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30th IEEE International Conference on image processing (ICIP)

作者： Prummel, Wieke Giraldo, Jhony H. Zakharova, Anastasia Bouwmans, Thierry La Rochelle Univ Lab Math Image & Applicat MIA La Rochelle France Inst Polytech Paris Telecom Paris LTCI Paris France

ISBN: (纸本)9781728198354

Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and testing data for evaluation. However, this assumption is not realistic in real-world applications where the system needs to handle new data during deployment. In this paper, we propose a novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on a Graph neural Network (GNN) architecture. Our approach builds a generic model capable of performing prediction on newly added data frames using the already trained model. GraphIMOS outperforms previous inductive learning methods and is more generic than previous transductive techniques. Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.

关键词： Moving object segmentation graph neural networks inductive learning graph signal processing

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image super-resolution based on self-similarity generative adversarial networks

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IET image processing 2023年第1期17卷 157-165页

作者： Wang, Shuang Sun, Zhengxing Li, Qian Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Jiangsu Vocat Inst Commerce Nanjing Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Peoples R China

Self-attention has been successfully leveraged for long-range feature-wise similarities in deep learning super-resolution (SR) methods. However, most of the SR methods only explore the features on the original scale, but do not take full advantage of self-similarities features on different scales especially in generative adversarial networks (GAN). In this paper, self-similarity generative adversarial networks (SSGAN) are proposed as the SR framework. The framework establishes the multi-scale feature correlation by adding two modules to the generative network: downscale attention block (DAB) and upscale attention block (UAB). Specifically, DAB is designed to restore the repetitive details from the corresponding downsampled image, which achieves multi-scale feature restoration through self-similarity. And UAB improves the baseline up-sampling operations and captures low-resolution to high-resolution feature mapping, which enhances the cross-scale repetitive features to reconstruct the high-resolution image. Experimental results demonstrate that the proposed SSGAN achieve better visual performance especially in the similar pattern details.

关键词： Optical, image and video signal processing Computer vision and image processing techniques neural nets

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A multi-view image fusion algorithm for industrial weld

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IET image processing 2023年第1期17卷 193-203页

作者： Zheng, Qingchun Zhao, Yangyang Zhang, Xu Zhu, Peihao Ma, Wenpeng Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intel Tianjin Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China

Multi-view image fusion can be used to extract features from redundant and complementary multisource images. And the technique of obtaining high quality fusion images has become one of the research hotspots for image processing. In order to realize defect detection and intelligent grinding smoothly, multi-view fusion technology was applied in the field of overexposure and underexposure industrial welds, achieving high quality image enhancement. When preparing the data set of multi-view images, a hybrid registration algorithm with high matching ability is proposed. The data set of overexposure and underexposure weld images was obtained successfully by using the registration algorithm. In order to improve the fusion ability of overexposure and underexposure industrial welds, we propose a novel multi-view image fusion algorithm based on deep learning. The multi-view fusion algorithm uses an autoencoder network structure, and its innovation lies in a parallel branch network with lightweight structure and strong generalization ability. The experimental results demonstrate that compared with other classical multi-view algorithms, our proposed algorithm gets the best parameters on the industrial weld data set in peak signal to noise ratio (PSNR) and root mean square error (RMSE), reaching 59.12 and 0.084, respectively. And the ablation and performance comparison experiments verify that the proposed parallel branch network has better generalization ability and fusion accuracy than other classical multi branch networks.

关键词： classical multibranch networks feature extraction data set high quality fusion images Sensor fusion image enhancement underexposure weld images multiview images classical multiview algorithms generalization ability parallel branch network lightweight structures multiview fusion technology hybrid registration algorithm overexposure welds grinding neural nets industrial weld data fusion ability Optical, image and video signal processing image fusion Computer vision and image processing techniques novel multiview image fusion algorithm high quality image enhancement deep learning (artificial intelligence) image processing underexposure industrial welds multiview fusion algorithm overexposure weld images fusion accuracy image registration high matching ability Interpolation and function approximation (numerical analysis) mean square error methods complementary multisource images image resolution geophysical image processing

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MFNet: Panoptic segmentation network based on multiscale feature weighted fusion and frequency domain attention mechanism

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IET COMPUTER VISION 2023年第1期17卷 88-97页

作者： Lei, Haiwei He, Fangyuan Jia, Bohui Wu, Qian North Univ China Sch Data Sci & Technol Taiyuan Peoples R China

Existing panoptic segmentation networks based on multiscale methods do not distinguish shared features according to different scales, which leads to a loss of feature information and suboptimal results. To solve this problem, the authors improve on UPSNet (a unified panoptic segmentation network) by introducing a multiscale fusion module cascaded between layers into the semantic segmentation head of UPSNet. On the one hand, this enriches the feature information for differently scaled features by cascading the feature information of adjacent scales, and on the other hand, the feature information at each scale can be combined adaptively to achieve a better segmentation effect. Furthermore, a frequency domain attention mechanism is introduced into the backbone network of UPSNet to improve the feature extraction ability of the backbone network by learning more frequency domain feature information. The experimental results of the authors' improved network on the Common Objects in Context (COCO) and Cityscapes data sets show that compared with UPSNet, the performance is significantly improved: the panoptic quality is improved by 0.9% and 1.6%, respectively.

