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Modal approach based on global stereocorrelation for defects measurement in wire-laser additive manufacturing J. Electron. Imaging (IF 1.1) Pub Date : 2024-03-01 Khalil Hachem, Yann Quinsat, Christophe Tournier, Nicolas Béraud
Producing near net shape parts with complex geometries using wire-laser additive manufacturing (AM) often requires a mastered and optimized process. Differences between the constructed and nominal geometries of the manufactured entities demand an in-situ defects measurement to complete the production of the entire part successfully. A contactless measuring system is needed to evaluate geometrical deviations
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Flexible machine/deep learning microservice architecture for industrial vision-based quality control on a low-cost device J. Electron. Imaging (IF 1.1) Pub Date : 2024-03-01 Stefano Toigo, Brendon Kasi, Daniele Fornasier, Angelo Cenedese
This paper aims to delineate a comprehensive method that integrates machine vision and deep learning for quality control within an industrial setting. The proposed innovative approach leverages a microservice architecture that ensures adaptability and flexibility to different scenarios while focusing on the employment of affordable, compact hardware, and it achieves exceptionally high accuracy in performing
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Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection J. Electron. Imaging (IF 1.1) Pub Date : 2024-03-01 Domen Rački, Dejan Tomaževič, Danijel Skočaj
Anomaly detection (AD) in an unsupervised manner has become the go-to approach in applications where data labeling proves problematic. However, these approaches are not completely unsupervised, since they rely on the weak knowledge of the dataset distribution into anomalous and anomaly-free subsets and typically require post-training threshold calibration in order to perform AD. Yet, they do not take
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Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning J. Electron. Imaging (IF 1.1) Pub Date : 2024-02-01 Kevin Helvig, Pauline Trouvé-Peloux, Ludovic Gavérina, Jean-Michel Roche, Baptiste Abeloos
“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyze and use contextual information from data and
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Generic neural architecture search toolkit for efficient and real-world deployment of visual inspection convolutional neural networks in industry J. Electron. Imaging (IF 1.1) Pub Date : 2024-02-01 Nikola Pižurica, Kosta Pavlović, Slavko Kovačević, Igor Jovančević, Miguel de Prado
Visual inspection plays a pivotal role in numerous industrial production processes, and the pursuit of automation has surged with the rise of deep learning and convolutional neural networks (CNNs). Therein, the deployment of visual inspection CNNs on resource-constrained edge devices stands as a critical problem as these devices are the most affordable and well-suited for many industrial applications
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Domain shared features for visual inspection of complex mechanical assemblies based on synthetically generated data J. Electron. Imaging (IF 1.1) Pub Date : 2024-02-01 Velibor Došljak, Igor Jovančević, Jean-José Orteu
Even though neural network methodologies have been established for a long time, only recently have they achieved exceptional efficacy in practical deployments, predominantly due to improvements in hardware computational capacity and the large amounts of available data for learning. Nonetheless, substantial challenges remain in utilizing deep learning in many domains, mainly because of the lack of large
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Deep learning contour-based method for semi-automatic annotation of manufactured objects in electron microscopy images J. Electron. Imaging (IF 1.1) Pub Date : 2024-02-01 Isaac Wilfried Sanou, Julien Baderot, Stéphanie Bricq, Yannick Benezeth, Franck Marzani, Sergio Martinez, Johann Foucher
Precision characterization is fundamental to achieve expected performance in semiconductors where Moore’s law pushes the boundaries to miniaturize components. To measure these attributes, deep learning models are used, which require manual annotation of several objects captured via electron microscopy. However, this annotation can be laborious and time-consuming. We propose a semi-automated method
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MA-YOLO: a multi-attention object detection network for remote sensing images J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Qingzeng Song, Maorui Hou, Yongjiang Xue, Jing Yu
In recent years, deep learning-based objects detection algorithms have demonstrated exceptional performance in natural environments. These algorithms have been extensively used in various remote sensing applications, which include the detection of structures and roads as well as flood and earthquake disasters. In these applications, remote sensing images may be captured by satellites, drones, and other
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Reversible data hiding algorithm for color images combining superpixel segmentation and pixel value ordering J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Yan Zhao, Xiaobing Bao
At present, most of the reversible data hiding (RDH) algorithms divide images into regular rectangular blocks for data embedding, and most of the RDH algorithms for color images apply the steganography algorithms of grayscale images directly to each color channel of color images. We propose a color image RDH algorithm combining superpixel segmentation and pixel value ordering (PVO), which uses a superpixel
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Dynamic PDGAN: discriminator-boosted knowledge distillation for StyleGANs J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Yuesong Tian, Li Shen, Xiang Tian, Zhifeng Li, Yaowu Chen
