radar cross section Monte Carlo methods kinematic states filtering theory Other topics in statistics

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Artefact-free image stitching via a better normed seam-cutting energy function

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IET image processing 2021年第6期15卷 1371-1381页

作者： Qiu, Xiangyan Li, Qiaoliang Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha 410081 Hunan Peoples R China

image stitching, as the important field of computer graphics and vision, has received much attention in recent years. image stitching techniques are generally decomposed into two phases: image alignment, which aligns target images with the reference images;and image composition, which fixes ghosting and visual artefacts. This work aims to propose a new strategy for the seam-cutting method which provides visually appealing result. Seam-cutting is one of the most influential methods in image composition, which can relieve artefacts and produce plausible results. However, it is observed that the state-of-the-art seam-cutting approaches usually lead to undesirable seams in some challenging scenes. Here, the authors put forward a novel seam-cutting method by defining a new energy function. This method uses 5/2 power of L1 norm as a colour difference which can magnify the weight of colour distinction to avoid undesirable seams and artefacts. The proposed method can be easily implemented. The test images are collected from the public available challenging datasets and taken by ourselves. Experiments demonstrate that the proposed method can create comparable or even better stitching results compared to other state-of-the-art seam-cutting approaches.

关键词： image alignment reference images image registration Patient diagnostic methods and instrumentation influential methods image colour analysis image recognition Biology and medical computing computer vision and image processing techniques visual artefacts undesirable seams computer graphics Optical, image and video signal processing image segmentation image matching visually appealing result stitching results plausible results feature extraction novel seam-cutting method image composition medical image processing artefact-free image stitching target images state-of-the-art seam-cutting approaches normed seam-cutting energy function image stitching techniques test images

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Sparse representation based intraframe and semi-intraframe video coding schemes for low bitrates

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IET image processing 2021年第5期15卷 1128-1143页

作者： Irannejad, Maziar Mahdavi-Nasab, Homayoun Islamic Azad Univ Digital Proc & Machine Vis Res Ctr Najafabad Branch Najafabad Iran Islamic Azad Univ Dept Elect Engn Najafabad Branch Najafabad Iran

This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi-intraframe video coding. The presented frameworks apply the efficient K-singular value decomposition and recursive least squares dictionary learning methods for sparse representation of videos to study their coding performances. In the proposed semi-intraframe schemes, namely, SISC1 and SISC2, only frame-blocks with more than a threshold deviation from the blocks of the previous frame are transmitted/coded. This reduces the required bitrate and prevents the sparse coding of similar blocks, leading to more efficient video coding methods. The results show that the dictionary learning-based intraframe coding improves the rate-distortion performance of the conventional Motion-JPEG and Motion-JPEG2000 at low bitrates for more than about 3 and 0.5 dB of PSNR on average (for 0.2-1 bpp compression), respectively. The proposed methods outperform the basic dictionary learning-based coding, especially for slower changing videos, generally, with more than 3 dB superiority on average over the tested bitrates. These schemes even present superior performance than the HEVC in the intramode for the complex textured or cluttered scenes. The proposed SISC2 method also saves up to about 50% of the sparse coding computational cost by preventing the coding of more similar frame-blocks.

关键词： Algebra image and video coding computer vision and image processing techniques Video signal processing Algebra

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Keypoint based comprehensive copy-move forgery detection

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IET image processing 2021年第6期15卷 1298-1309页

作者： Diwan, Anjali Sharma, Rajat Roy, Anil K. Mitra, Suman K. DA IICT Gandhinagar 382007 Gujarat India

Verifying the authenticity of a digital image has been challenging problem. The simplest of the image tampering tricks is the copy-move forgery. In copy-move forgery copied portion of the image is pasted on another part of the same image. Geometrical transformations are used on the copied portions of the image before pasting it for the tampered image to look realistic and visually convincing. To make it more complex, other processing approaches may also be applied in the forged region for hiding traces of forgery. These processings are the scale, rotation, JPEG compression, and AWGN. In this paper, an approach based on features of the CenSurE keypoint detector and FREAK descriptor is proposed. This combination has novelty in itself as it has never been used for this purpose before to the best of authors' literature studies. CenSurE detectors are fast and give stable and accurate output even in the case of rotated images, which we club with binary descriptor FREAK. Hierarchical clustering and Neighbourhood search is applied in such a way that it can locate and detect multiple copy-move forgeries. The authors are hopeful that the proposed approach may be used in real-time image authentication and copy-move forgery detection.

