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Weakly supervised video anomaly detection based on spatial-temporal feature fusion enhancement

signal image AND VIDEO processing 2024年第2期18卷 1111-1118页

作者： Liang, Weijie Zhang, Jianming Zhan, Yongzhao Jiangsu Univ Sch Comp Sci & Commun Engn Xuefu Rd 301 Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Engn Res Ctr Big Data Ubiquitous Percept & Zhenjiang 212013 Jiangsu Peoples R China

When an abnormal event occurs, the features of the video will gradually change in the temporal sequences. Since the abnormal event is a continuous process, the abnormal features should have a continuous prominence compared to the normal features. Incorporating this differential information is critical for effective anomaly detection. Nevertheless, prior multi-instance learning (MIL) methods have overlooked this aspect. Therefore, this study proposes the spatial-temporal feature fusion enhancement (STFFE) learning approach to address this issue. STFFE improves the discriminative power of normal and abnormal features by enhancing temporal information through the fusion of top-k video segment features with their corresponding temporal information features. Furthermore, a temporal information constraint is proposed to increase the concentration of abnormal information and accentuate the saliency of abnormal features. Extensive experimentation demonstrates that our approach surpasses state-of-the-art (SOTA) methods on the Shanghai-Tech and XD-Violence datasets. Moreover, our method achieves competitive results on the UCF-Crime dataset, with an AUC of 84.52%. The results demonstrate the effectiveness of the proposed method.

关键词： Weakly supervised video anomaly detection neural network Fusion enhancement Top-k mechanics

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Medical image fusion method based on saliency measurement improvement and local structure similarity correction

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BIOMEDICAL signal processing AND CONTROL 2024年 89卷

作者： Pan, Qing Li, Yunhang Tian, Nili Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China

Medical image fusion is an important tool in medical diagnosis, because the quality of the fused image is closely related to the diagnosis result. However, the approaches of highlighting details through the acquisition of high-contrast lead to information distortion in the fused image and then increase the misdiagnosis rate. In order to solve the problem, a medical image fusion method based on saliency measurement improvement and local structural similarity correction is proposed in this paper. First, multi-level decomposition latent low rank representation (MDLatLRR) combined with nonsubsampled shearlet transformation (NSST) is presented to decompose source images to obtain high SNR base layers and multi-scale detail layers. Then, the saliency measurement improvement with the source image mask filtering is advanced for base layer fusion rule. Specially, after the base layer is refined by the source image mask for the salient area extraction, the salient area is refreshed by its mean gray to calculate the fusion coefficient. Finally, a correction method based on structural similarity index measure (SSIM) is put forward to deal with the information distortion in the initial fused image, in which the correction weight is acquired through the weighted structural similarity between the initial fused image and the source ones. Experiments show that our method has excellent subjective visual effects, and outperforms the other states-of-the-art methods in objective indexes.

关键词： image fusion Nonsubsampled shearlet transform Multi-level decomposition latent low-rank representation Saliency measurement Pulse coupled neural network

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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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Detecting HI Galaxies with Deep neural Networks in the Presence of Radio Frequency Interference

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Research in Astronomy and Astrophysics 2023年第11期23卷 38-50页

作者： Ruxi Liang Furen Deng Zepei Yang Chunming Li Feiyu Zhao Botao Yang Shuanghao Shu Wenxiu Yang Shifan Zuo Yichao Li Yougang Wang Xuelei Chen National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 101408China Department of Physics Northeastern UniversityBostonMA 02115USA Biomedical Instrument Institute School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmology and Astrophysics(Liaoning) College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China

In the neutral hydrogen(H I)galaxy survey,a significant challenge is to identify and extract the H I galaxy signal from the observational data contaminated by radio frequency interference(RFI).For a drift-scan survey,or more generally a survey of a spatially continuous region,in the time-ordered spectral data,the H I galaxies and RFI all appear as regions that extend an area in the time-frequency waterfall plot,so the extraction of the H I galaxies and RFI from such data can be regarded as an image segmentation problem,and machine-learning methods can be applied to solve such *** this study,we develop a method to effectively detect and extract signals of H I galaxies based on a Mask R-CNN network combined with the PointRend *** simulating FAST-observed galaxy signals and potential RFI impact,we created a realistic data set for the training and testing of our neural *** compared five different architectures and selected the best-performing *** architecture successfully performs instance segmentation of H I galaxy signals in the RFI-contaminated time-ordered data,achieving a precision of 98.64%and a recall of 93.59%.