Generative adversarial networks have shown remarkable success in image synthesis, especially StyleGANs. Equipped with delicate and specific designs, StyleGANs are capable of synthesizing high-resolution and high-fidelity images. Previous works aiming at improving StyleGANs mainly focus on modifying the architecture of StyleGANs or transferring knowledge from other domains. However, the knowledge contained
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Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Joaquin Rodriguez, Lew-Fock-Chong Lew-Yan-Voon, Renato Martins, Olivier Morel
Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With the advent of new and cheap polarimetric sensors, this imaging modality is becoming accessible to a wider public for solving problems, such as pose estimation, 3D reconstruction, underwater navigation, and depth estimation
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Temporal context-aware motion-saliency detection J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Mengxi Xu, Xiaobin Wu, Zhizhong Ma, Ruili Wang, Huimin Lu
A new type of motion saliency—that is, temporal context-aware motion saliency (TCAMS)—was proposed to detect the saliency of motion using its temporal context, presenting the semantic information of an event in a highly informative manner. Our definition differs from the typical definition of motion saliency. According to our definition of TCAMS, a novel detection method is proposed based on the interaction
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Graphion: graph neural network-based visual correlation feature extraction module J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Chengqian Chu, Shijia Li, Xingui Yu, Yongjing Wan, Cuiling Jiang
Global contextual features are essential in computer vision tasks. Traditional convolutional networks are limited by the size of the convolutional kernel, resulting in a limited receptive field for each layer of the network. To address this issue, transformers introduced global attention, which has demonstrated excellent performance in natural language processing and has been widely applied in visual
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Offset flow-guide transformer network for semisupervised real-world video denoising J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Lihui Sun, Heng Chen, Jialin Li, Chuyao Wang
Video denoising is a fundamental task in low-level computer vision. Most existing denoising algorithms use synthetic data learning. However, there is a significant difference between the noise distributions of synthetic and natural data, which leads to poor generalization performance of the model in actual scenes. Hence, a video method based on an offset optical flow-guided transformer is proposed
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Study and VHDL implementation of a novel chaos-based block cipher algorithm for digital image security J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Mohamed Ali Hajjaji, Adnen Albouchi
We live in an interconnected world, and digital data transmissions have reached significant importance as they are the most used means of communication. Connected objects are widely used in our digital life, yet they may pose vulnerabilities and risks such as hacking or personal data theft due to their often inadequate security. In response to the size and capability restrictions of many existing embedded
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Few-shot classification based on manifold metric learning J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Qingzhen Shang, Jinfu Yang, Jiaqi Ma, Jiahui Zhang
Few-shot classification aims to classify samples with a limited quantity of labeled training data, and it can be widely applied in practical scenarios such as wastewater treatment plants and healthcare. Compared with traditional methods, existing deep metric-based algorithms have excelled in few-shot classification tasks, but some issues need to be further investigated. While current standard convolutional
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FSDGNet: frequency and spatial dual guide network for crack detection J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Yang Liu, Genji Yuan, Jianbo Li, Shuteng Hu
Cracks are one of the major threats to the safe operation of civil infrastructures, so timely and accurate detection of cracks is crucial for accident prevention. However, in practical applications, the poor continuity and low contrast of many cracks (e.g., pavement cracks) pose a great challenge to image-based crack detection. In previous approaches, the detection results are often unsatisfactory
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TEG: image theme recognition using text-embedding-guided few-shot adaptation J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Jikai Wang, Wanglong Lu, Yu Wang, Kaijie Shi, Xianta Jiang, Hanli Zhao
Grouping images into different themes is a challenging task in photo book curation. Unlike image object recognition, image theme recognition focuses on the understanding of the main subject or overall meaning conveyed by an image. However, it is challenging to achieve satisfactory performance using existing general image recognition methods. In this work, we aim to solve the image theme recognition
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Pillar-based multilayer pseudo-image 3D object detection J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Lie Guo, Ke Lu, Liang Huang, Yibing Zhao, Zhenwei Liu
Real-time and reliable three-dimensional (3D) object detection is critical for the autonomous driving system. Many 3D object detectors have recently adopted the pillar-based method represented by PointPillars due to its benefits of simplicity, speed, modularity, and ease of expansion. However, the detection effectiveness of the pillar-based method is limited by the lack of intra-pillar feature learning
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Frontal-view gait recognition using discriminative dynamics feature representations and learning J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Muqing Deng, Yi Zou, Wuqiao Zhu, Mali Xing, Yonghui Huang, Jie Yang