关键词： Optical, image and video signal processing image and video coding image recognition computer vision and image processing techniques

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Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms

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HEALTHCARE TECHNOLOGY LETTERS 2021年第6期8卷 148-158页

作者： Zhang, Yiyuan Xue, Tianwei Liu, Zhenjie Chen, Wei Vanrumste, Bart Katholieke Univ Leuven E Media Res Lab B-3000 Leuven Belgium Katholieke Univ Leuven Dept Elect Engn STADIUS Leuven Belgium Fudan Univ Sch Informat Sci & Technol Ctr Intelligent Med Elect Shanghai Peoples R China

During COVID-19, awareness of proper hand washing has increased significantly. It is critical that people learn the correct hand washing techniques and adopt good hand washing habits. Hence, this study proposes using wearable devices to detect hand washing activity among other daily living activities (ADLs) and classify steps proposed by the World Health Organization (WHO). Two experiments were conducted with 16 participants, aged from 20 to 31. The first experiment was hand washing following WHO regulation (ten participants), and the second experiment was performing eight ADLs (eight participants). All participants wore two wearable devices equipped with accelerometers and gyroscopes;one on each wrist. Four machine learning classifiers were compared in classifying hand washing steps in the leave-one-subject-out (LOSO) mode. The SVM model with Gaussian kernel achieved the best performance in classifying 11 washing hands steps, with an average F1-score of 0.8501. When detected among the other ADLs, hand washing following WHO regulation obtained the F1-score of 0.9871. The study demonstrates that wearable devices are feasible to detect hand washing activity and the hand washing techniques as well. The classification results of getting the soap and rubbing thumbs are low, which will be the main focus in the future study.

关键词： classify steps accelerometers machine learning classifiers hand washing activity gyroscopes learning (artificial intelligence) support vector machines Biology and medical computing computer vision and image processing techniques proper hand washing Other topics in statistics good hand washing habits correct hand washing techniques pattern classification hands steps wearable devices

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Down-sampling template curve to accelerate LDDMM-curve with application to shape analysis of the corpus callosum

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HEALTHCARE TECHNOLOGY LETTERS 2021年第3期8卷 78-83页

作者： Huang, Weikai Tang, Xiaoying Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Guangdong Peoples R China

Large deformation diffeomorphic metric mapping for curve (LDDMM-curve) has been widely used in deformation based statistical shape analysis of the mid-sagittal corpus callosum. A main limitation of LDDMM-curve is that it is time-consuming and computationally complex. In this study, down-sampling strategies for accelerating LDDMM-curve are investigated and tested on two large datasets, one on Alzheimer's disease (155 Alzheimer's disease, 325 mild cognitive impairment and 185 healthy controls) and the other on first-episode schizophrenia (92 first-episode schizophrenia and 106 healthy controls). For both datasets a variety of down-sampling factors are tested in terms of registration accuracy, registration speed, and most importantly disease-related patterns. Experimental results revealed that down-sampling template curve by a factor of 2 can significantly reduce the running time of LDDMM-curve without sacrificing the registration accuracy. Also, the disease-induced patterns, or more specifically the group comparison results, were almost identical before and after down-sampling. It is also shown that there was no need to down-sample the target population curves but only the single template curve of the study of interest. Comprehensive analyses were conducted.

关键词： down-sampling template curve statistical analysis image registration Patient diagnostic methods and instrumentation neurophysiology diseases brain Biology and medical computing computer vision and image processing techniques Other topics in statistics biomedical MRI Optical, image and video signal processing 106 healthy controls cognition 155 Alzheimer's disease Biomedical magnetic resonance imaging and spectroscopy 185 healthy controls LDDMM-curve deformation based statistical shape analysis medical image processing medical disorders target population curves Probability theory, stochastic processes, and statistics Medical magnetic resonance imaging and spectroscopy single template curve

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Driving distraction detection based on gaze activity

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ELECTRONICS LETTERS 2021年第22期57卷 857-859页

作者： Zhang, Yingji Yang, Xiaohui Feng, Zhiquan Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Peoples R China

Driving distraction detection can effectively prevent the occurrence of traffic accidents. Thus, monitoring a driver's state is very important for road safety. At present, most driving distraction detection methods focus on singular aspects, such as gaze distraction or hand distraction, and rarely focus on cognitive distraction as it is difficult to detect. This study proposed a driving distraction detection method based on gaze activity. A multi-channel convolutional neural network was used to classify the driver's gaze area and calculate the gaze activity. In cognitive distraction, the driver's gaze activity is significantly lower than that in the normal driving state. The activity thresholds via experiments have been obtained and used to determine whether drivers were in a cognitively distracted state. Through experiments, the accuracy of this method reached 92.36%. To identify driving distractions more comprehensively, a gaze distraction algorithm based on the two-second rule has been added to the method. The experiment demonstrated that our method in combination with this algorithm improved all of the indicators (when compared to only the gaze activity algorithm), and the accuracy rate increased to 95%.

关键词： Optical, image and video signal processing computer vision and image processing techniques Traffic engineering computing Neural nets

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