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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Nada Haj Messaoud Neirez Zina Manel Mili Rihab Ben Dhia Asma Ben Abdallah Mohamed Hedi Bedoui Medical Technology and Image Processing Laboratory Univ. of Monastir Tunisia Higher Institute of Computer Science and Mathematics of Monastir Monastir Tunisia Faculty of Medicine of Monastir Univ. of Monastir Tunisia

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

In this paper, we present a hybrid approach for segmenting multiple sclerosis (MS) lesions and predicting disability progression. Using Concat-UNet for accurate MS lesion segmentation and a multi-class UNet for brain lobe segmentation, we enable regional analysis of lesion impact. By fusing MRI, clinical, and demographic data, we enhanced EDSS prediction, with LightGBM achieving a high R2 score (0.96 ± 0.07)

关键词： Multiple sclerosis Accuracy Magnetic resonance imaging Feature extraction Lesions Informatics

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Dataset and Metric for Quality Assessment of HDR Tone Mapping: Detail Visibility, Color Naturalness, and Overall Quality

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IEEE Transactions on Multimedia 2025年

作者： Jiang, Qiuping Li, Xiwen Wang, Xinyi Wang, Zhihua Zhai, Guangtao Ningbo University School of Information Science and Engineering Ningbo315211 China Hangzhou DtDream Science and Technology Ltd. Hangzhou310024 China City University of Hong Kong Department of Computer Science Kowloon999077 Hong Kong Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China

Tone-Mapping Operators (TMOs) aim at converting high dynamic range (HDR) images into standard dynamic range (SDR) ones that are suitable for being displayed on standard screens. As the visual quality of tone-mapped image (TMI) is paramount, conducting quality assessment of TMIs becomes crucial. Despite the growing body of research on TMI quality assessment, the existing metrics are often limited to a narrow selection of hand-picked examples generated by a restricted range of TMOs. Consequently, their ability of generalizing to the wide array of TMIs encountered in practical scenarios remains unclear. Moreover, the quality degradation in practical TMIs can be intricate, diverse, and complex. To overcome these limitations, we construct so far the largest subjective-annotated TMI quality assessment dataset which comprises a total number of 14,000 TMIs generated by applying 20 representative TMOs to 700 HDR images. The dataset is accompanied by subjective scores that encompass multiple quality dimensions, i.e., TMI quality dataset in terms of Detail visibility, Color naturalness, and overall Quality (TDCQ). In addition, we also design a multi-branch deep neural network tailored to characterize the multi-dimensional quality perception of TMIs, i.e., Color naturalness-, Detail visibility-aware TMI Quality (CDTIQ) metric, allowing for a comprehensive and multifaceted quality assessment of TMIs. Through extensive experiments, we demonstrate the superiority of our proposed metric, showcasing a higher correlation with subjective rating results compared to other relevant no-reference image quality metrics. Our newly constructed TDCQ database and the implementation of our CDTIQ model are available at https://***/zzeu001/CDTIQ. © 1999-2012 IEEE.

关键词： Mapping

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation 17

Scientific Research Transformation Needed for Comprehensive ...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Medennikov, Viktor Bogatyreva, Larisa Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive, system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform. © 2024 IEEE.

关键词： Agricultural economics

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Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons

Deeper Bidirectional Neural Networks with Generalized Non-Va...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

The new NoVa hidden neurons have outperformed ReLU hidden neurons in deep classifiers on some large image test sets. The NoVa or nonvanishing logistic neuron additively perturbs the sigmoidal activation function so that its derivative is not zero. This helps avoid or delay the problem of vanishing gradients. We here extend the NoVa to the generalized perturbed logistic neuron and compare it to ReLU and several other hidden neurons on large image test sets that include CIFAR-100 and Caltech-256. Generalized NoVa classifiers allow deeper networks with better classification on the large datasets. This deep benefit holds for ordinary unidirectional backpropagation. It also holds for the more efficient bidirectional backpropagation that trains in both the forward and backward directions.