关键词： methods:data analysis methods:observational techniques:image processing

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Lightweight Plank Grading Based on a Multichannel Spatial Attention Mechanism

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APSIPA TRANSACTIONS ON signal AND INFORMATION processing 2024年第1期13卷

作者： Li, Ziqiang Wang, Huixuan Lv, Na Guo, Yihong Zhang, Yan Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China China Mobile Grp Shandong Co Ltd Jinan 250013 Peoples R China Mstar Machinery Co Ltd Fei County 273400 Peoples R China Shandong Jianzhu Univ Sch Sci Jinan 250101 Peoples R China

The grading and sorting process of the wood planks is a critical stage within the production line. However, in the real world, many factories still rely on humans to perform this task manually. This method is not only inefficient, but also time-consuming and laborintensive. To solve this problem, a lightweight wood board image classification algorithm based on a multichannel spatial attention mechanism is proposed in this paper, which can be used for the real-time classification of wood planks on the production line. This method is used to classify freshly rotated cut wood planks based on defects such as damage and voids on the production line. Specifically, the received images of the wood planks were processed by a feature extraction module to effectively separate the interfering background from the foreground of the wood planks. After fusing the edge information map with the foreground image of the wood planks, a multichannel convolutional neural network with spatial and channel attention ability was used to learn the features for correctly grading the wood planks. Experimental results show that the proposed method is superior to traditional methods and some existing deep learning algorithms in terms of performance and benefits.

关键词： Wood plank image grading real-time classification feature fusion lightweight neural network convolutional neural network

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Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning

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IEEE signal processing MAGAZINE 2023年第1期40卷 134-147页

作者： Chen, Dongdong Davies, Mike Ehrhardt, Matthias J. Schonlieb, Carola-Bibiane Sherry, Ferdia Tachella, Julian Univ Edinburgh Computat Sensing & Machine Learning Edinburgh EH8 9YL Midlothian Scotland Univ Edinburgh Signal & Image Proc Edinburgh EH9 3JL Midlothian Scotland European Assoc Signal Proc Sesimbra Portugal Royal Acad Engn London England Univ Bath Bath BA2 7AY Avon England Univ Cambridge Cambridge CB3 0WA England Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England Ecole Normale Super Lyon CNRS F-69364 Lyon France

From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry. Here symmetry refers to the invariance property of signal sets to transformations, such as translation, rotation, or scaling. Symmetry can also be incorporated into deep neural networks (DNNs) in the form of equivariance, allowing for more data-efficient learning. While there have been important advances in the design of end-to-end equivariant networks for image classification in recent years, computational imaging introduces unique challenges for equivariant network solutions since we typically only observe the image through some noisy ill-conditioned forward operator that itself may not be equivariant. We review the emerging field of equivariant imaging (EI) and show how it can provide improved generalization and new imaging opportunities. Along the way, we show the interplay between the acquisition physics and group actions and links to iterative reconstruction, blind compressed sensing, and self-supervised learning.