Gait recognition plays an important role in the area of biometric recognition. Despite that much progress has been made for gait recognition in recent years, most of them are based on lateral-view gait characteristics. These methods usually require a large data collection area to capture full gait sequences, which are only applicable in wide outdoor spaces. In this paper, we propose a new frontal-view
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AIOD-YOLO: an algorithm for object detection in low-altitude aerial images J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Peng Yan, Yong Liu, Lu Lyu, Xianchong Xu, Bo Song, Fuqiang Wang
Aerial image object detection has a wide range of application values in civilian or military fields. Due to its unique high-altitude imaging viewpoint and the multiangle shooting method, aerial images lead to problems, such as small objects being detected in the image, large variations in object scales, and dense distribution. To alleviate the above problems, we propose an improved aerial image object
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Enhancing titanium spacer defect detection through reinforcement learning-optimized digital twin and synthetic data generation J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Sankarsan Mohanty, Eugene Su, Chao-Ching Ho
In the field of automatic defect detection, a major challenge in training accurate classifiers using supervised learning is the insufficient and limited diversity of datasets. Obtaining an adequate amount of image data depicting defective surfaces in an industrial setting can be costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in an underrepresentation
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Pedestrian trajectory prediction with high-order interactions J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Nan Xiang, Bingdi Tian, Zhenguo Wang
To tackle the problem of human trajectory prediction in complex scenes, we propose a model using hypergraph convolutional neural networks for social interaction (HGCNSI). Our model leverages a hypergraph structure to capture both high-order interactions and complex social dynamics among pedestrians (who often influence each other in a nonlinear and structured manner). First, we propose a social interaction
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2023 List of Reviewers J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01
JEI thanks the reviewers who served the journal in 2023.
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Lightweight image super-resolution network using 3D convolutional neural networks J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Hailong Li, Zhonghua Liu, Yong Liu, Di Wu, Kaibing Zhang
In recent years, significant progress has been made in single-image super-resolution (SISR) with the emergence of convolutional neural networks (CNNs). However, the application of SISR on low computing power devices is hindered by the massive number of parameters and computational costs. Despite the focus on lightweight SISR models in many studies, the majority still struggles to balance performance
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Event camera object recognition using spatiotemporal event time surface and reward-modulated spike-timing-dependent plasticity learning rule J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Qian Zhou, Guimeng Zhang
Event cameras record moving objects with asynchronous event streams. It remains a challenge to make full use of the spatiotemporal information of event streams to extract high-quality features and to make event camera object recognition. We propose an event-based event camera object recognition system, which includes a denoising module and an object recognition module. The denoising module removes
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Run length encoding based reversible data hiding scheme in encrypted images J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Bharathi Chidirala, Bibhudendra Acharya
This paper presents a run length encoding (RLE) based reversible data hiding (RDH) scheme designed specifically for higher order pixel group values of image intensities. RDH aims to embed additional data into the host image while maintaining the ability to completely recover the original image without any loss. The proposed scheme utilizes the RLE technique to exploit the statistical characteristics
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Unsupervised monocular depth learning using self-teaching and contrast-enhanced SSIM loss J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Chuchu Feng, Yu Wang, Yongsun Lai, Qiong Liu, Yijun Cao
In recent years, unsupervised learning has gained significant attention as a promising approach for monocular depth estimation. We propose an unsupervised monocular depth learning approach that combines self-teaching method and contrast-enhanced structural similarity (SSIM) loss. The self-teaching method involves learning from both a teacher and a student network. The teacher network generates a pseudo-ground
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Secure most significant bit plane compression based reversible data hiding in encrypted image technique using Huffman Ciphersystem J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Rama Singh, Saumya Patel, Ankita Vaish
In this work, a secure encryption technique for implementing reversible data hiding, named the Huffman Ciphersystem, is developed. First, a cover image is decomposed into two sub-images. The first sub-image consists of four most significant bits (MSBs), and the second sub-image consists of four least significant bits. The difference of adjacent MSB values for the first sub-image is calculated using
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Quantizing separable convolution of MobileNets with mixed precision J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Chenlu Zhang, Guanpeng Zuo, Zhe Zheng, Wu Zhang, Yuan Rao, Zhaohui Jiang