关键词： differently scaled features image segmentation feature extraction unified panoptic segmentation network shared features authors frequency domain feature information multiscale fusion module multiscale methods neural nets UPSNet multiscale feature weighted fusion Computer vision and image processing techniques Optical, image and video signal processing convolutional neural nets segmentation effect semantic segmentation head feature extraction ability panoptic segmentation networks backbone network frequency domain attention mechanism

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DEEP SUB-image SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN ADVERSARIAL STEGANOGRAPHY 49

DEEP SUB-IMAGE SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Huang, Xinyu Cao, Yuwen Ohtsuki, Tomoaki Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Keio Univ Dept Informat & Comp Sci Tokyo Japan

ISBN: (纸本)9798350374520;9798350374513

Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks;ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.

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Balancing Rates and Variance via Adaptive Batch-Size for stochastic Optimization Problems

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3693-3708页

作者： Gao, Zhan Koppel, Alec Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Amazon Supply Chain Optimizat Technol Bellevue WA 98004 USA

stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, which forms the bedrock of modern machine learning. In this work, we seek to balance the fact that attenuating step-size is required for exact convergence with the fact that constant step-size learns faster to a limiting error. To do so, rather than fixing the mini-batch and the step-size at the outset, we propose a strategy to allow parameters evolving adaptively. Specifically, the batch-size is set to be a piecewise-constant increasing sequence where the increase occurs when a suitable error criterion is satisfied. Moreover, the step-size is selected as that which yields the fastest convergence. The overall algorithm, two scale adaptive (TSA) scheme, is developed for both convex and non-convex problems. It inherits the exact convergence and more importantly, the optimal error decreasing rate and an overall computation reduction are achieved. Furthermore, we extended the TSA method to the generalized adaptive batching framework, which is a generic methodology modular to any stochastic algorithms pursuing a trade-off between convergence rates and stochastic variance. We evaluate the TSA method on the image classification problem on MNIST and CIFAR-10 datasets compared with standard SGD methods and existing adaptive batch-size methods, to corroborate theoretical findings.

关键词： Convergence stochastic processes Optimization Training Statistics Sociology signal processing algorithms stochastic optimization stochastic gradient descent adaptive batch-size optimal step-size

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DOMAIN GENERALIZATION WITH FOURIER TRANSFORM AND SOFT THRESHOLDING 49

DOMAIN GENERALIZATION WITH FOURIER TRANSFORM AND SOFT THRESH...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Pan, Hongyi Wang, Bin Zhang, Zheyuan Zhu, Xin Jha, Debesh Cetin, Ahmet Enis Spampinato, Concetto Bagci, Ulas Northwestern Univ Dept Radiol Machine Hybrid Intelligence Lab Evanston IL 60208 USA Univ Illinois Dept Elect & Comp Engn Chicago IL USA Univ Catania Dept Elect Elect & Comp Engn Catania Italy

ISBN: (纸本)9798350344868;9798350344851

Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transform-based domain generalization methods have gained popularity primarily because they exploit the power of Fourier transformation to capture essential patterns and regularities in the data, making the model more robust to domain shifts. The mainstream Fourier-transform-based domain generalization swaps the Fourier amplitude spectrum while preserving the phase spectrum between the source and the target images. However, it neglects background interference in the amplitude spectrum. To overcome this limitation, we introduce a soft-thresholding function in the Fourier domain. We apply this newly designed algorithm to retinal fundus image segmentation, which is important for diagnosing ocular diseases but the neural network's performance can degrade across different sources due to domain shifts. The proposed technique basically enhances fundus image augmentation by eliminating small values in the Fourier domain and providing better generalization. The innovative nature of the soft thresholding fused with Fourier-transform-based domain generalization improves neural network models' performance by reducing the target images' background interference significantly. Experiments on public data validate our approach's effectiveness over conventional and state-of-the-art methods with superior segmentation metrics.

