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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Chauhan, Kaushal Tomar, Harshit Kamal, Kushagra Goel, Pallavi Galgotias College Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with 'features' is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN's, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer. © 2023 IEEE.

关键词： image segmentation

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Recognizing Indian Classical Dance Forms Using Transfer Learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

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4th International Conference on machine Intelligence and Signal processing, MISP 2022

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. Computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Advancements in Road Surface Classification: A Comprehensive Review of Computer vision and IoT Approaches

Advancements in Road Surface Classification: A Comprehensive...

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International Conference on Computer vision and Internet of Things 2023, ICCVIoT 2023

作者： Kovilpillai, Judeson Antony J. Dhar, Soumi Singh, Uday Kumar Prakash, S. Saidala, Ravi Kumar Jayanthy, S. Department of CSE Alliance University Anekal Bangalore India Department of Research and Innovation Alliance University Anekal Bangalore India Department of ECE Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9781839539848

The rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by investigating diverse methodologies within the realms of computer vision and the Internet of Things (IoT). Through an extensive investigation, various techniques encompassing image processing, machine Learning(ML), Deep Learning(DL), and IoT are examined for their effectiveness in classifying road surfaces at different terrains. Moreover, this article also reviews datasets, signals, sensors, communication protocols, IoT implementation strategies, pre-processing methodologies, and feature extraction techniques. This investigation delves into novel approaches aimed at resolving road surface classification challenges, meticulously examining their respective strengths and limitations. Furthermore, this article includes a comparative analysis of these advanced methodologies, facilitating the identification of the most suitable model for the task. The assessment takes into account intricate methodological aspects, types of sensors/cameras, dataset variations, and performance metrics, thereby providing valuable insights into the landscape of road surface classification for autonomous driving applications. © The Institution of Engineering & Technology 2023.

关键词： Internet of things

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11th Pacific-Rim Symposium on image and Video Technology, PSIVT 2023

11th Pacific-Rim Symposium on Image and Video Technology, PS...

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11th Pacific-Rim Symposium on image and Video Technology, PSIVT 2023

ISBN: (纸本)9789819703753

The proceedings contain 34 papers. The special focus in this conference is on image and Video Technology. The topics include: Spatial Variation Sequences for Remote Sensing applications with Small Sample Sizes;exploring the Potential of High-Resolution Drone imagery for Improved 3D Human Avatar Reconstruction: A Comparative Study with Mobile images;point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor;Efficient 3Dconv Fusion of RGB and Optical Flow for Dynamic Hand Gesture Recognition and Localization;an Investigation of Video vision Transformers for Depression Severity Estimation from Facial Video Data;real-Time Automated Body Condition Scoring of Dairy Cows;Logo-SSL: Self-supervised Learning with Self-attention for Efficient Logo Detection;HAHANet: Towards Accurate image Classifiers with Less Parameters;evaluating Mammogram image Classification: Impact of Model Architectures, Pretraining, and Finetuning;melanoma Classification Using Deep Learning;3D Formation Control of Multiple Cooperating Autonomous Agents via Leader-Follower Strategy;LAPRNet: Lightweight Airborne Particle Removal Network for LiDAR Point Clouds;REAL-NET: A Monochromatic Depth Estimation Using REgional Attention and Local Feature Mapping;Spike-EFI: Spiking Neural Network for Event-Based Video Frame Interpolation;scrambleMix: A Privacy-Preserving image processing for Edge-Cloud machine Learning;Comparison of Simplified SE-ResNet and SE-DenseNet for Micro-Expression Classification;facial Deepfake Detection Using Gaussian Processes;A Novel Steganography Scheme Using Logistic Map, BRISK Descriptor, and K-Means Clustering;a Holistic Approach to Elderly Safety: Sensor Fusion, Fall Detection, and Privacy-Preserving Techniques;cluster-Based Video Summarization with Temporal Context Awareness;On Deploying Mobile Deep Learning to Segment COVID-19 PCR Test Tube images;enhancing Safety During Surgical Procedures with Computer vision, Artificial Intelligence, and Natural

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CNN-based On-board Intelligent processing for Remote Sensing images Interpretation

CNN-based On-board Intelligent Processing for Remote Sensing...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Xiaojie Cui Yuanyuan Liu Xuehua Chen Gang Wang Beijing Institute of Remote Sensing Information Beijing China

Traditional remote sensing image processing is not able to provide timely information for near real-time applications due to the hysteresis of satellite-ground mutual communication and low processing efficiency. On-board intelligent processing is an important approach to improve the efficiency and intelligence of remote sensing satellites. This paper takes convolutional neural network (CNN) based on-board processing as the focus. Firstly, the basic workflow of CNN based on-board processing system is illustrated. Afterwards, the applications of lightweight CNN based on-board processing are thoroughly reviewed. The used CNN models are further analyzed to compare the advantages and disadvantages. Finally, current challenges are summarized and future works concerned with artificial intelligence are concluded.

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Multi-modal and multi-dimensional biomedical image data analysis using deep learning

Multi-modal and multi-dimensional biomedical image data anal...

