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When Skeleton Meets Appearance: Adaptive Appearance Information Enhancement for Skeleton Based Action Recognition

When Skeleton Meets Appearance: Adaptive Appearance Informat...

引用

2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Wang, Suqin Zhou, Lu Chen, Yingying Huo, Jiangtao Wang, Jinqiao Institute of Automation Chinese Academy of Sciences National Laboratory of Pattern Recognition Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Development Research Institute of Guangzhou Smart City China Army Medical University Nco School of Pla China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Skeleton-based action recognition methods which utilize graph convolution networks (GCNs) have achieved remark-able success in recent years. However, action recognizer can be easily confused by the ambiguity caused by different actions with similar skeleton sequences when only skeleton data is trained. Introducing appearance information can effectively eliminate the ambiguity. Based on this, we introduce a two-stream network for action recognition. One trained on RGB images extracts appearance information. The other trained on skeleton data models motion information and adaptively captures appearance information of action areas at action-related intervals via a specially tailored attention mechanism. Our architecture is trained and evaluated on two large-scale datasets: NTU RGB+D and NTU RGB+D 120, and a small scale human-object interaction dataset Northwestern-UCLA. Experiment results verify the effectiveness of our method and the performance of our method exceeds the state-of-the-art with a significant margin. © 2022 ieee.

关键词： image enhancement

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Enhanced Color Palette Modeling For Lossless Screen Content Compression

Enhanced Color Palette Modeling For Lossless Screen Content ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hannah Och Shabhrish Reddy Uddehal Tilo Strutz André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Coburg Germany

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

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Logarithmic Lenses: Exploring Log RGB Data for image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Bruce A. Maxwell Sumegha Singhania Avnish Patel Rahul Kumar Heather Fryling Sihan Li Haonan Sun Ping He Zewen Li Northeastern University Boston USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： Training Computer vision image color analysis Pipelines Transforms visual systems Sensor systems

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Utilizing Deep Learning Architectures for Early Detection of Lung Diseases in Chest X-ray images 26

Utilizing Deep Learning Architectures for Early Detection of...

引用

26th International conference on Computer and Information Technology, ICCIT 2023

作者： Ahmed, Sarder Tanvir Mahdin, Mohammad Fairoz Barua, Shomtirtha Abir, Fahim Shariar Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

In addressing the critical need for early detection of COVID-19 and viral pneumonia, our study introduces an advanced system using deep learning for analyzing chest X-ray images and their respective lung masks. Utilizing the comprehensive COVID-QU-Ex dataset, we extensively trained and evaluated five pre-trained deep learning models (ResNet-18, VGG-16, AlexNet, Inception-V3, and DenseNet-169) and developed a novel ensemble model. This ensemble model, combining the strengths of the top-performing models, achieved a notable 96.002% prediction accuracy and 95% recall, outperforming the individual models. Our research goes a step further by integrating explainable AI methodologies, specifically LIME and SHAP, to provide understandable and transparent explanations of the model's decision-making process. These explanations are crucial for identifying critical areas of lung infections and enhancing the model's credibility. Furthermore, we deployed a user-friendly Flask web application, enabling easy access to our system for medical professionals. This application offers immediate analysis of uploaded X-ray images, delivering predictions, accuracy percentages, and interpretative visual explanations. Our work significantly advances the field by providing an accurate, explainable, and accessible tool for early lung disease detection, potentially revolutionizing the approach to healthcare diagnostics. © 2023 ieee.

关键词： Chest X-rays (CXR) Convolutional Neural Network (CNN) COVID-19 Data Mining Deep Learning Ensemble image processing LIME Machine Learning Pneumonia SHAP

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Automatic SPECT image processing for Parkinson’s Disease Early Detection 4th

Automatic SPECT Image Processing for Parkinson’s Disease Ea...