关键词： Backpropagation Training Neurons Machine learning Delays Biological neural networks Logistics

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image Registration of High Resolution Chandrayaan-2 Data

Image Registration of High Resolution Chandrayaan-2 Data

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ridhi Makharia Jai G. Singla Amitabh Nitant Dube Harish Sharma Department of AIML School of Computer Science Engineering Manipal University Jaipur Rajasthan India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Accurate and reliable detection of features, along with effective mapping and registration, has been a topic of considerable discussion. Detecting and identifying craters on the lunar surface poses significant challenges due to subtle intensity variations and shadow effects. Conventional corner and edge detection methods often struggle to outline these craters, complicating the image registration. This paper proposes a novel strategy for data preprocessing to enhance edge and corner detection for OHRC and NAC datasets. Utilizing SIFT (Scale-Invariant Feature Transform) and ASIFT (Affine Scale-Invariant Feature Transform) algorithms, the study identifies and maps key points between the OHRC (data to be registered) and NAC (reference) datasets, achieving high pixel-level registration accuracy (within the range of 0.2-2.5 pixels). The application of image stitching and warping, results in georeferenced images that are both accurate and precise, contributing to improved navigation and enhanced image quality.

关键词： image quality image registration Accuracy Navigation image edge detection Moon Transforms Feature extraction Reliability image stitching

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Audio-Visual Quality Assessment for User Generated Content: Database and Method

Audio-Visual Quality Assessment for User Generated Content: ...

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IEEE International Conference on image processing

作者： Yuqin Cao Xiongkuo Min Wei Sun Xiaoping Zhang Guangtao Zhai Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University

With the explosive increase of User Generated Content (UGC), UGC video quality assessment (VQA) becomes more and more important for improving users’ Quality of Experience (QoE). However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the user’s QoE also depends on the accompanying audio signals. In this paper, we conduct the first study to address the problem of UGC audio and video quality assessment (AVQA). Specifically, we construct the first UGC AVQA database named the SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences, and conduct a user study to obtain the mean opinion scores of the A/V sequences. The content of the SJTU-UAV database is then analyzed from both the audio and video aspects to show the database characteristics. We also design a family of AVQA models, which fuse the popular VQA methods and audio features via support vector regressor (SVR). We validate the effectiveness of the proposed models on the three databases. The experimental results show that with the help of audio signals, the VQA models can evaluate the perceptual quality more accurately. The database will be released to facilitate further research.

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SADDLE: Spacecraft Anomaly Detection using Deep Learning

SADDLE: Spacecraft Anomaly Detection using Deep Learning

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Ankit Srivastava Neeraj Badal BS Manoj Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India Signal and Image Processing Area (SIPA) Space Applications Centre (SAC) Ahmedabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

Efficiently detecting anomalies in spacecraft data poses a significant challenge in modern space missions. We introduce Spacecraft Anomaly Detection using Deep Learning (SADDLE), a novel transformer network-based model tailored for spacecraft anomaly detection. SADDLE leverages an attention-based encoder to analyze telemetry data, capturing broader temporal trends critical for anomaly identification. It utilizes self-conditioning for robust feature extraction across multiple telemetry modalities. Additionally, SADDLE leverages Meta Gradient Descent to adapt faster to spacecraft data characteristics, simultaneously enabling effective training with limited anomaly examples. Extensive evaluations conducted on spacecraft datasets demonstrate that SADDLE outperforms all existing methods while significantly reducing training time.

关键词： Space vehicles Training Deep learning Adaptation models Space missions Transformers Market research

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Automatic Viewpoint Estimation for Inspection Planning Purposes 25th

Automatic Viewpoint Estimation for Inspection Planning Purpo...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Dutta, Siddhartha Rauhut, Markus Hagen, Hans Gospodnetić, Petra Department of Computer Science Technische Universität Kaiserslautern Kasierslautern67663 Germany Department of Image Processing Fraunhofer ITWM Kaiserslautern67663 Germany

ISBN: (纸本)9783030687984

Viewpoint estimation is an important aspect of surface inspection and planning. Typically viewpoint estimation has been done only with the 3D model and not with the actual object. This, therefore, limits the flexibility of using the actual object in inspection planning. In this work, we present a novel pipeline that can efficiently estimate viewpoints from live camera images. The pipeline can be used for different sized industrial objects to efficiently estimate the viewpoint. The achieved results are real-time and the method is easily generalizable to different objects. The presented method is based on a 3D model of the object, which is self-supervised and requires no manual data annotation or real images to be used as an input. The complete solution, together with documentation and examples is available in the public domain for testing. https://***/dutta/real-time-pose-est/. © 2021, Springer Nature Switzerland AG.

关键词： Pipelines

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Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Corvin-Petruţ Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap.

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