As deep learning moves toward edge computing, researchers have developed techniques for efficient resource usage and accurate inference on mobile devices. Quantization, as one of the key approaches, enables the deployment of deep learning models on embedded platforms. However, MobileNet’s accuracy suffers due to quantization errors in depth-wise separable convolutions. To reach a smaller model size
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City scene super-resolution via geometric error minimization J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Zhengyang Lu, Feng Wang
Super-resolution techniques are crucial in improving image granularity, particularly in complex urban scenes, where preserving geometric structures is vital for data-informed cultural heritage applications. We propose a city scene super-resolution method via geometric error minimization. The geometric-consistent mechanism leverages the Hough transform to extract regular geometric features in city scenes
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Image steganalysis algorithm based on deep learning and attention mechanism for computer communication J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Huan Li, Shi Dong
In today’s digital era, network communication has become ubiquitous, evincing pressing concerns regarding the confidentiality of transmitted information. Given heightened public scrutiny of information security, image steganalysis has emerged as a pivotal concern within the ambit of information security. To further optimize the image steganalysis algorithm, attention mechanism is introduced into convolutional
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Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Mengzhu Yu, Zhenjun Tang, Xiaoping Liang, Zixuan Yu, Chunqiang Yu, Xianquan Zhang
Robust video hashing is an effective method of copy detection. But most existing schemes do not make effective classifications; thus their copy detection performances are unsatisfactory. To address these problems, we propose a robust video hashing based on deep feature and the quaternion generic Fourier descriptor (QGFD) for copy detection. In the proposed hashing scheme, an entropy-weighted secondary
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Dynamic template updating Siamese network based on status feedback with quality evaluation for visual object tracking J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Lifan Sun, Xinxiang Li, Dan Gao, Bo Fan
Visual object tracking algorithms based on Siamese networks yield promising results through offline training on large benchmarks. However, they cannot adapt well to changes in the target’s appearance and tracking scenarios during online tracking because they rely on a single initial template. Most existing template update algorithms use the tracking result of the previous frame to update the template
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Polarization imaging shadow removal based on attention conditional generative adversarial networks J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Guoming Xu, Ang Cao, Feng Wang, Jian Ma, Yi Li
Shadows in polarized images often interfere with the acquisition and analysis of the polarization state of light. By removing the shadows, these interferences can be eliminated, and the polarization information can be extracted more accurately for subsequent processing. To solve the problem of insufficient illumination and information recovery in the shadow region of polarization images, we propose
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Multiscale luminance adjustment-guided fusion for the dehazing of underwater images J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Huipu Xu, Min Wang, Shuo Chen
Due to absorption and scattering effects in the underwater medium, acquired underwater images often suffer from hazy content and color casts, which lead to significant degradation of visual quality. In this work, a dehazing model is proposed; it is divided into three parts and effectively eliminates the problem of visual degradation caused by hazy content. The attenuation characteristics of light at
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ELANet: an efficiently lightweight asymmetrical network for real-time semantic segmentation J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Jiafei Chen, Junyang Yu, Yingqi Wang, Xin He
Semantic segmentation plays a crucial role in practical applications, such as autonomous driving and robot navigation. However, prevalent semantic segmentation networks suffer from two primary challenges: oversized networks with redundant parameters that hinder network inference speed and excessively lightweight network structures that sacrifice semantic segmentation accuracy. Therefore, it is essential
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Underwater image enhancement based on a combination of improved gated context aggregation network and gray world algorithms J. Electron. Imaging (IF 1.1) Pub Date : 2024-01-01 Zhen Liu, Hanchi Hong, Xiujing Gao
Underwater images received by underwater robots in unrestricted environment during underwater operations are characterized by overall bluish and greenish tones, blurrier edge details, and low contrast. This phenomenon is due to the attenuation and scattering of light in the water and the influence of artificial light sources. To improve the visual performance of underwater imaging, we propose a two-step
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Salient object detection based on hierarchical segmentation and objectness-guided J. Electron. Imaging (IF 1.1) Pub Date : 2023-12-01 Jinxia Shang, Runxin Li, Yun Liu
Unsupervised salient object detection aims to automatically detect important or attractive target objects in the scene without any user annotation. Compared to fully supervised, it can save a lot of manpower and resources invested in pixel-level annotation. However, the performance of existing unsupervised methods is still far from satisfactory because of the interference of complex backgrounds and