关键词： Deep learning neural networks Imaging signal processing algorithms Self-supervised learning Noise measurement Iterative methods

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Ts-vit: feature-enhanced transformer via token selection for fine-grained image recognition

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signal image AND VIDEO processing 2025年第1期19卷 1-11页

作者： Wang, Yingge Liang, Hu Wen, Changchun Zhao, Shengrong Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist EducShandong Comp Sci Ctr Jinan Peoples R China Qilu Univ Technol Shandong Acad Sci Fac Comp Sci & Technol Shandong Engn Res Ctr Big Data Appl Technol Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China Inspur Genersoft Co Ltd Jinan Peoples R China

Fine-grained image recognition is a challenging task that focuses on identifying images from similar subordinate categories. Recently, methods based on the vision transformer (ViT) have demonstrated remarkable achievements in fine-grained image recognition, which inherent multi-head self-attention (MHSA) can effectively capture the discriminative regions in images. However, most of these ViT-based methods ignore the channel relationships of image features, and there are also problems with the inconsistent learning performance of different heads in MHSA and different layers in ViT. To address these issues, an innovative feature-enhanced transformer is proposed, named TS-ViT. TS-ViT includes three key modules: soft channel attention (Soft-CA), multi-head token selection (MHTS), and multi-level feature enhancement (MLFE). The Soft-CA enables the model to concentrate on the relationships among various channels of image features. The MHTS is proposed to address the issue of inconsistent multi-head learning performance. It selects tokens with discriminative region positions based on attention maps to form the multi-level feature. By employing contrastive learning and enhanced feature extraction, the MLFE is proposed to effectively utilize multi-level features while mitigating background noise. Extensive experiments have demonstrated that TS-ViT achieves superior performance compared to popular methods, with average accuracy of 91.8%, 91.2%, 99.5%, and 93.9% on the experimental data sets, respectively. Furthermore, TS-ViT demonstrated outstanding performance in terms of computational complexity and efficiency, with an average parameter count of 93.5M, FLOPs of 73.2G, training time of 7.8 h, and inference time of 4.1 milliseconds.

关键词： Fine-grained image recognition Vision transformer Multi-head self-attention neural networks

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Multi-user watermarking based on paralleled invertible neural networks

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signal image AND VIDEO processing 2025年第6期19卷 1-12页

作者： Shi, Jinghui Yang, Zhiguang Li, Li Ye, Jingyu Dong, Zhicheng Wu, Hanzhou Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Digitial Guangxi Grp Guilin 541001 Guangxi Peoples R China Guangxi Normal Univ Guilin 541001 Guangxi Peoples R China Tibet Univ Lhasa 850000 Xizang Peoples R China

With the rapid development of artificial intelligence technology, deep-faked audios have become increasingly prevalent in cyberspace, creating an urgent need for effective detection and tracing mechanisms. Watermarking offers an active defense strategy for identifying deep-faked audios, and the integration of multi-user watermarking further enhances its protection and tracking capabilities. By leveraging multi-bit watermarking technology, multiple independent watermarks can be embedded within a single audio file, providing unique identifiers for different stakeholders (e.g., lyricists, composers, platforms, etc.). This enables granular content protection and precise source tracing across diverse scenarios. For instance, users can upload audios embedded with both personal watermarks (e.g., lyricist and composer) and platform watermarks, ensuring better monitoring of audio dissemination and usage. Existing methods for embedding multi-user watermarks often require training multiple networks, which can lead to interference during the training process. To address this challenge, we propose an audio watermarking technique based on Paralleled Invertible neural Networks (PINN). Our approach enables the splitting of a single network into multiple sub-networks for multi-user watermark embedding while requiring only one network to be trained. Specifically, we design a parallel-structured invertible neural network that is highly flexible, allowing the network to be divided into multiple sub-networks for watermark rewriting or multi-user watermark embedding. These embedded watermarks remain independent of each other, ensuring robust and interference-free operations. Experiments demonstrate the framework's robustness, achieving an average BER of 1.8% and SNR of 26.5 dB when embedding four 32-bit watermarks, and also exhibits strong resistance to common distortions, such as noise addition, MP3 compression, and re-sampling. Furthermore, partial rewriting experiments highlight the f

关键词： Invertible neural networks Audio Deep learning Digital watermarking

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