关键词： Domain generalization Fourier transform soft thresholding fundus image segmentation

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Analysis of microvascular pattern in diabetes mellitus condition using the nailfold capillaroscopy images

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2024年第3期238卷 340-347页

作者： Elumalai, Sowmiya Krishnamoorthi, Nirmala Periyasamy, Naveen Farazullah, Mohamed Raj, Kiran Mahadevan, Shriraam Sri Sivasubramaniya Nadar Coll Engn Dept Biomed Engn Chennai 603110 Tamil Nadu India Sri Ramachandra Med Coll & Hosp Dept Endocrinol Chennai Tamil Nadu India

Diabetes is often considered a vascular disease due to its impact on blood vessels, it is a complex condition with various metabolic and autoimmune factors involved. One of the long term comorbidities of diabetes includes microvascular complications. The microvascular complications can be analyzed using the Nailfold capillaroscopy, a non-invasive technique that allows for the visualization and analysis of capillaries in the proximal nailfold area. Using advanced video capillaroscopy with high magnification, capillary images can be captured from and processed to analyze their morphology. The capillary images of normal group and diabetic group are acquired from 118 participants using nailfold capillaroscopy and the obtained images are preprocessed using image processing filters. The identification and segmentation of the capillaries are the challenges to be addressed in the processing of the images. Hence segmentation of capillaries is done using morphological operations, thresholding and convolutional neural networks. The performance of the filters and segmentation methods are evaluated using Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Jaccard Index and Sorensen coefficient. By analyzing the morphological features namely the capillary diameter, density, distribution, presence of hemorrhage and the shape of the capillaries from both the groups, the capillary changes associated with diabetic condition were studied. It was found that the non diabetic participants considered in this study has capillary diameter in the range of 8-14 mu m and the capillary density in the range of 10-30 capillaries per mm2 whereas the diabetic participants has capillary diameter greater than 30 mu m and the capillary density is less than 10 capillaries per mm2. In addition to capillary density and diameter, the presence of hemorrhage, the orientation and distribution of the capillaries are also considered to differentiate the diabe

关键词： Diabetes nailfold capillaroscopy image processing convolutional neural networks performance metrics

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Exploring optimizer efficiency for facial expression recognition with convolutional neural networks

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JOURNAL OF ENGINEERING-JOE 2025年第1期2025卷

作者： Madni, Syed Hamid Hussain Pathmanatan, Lokessh A. L. Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Shah, Sajid Univ Southampton Malaysia Sch Elect & Comp Sci Johor Baharu Malaysia Univ Teknol Malaysia Fac Comp Skudai Johor Malaysia Univ Vaasa Sch Technol & Innovat Vaasa Finland VTT Tech Res Ctr Finland Ltd Espoo Finland Univ Sargodha Dept Comp Sci Sargodha Pakistan

It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore the importance of facial expressions and the development of machine-assisted recognition techniques. Significant progress is being made in facial and expression recognition, largely due to the rapid growth of machine learning and computer vision. A variety of algorithmic approaches and methods exist for detecting and recognizing facial expressions and features. This study investigates various optimization algorithms used with convolutional neural networks for facial expression recognition. The primary focus is on Adam, RMSProp, stochastic gradient descent and AdaMax optimizers. A comprehensive comparison is being made, examining the key aspects of each optimizer, including its advantages and disadvantages. Furthermore, the study also incorporates findings from recent studies that used these optimizers in various applications, highlighting their performance in terms of training time and precision. The aim is to illuminate the process of selecting a suitable optimizer for specific applications, analysing the trade-offs between training speed and higher accuracy levels. Moreover, this study provides a deeper analysis of the role optimizers play in machine learning-based facial expression recognition models. The discussion of the technical challenges posed by these optimizers and future improvements for achieving much more optimal results concludes the study.

关键词： image classification image processing image recognition optimisation convolution neural network machine leanring

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Weather-informed lightweight framework for robust smoke video detection using BFBlock-enhanced feature extraction

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signal image AND VIDEO processing 2025年第5期19卷 1-10页

作者： Li, Xinying Cheng, Pengle Liu, Xiaodong Huang, Ying Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Sch Ecol & Nat Conservat Beijing 100083 Peoples R China North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND 58102 USA

Frequent occurrences of forest fires globally result in significant harm to both human populations and the natural environment. Smoke is the earliest visual signal of wildfire and its efficient detection is crucial. However, due to the lack of real-world smoke video data, existing deep learning methods often face difficulties in effective training, and their generalization ability and detection stability in different scenarios are not validated and guaranteed. Since smoke characteristics are strongly correlated with environmental conditions, the weather state information in the smoke data is additionally exploited in this study to benefit the model training and optimization. This is a new approach to explore more useful information within limited smoke data, inspired by human perceptual tendencies. For the efficient modeling of smoke features and to achieve a trade-off between computational complexity and recognition accuracy, we further propose BFBlock for enhanced smoke feature extraction and build a two-stage lightweight video detection framework WISNet. Compared to regular 3DCNN models, the proposed framework reduces the model's parameters by 85% to only 5.6M, and achieves 96.69% accuracy and 00.58% false alarm rate. Extensive experimental results on real-world smoke samples prove the effectiveness of the proposed method.

关键词： Video smoke detection Auxiliary learning Convolutional neural network Lightweight model

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