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Siamese network with contrastive learning and adaptive template updating for object tracking J. Electron. Imaging (IF 1.1) Pub Date : 2023-12-01 Wei Cui, Xun Duan, Guangqian Kong, Huiyun Long
Visual object tracking is a crucial task across numerous computer vision applications. However, object tracking algorithms face significant challenges stemming from deformation and fast motion, which frequently incur dramatic changes to the target’s appearance. To address this problem, we propose a Siamese-network-based object tracking method that combines contrastive learning and adaptive template
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Video encryption algorithm based on the fractional order chaos system J. Electron. Imaging (IF 1.1) Pub Date : 2023-12-01 Qiuxia Xie, Qingping Zhang
With the development of technology and the diversification of information acquisition methods, video has become an important component of our daily life and work, and the security of video information is of increasing concern. In this work, a general video encryption algorithm is proposed and implemented. The algorithm extracts a video file frame by frame to obtain video frames and encrypts the video
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CrackF-Net: a pixel-level segmentation network for pavement crack detection J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Shen Luan, Xingen Gao, Chen Wang, Hongyi Zhang, Fei Chao, Juqiang Lin, Junqi Huang, Huali Jiang, Feng Lin
Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder–decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional
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Stabilized videos source identification method based on undecimated dual-tree complex wavelet transform and Bayesian adaptive direct search algorithm J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Nili Tian, Kunmao Lin, Qing Pan
The widely adopted video stabilization techniques in imaging devices have become a bottleneck that limits the performance of source camera identification (SCI) based on photo-response non-uniform (PRNU) noise. Affine transformations in the stabilized video, including scaling, translation, and rotation, introduce varying degrees of distortion to the PRNU pattern across frames, which lead to a degradation
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Graph neural network news recommendation based on weight learning and preference decomposition J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Junwen Lu, Ruixin Su
Personalized news suggestions are an important technology to enhance people’s online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective
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Image watermarking based on remainder value differencing and extended Hamming code J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Anantha Rao Gottimukkala, Naween Kumar, Jatindra Kumar Dash, Gandharba Swain
Due to the availability of various photo editing tools, intruders can tamper with an image very easily. So, various watermarking and tamper detection approaches have been proposed by researchers. Basically, tamper detection techniques focus on embedding the watermark, extracting the water mark, and identifying the tampered regions. But it is very important that the tampered pixels should also be corrected
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Aerial tracking of camouflaged people in woodlands J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Yang Liu, Cong-Qing Wang, Bin Xu, Yong-Jun Zhou
With the remarkable advances of unmanned aerial vehicles (UAVs) and machine vision, aerial tracking has attracted wide attention from scholars. Previous tracking methods were mostly implemented in clean and well-lit environments, making it challenging to track camouflaged people rapidly and accurately in woodlands. We develop a framework for camouflaged people aerial tracking (CPAT) based on transformer
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Camouflage target detection based on strong semantic information and feature fusion J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Junhua Yan, Xutong Hu, Yun Su, Yin Zhang, Mengwei Shi, Yinsen Gao
Aiming at the detection difficulties in camouflage target detection, such as the high similarity between the target and its background, serious damage to the edge, and strong concealment of the target, a camouflage target detection algorithm YOLO of camouflage object detection based on strong semantic information and feature fusion is proposed. First, the attention mechanism convolutional block attention
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Reconstruction error-assisted anomaly detection method for underground pipelines J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Jingjing Bai, Liwen Mei, Yiwen Wu, Xingming Feng, Yunpeng Cheng, Zhihong Yu, Yunpeng Ma
With the expanding scale of underground cable pipelines, the stable operation of underground power grid is essential for the orderly development of human production and life. However, once there are foreign objects, defects, or other anomalies in pipelines, it will lead to a series of problems, such as electric discharge, trip, and fire, seriously threatening the life and property safety of surrounding
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Optimized anchor-free network for dense rotating object detection in remote sensing images J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 He Yan, Ming Zhang, Ruikai Hong, Qiannan Li, Dengke Zhang
Extracting dense rotating objects accurately from remote sensing images is an emerging task in object detection. To increase the applicability of existing algorithms in the above tasks, an optimized anchor-free network optimized by a dual attention mechanism (DAM) and gate multiscale feature fusion (GMFF) is designed. The DAM module is composed of two attention mechanisms with different functions.