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作者： Wang, Yangyang University of Missouri

学位级别：博士

There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early diagnosis, and scientific discovery. Recent advances in artificial intelligence and machine learning, particularly in deep learning, have revolutionized computer vision and image analysis for many application areas. While processing of non-biomedical signal, image, and video data using deep learning methods has been very successful, high-stakes biomedical applications present unique challenges such as different image modalities, limited training data, need for explainability and interpretability etc. that need to be addressed. In this dissertation, we developed novel, explainable, and attention-based deep learning frameworks for objective, automated, and quantitative analysis of biomedical signal, image, and video data. The proposed solutions involve multi-scale signal analysis for oraldiadochokinesis studies; ensemble of deep learning cascades using global soft attention mechanisms for segmentation of meningeal vascular networks in confocal microscopy; spatial attention and spatio-temporal data fusion for detection of rare and short-term video events in laryngeal endoscopy videos; and a novel discrete Fourier transform driven class activation map for explainable-AI and weakly-supervised object localization and segmentation for detailed vocal fold motion analysis using laryngeal endoscopy videos. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of biomedical data, that is of great value for potential early diagnosis and effective disease progress or treatment monitoring.

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SRUSTHI: super local bubble tracking inspired by machine vision

SRUSTHI: super local bubble tracking inspired by machine vis...

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IEEE International Ultrasonics Symposium (IUS)

作者： Sripada, Siva Saket Porter, Tyrone M. Univ Texas Austin Dept Biomed Engn Austin TX 78712 USA

ISBN: (数字)9781665466578

ISBN: (纸本)9781665466578

Super-resolution (SR) is a fascinating frontier in medical ultrasound (US) imaging offering the possibility of studying biological activity at spatiotemporal scales beyond the classical diffraction limit [1]. The key to SR is reliable detection and subsequent tracking of centroids of US contrast agents, over thousands of frames [1]. However, methods to overcome motion artefacts and background tissue speckle impose computational overhead [2];in addition to physical tradeoffs in data acquisition [1][3];thereby limiting biological applications to larger vessels with high blood flow rates [1]. The real-time or online nature of ultrasound imaging is sacrificed due to the offline nature of super-resolution processing methods [1]. In this work, we explore combinations of current machine vision algorithms, popular for similar object detection and tracking problems in optical imaging [4] - towards near real-time [5] super-resolution ultrasound imaging. We report encouraging results motivating further work towards improving state-of-the-art machine vision models designed for online, real-time, detection and tracking for ultrasound super-resolution.

关键词： biomedical ultrasound super-resolution computer vision machine learning image processing real-time

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Performance analysis of saliency detection in images

Performance analysis of saliency detection in images

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3rd International Conference on Advancement in Nanoelectronics and Communication Technologies (ICANCT)

作者： Reddy, Maram Sreerama Nithya, B. Natl Inst Technol Comp Sci & Engn Dept Trichy 620015 Tamil Nadu India

Detecting salient objects in an image has a vast number of applications across the web, and mostly it is done manually. Since the internet is growing faster than ever, it is not a feasible solution for high-scale dynamic applications. Hence, image processing automation with computer vision and machine learning has become a burning topic of research in the past few years. Saliency based image cropping is a task of identifying the notable segments in an image and be able to accommodate these parts in the crop ratio for any given specific viewport. It helps not only in image cropping but also with object recognition, visual tracking and visual restoration, etc. To address this, a novel approach for saliency detection is proposed based on heat map analysis and boundary prior. In this approach, the images go through heat map anal-ysis to identify salient objects based on machine learning model. After that, the images are segmented based on color and texture difference and then major contours are identified. The contours and the obtained saliency coordinates are accommodated inside the crop for each viewport requirement. The crop coordinates then go through two more processes, firstly a shift center process where the crop center is moved towards the important but lesser salient object, and then an inclusivity rule checks that the image is not cropped at coordinates without any salient objects. The simulation results reveal that the proposed algorithm attains better results than the cutting-edge algorithm of Twitter with similarity index of 89.89%.(c) 2022 Elsevier Ltd. All rights *** and peer-review under responsibility of the scientific committee of the 3rd International Con-ference on "Advancement in Nanoelectronics and Communication Technologies".

关键词： Saliency detection image processing Viewport Heat map Inclusivity rule Similarity index

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An evaluation of platforms for processing camera-trap data using artificial intelligence

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METHODS IN ECOLOGY AND EVOLUTION 2023年第2期14卷 459-477页

作者： Velez, Juliana McShea, William Shamon, Hila Castiblanco-Camacho, Paula J. Tabak, Michael A. Chalmers, Carl Fergus, Paul Fieberg, John Univ Minnesota Dept Fisheries Wildlife & Conservat Biol St Paul MN 55108 USA Smithsonians Natl Zoo & Conservat Biol Inst Conservat Ecol Ctr Front Royal VA 22630 USA Univ Los Andes Dept Ciencias Biol Bogots Colombia Quantitat Sci Consulting LLC Toronto ON Canada Liverpool John Moores Univ Sch Comp Sci & Math Liverpool Merseyside England

Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost-effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera-trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera-trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera-trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy-to-use interfaces. We review key characteristics of four AI platforms-Conservation AI, MegaDetector, MLWIC2: machine Learning for Wildlife image Classification and Wildlife Insights-and two auxiliary platforms-Camelot and Timelapse-that incorporate AI output for processing camera-trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either 'blank' or 'animal'. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera-trap-data processing by allowing users to filt

关键词： artificial intelligence camera traps computer vision data processing deep learning image classification remote sensing review

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information Technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of Computer applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs Computer vision Deep learning machine learning Thoracic anomalies

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