引用

4th International conference on Smart Applications and Data Analysis, SADASC 2022

作者： Boucherouite, Jihad Jilbab, Abdelilah Jbari, Atman E2SN ENSAM Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031204890

The absence of an effective cure to Parkinson as a neurodegenerative disease calls for early diagnosis and appropriate therapeutic process to provide patients with better quality of treatment. Therefore, to diagnose Parkinson’s Disease (PD) at its early stage, clinicians rely on visual observation of dopaminergic deficit in both caudate and putamen in the striatum region of the brain. SPECT images (Single Photon Emission Computed Tomography) are among functional neuroimaging scans that can show putamen and caudate, and hence help visualizing Dopamine deficit. In this work, we developed an automatic SPECT image model to classify patients as Healthy Control (HC) or Early PD, starting from Dicom SPECT images from PPMI (Parkinson’s Progression Markers Initiative) database to Machine learning classification. The approach we proposed starts with image processing of SPECT images, then extraction of boundary, radial, Striatal Binding Ratio (SBR) and threshold features, then classification using Support Vector Machine (SVM). To the best of our knowledge, no work in the literature has used this combination of the mentioned features together in the classification model. The use of this combination demonstrates promising results. We used a database of 526 images, with 130 HC and 396 PD. The results of our approach show that the Medium Gaussian SVM has a high performance with an accuracy of 97.3%, sensitivity of 95.3%, and specificity of 98%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Single photon emission computed tomography

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Research on the Application of Virtual Reality Technology in Digital Protection of Cultural Heritage Landscape

Research on the Application of Virtual Reality Technology in...

引用

Electronics, Energy Systems and Power Engineering (EESPE), ieee International conference on

作者： Yan Huang Yu Huang Nanning University Nanning China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper puts forward a kind of digital protection system based on VR, which uses 3D modeling and dynamic rendering to provide high quality digital protection and exhibition platform. First, the cultural heritage landscape is modeled with high precision through laser scanning and drone photogrammetry technology to obtain three-dimensional point cloud data. Subsequently, an efficient dynamic rendering algorithm is designed, which can enhance the realism and immersion of the landscape by simulating physical properties such as lighting, shadows, reflection and refraction. To ensure that the system can still run smoothly under high load, image processing optimization technology is used to stabilize the rendering frame rate at more than 60 frames per second and perform well under multiple dynamic scenes switching. At last, the paper builds a virtual reality simulation environment based on Unity and Unreal Engine. The experiment shows that the system can reconstruct the cultural heritage scene exactly, the rendering effect is high, and the user experience rate reaches 92%.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms Virtual environments Rendering (computer graphics) visual effects User experience image restoration Cultural differences Protection

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image to Audio Conversion to Aid visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

引用

4th International conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 ieee.

关键词： Convolutional neural networks

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image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology

Image Recognition and Analysis of Computer Interlock Interfa...

引用

ieee Advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Xuanmo Chen Qiang Li College of Computer Science and Technology Lanzhoujiaotong University Lanzhou China Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Province Lanzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (Optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces.

关键词： visualization Technological innovation image recognition Machine learning algorithms Optical character recognition Software algorithms Clustering algorithms

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy image edge detection visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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Color Agnostic Cross-Spectral Disparity Estimation

Color Agnostic Cross-Spectral Disparity Estimation

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Frank Sippel Nils Genser Hannah Och Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Since camera modules become more and more affordable, multi-spectral camera arrays have found their way from special applications to the mass market, e.g., in automotive systems, smartphones, or drones. Due to multiple modalities, the registration of different viewpoints and the required cross-spectral disparity estimation is up to the present extremely challenging. To overcome this problem, we introduce a novel spectral image synthesis in combination with a color agnostic transform. Thus, any recently published stereo matching network can be turned to a cross-spectral disparity estimator. Our novel algorithm requires only RGB stereo data to train a cross-spectral disparity estimator and a generalization from artificial training data to camera-captured images is obtained. The theoretical examination of the novel color agnostic method is completed by an extensive evaluation compared to state of the art including self-recorded multispectral data and a reference implementation. The novel color agnostic disparity estimation improves cross-spectral as well as conventional color stereo matching by reducing the average end-point error by 41 % for cross-spectral and by 22 % for mono-modal content, respectively.

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