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Learned video compression with channel-wise autoregressive entropy model J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Yang Yu, Xiaohai He, Xiaohong Wu, Tingrong Zhang, Chao Ren
Although learned image compression methods have achieved competitive rate-distortion performances, learned video compression remains challenging. The current mainstream learned video compression frameworks usually improve the motion prediction module to reduce the redundancy in video sequences. Although these methods can achieve a great compression ratio, they often ignore the improvement in the entropy
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Lightweight infrared and visible image fusion network with edge-guided dual attention J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Xingyue Zou, Jiqiang Tang, Luqi Yang, Zhenhang Zhu
Existing methods for fusing infrared and visible images prioritize the fusion effect at the expense of the model size and inevitably tend to be more oriented toward infrared images during fusion, which results in fused images that can lack the texture detail information of visible images. Therefore, a new feature gradient attention block is designed in our model to extract the texture detail of the
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PDIN: a progressive image rain removal network based on camera imaging principle J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Wumingrui Zhu, Weiwei Yu
Traditional rain models typically focus on modeling images based on the physical characteristics and generation of rain streaks, which often obtain good results in the synthetic dataset. However, in real-world scenarios, the transformation of the camera depth of field (DOF) and the focusing position lead to different degrees of blur in different areas of the image. This variability poses challenges
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End-to-end image splicing localization based on multi-scale features and residual refinement module J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Zhihua Gan, Wenbin Jiang, Xiuli Chai, Yalin Song, Junyang Yu
The addition of objects to the original image in splice forgery results in a change in the semantics of the original image, and distribution of these spliced images may bring negative impacts. To solve this issue, many forgery detection methods based on convolutional neural networks are presented. However, they tend to extract deep features, but ignore the importance of shallow semantics. In addition
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Diabetic retinopathy classification using hybrid optimized deep-learning network model in fundus images J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Sesikala Bapatla, Jonnadula Harikiran
Many working-age people suffer from diabetic retinopathy (DR), one of the chronic retinal conditions caused by diabetes that eventually results in blindness. The classification of the DR severity level has been an arduous task due to the complexity of the lesion features. An efficient detection technique is needed for the screening procedure to categorize the retina’s subtle pathologies. Deep neural
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Attention UNet3+: a full-scale connected attention-aware UNet for CT image segmentation of liver J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Congping Chen, Jing Shi, Zhiwei Xu, Zhihan Wang
With the increasing global concern regarding public health, accurate diagnosis and treatment of diseases have become critical. In the context of liver computed tomography (CT) image diagnosis, obtaining precise liver segmentation output samples can save consultation time and reduce the risk of misdiagnosis. We propose a full-scale connected attention-aware segmentation network, called Attention UNet3+
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Image inpainting based on double joint predictive filtering and Wasserstein generative adversarial networks J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Yuanchen Liu, Zhongliang Pan
Image inpainting is promising but challenging in computer vision tasks; it aims to fill in missing regions of corrupted images with semantically sensible content. By utilizing generative adversarial networks (GAN), state-of-the-art methods have achieved great improvements, but the ordinary GAN generally suffers from difficulties in training and unstable gradients, leading to unsatisfactory inpainting
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Effective shape features for leaf classification J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Hongjuan Zhang, Chuyi Dai, Keming Long, Zhaobin Wang
When conducting image classification, traditional and deep-learning based methods require extracting target features in different ways and then classifying targets based on the features. Therefore, extracting more informative and effective features is very important, and this problem also exists in plant leaf classification. To address the issue for traditional leaf classification, this article carefully
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STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Chunmei Wang, Huan Liu, Xiaobao Yang, Sugang Ma, Zhonghui Jin
Due to the small size, high density, and background noise associated with strip surface defects, the current object detection model commonly faces limitations in performance. To address this issue, we propose a spatial-to-depth feature-enhanced detection method called STD-Detector. The method consists of two types STD-Conv-A and STD-Conv-B. First, the STD-Conv-A module is used in the backbone feature
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W-shaped network: a lightweight network for real-time infrared and visible image fusion J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Tingting Zhang, Huiqian Du, Min Xie
Autoencoder (AE) is widely used in image fusion. However, AE-based fusion methods usually use the same encoder to extract the features of images from different sensors/modalities without considering the differences between them. In addition, these methods cannot fuse the images in real time. To solve these problems, an end-to-end fusion network is proposed for fast infrared image and visible image
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Low-light image enhancement by two-stream contrastive learning in both spatial and frequency domains (Erratum) J. Electron. Imaging (IF 1.1) Pub Date : 2023-11-01 Yi Huang, Xiaoguang Tu, Gui Fu, Wanchun Ren, Bokai Liu, Ming Yang, Jianhua Liu, Xiaoqiang Zhang
Erratum corrects affiliation errors.