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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6111 条记录，以下是201-210 订阅

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A Cnn Model for Computationally Efficient Haze Removal in Railway Monitoring images

A Cnn Model for Computationally Efficient Haze Removal in Ra...

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International conference on Knowledge and Smart Technology (KST)

作者： Pitchayapatchaya Srikram Atid Aumchoo Anurak Khamchaimongkhon Panrawee Rungskunroch Dept. Computer engineering RMUTT Pathum thani Thailand Dept. Industrial Engineering RMUTT Pathum thani Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Haze degrades the accuracy of computer vision algorithms for railway monitoring applications. This study proposed a dehazing algorithm emphasizing image quality, performed on GPUs and CPUs rather than on the IoT or low power devices, as a CNN-based haze removal model for an edge device. Our optimized CNN model utilized the ADD operation to relieve concatenation between two convolutional layers. The model was tested on a TFlite file, with results showing acceptable SSIM and PSNR image quality assessment values. The average computational time was $\mathbf{2 1 0}$ milliseconds on an Intel Xeon processor while a Raspberry Pi 4 Model B, operating as an offline edge inference device, achieved a processing time of $\mathbf{2 8 0}$ milliseconds per image.

关键词： image quality Performance evaluation Computational modeling image edge detection Tiny machine learning Rail transportation Computational efficiency Convolutional neural networks Monitoring Testing

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Segmentation Aided Multiclass Classification of Lung Disease in Chest X-ray images using Graph neural networks

Segmentation Aided Multiclass Classification of Lung Disease...

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International conference on Information Networking

作者： Iftekharul Islam Shovon Ijaz Ahmad Seokjoo Shin Dept. of Computer Engineering Chosun University Gwangju Korea Dept. of Electrical and Computer Engineering Korea University Seoul Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Deep learning (DL)-based medical image classification has become a pivotal research area in computer vision, significantly enhancing the diagnostic process across the medical field. DL-based image classification can be aided by supplementary data, further improving its performance. One prominent example of such data is gaze-points, which involve eye-tracking techniques to document radiologists’ interaction with images. Recently, gaze-point data has been used as a pre-processing method to map an image into a graph, enabling the use of graph neural networks (GNNs) in computer vision domain. Such techniques have demonstrated significant improvements compared to conventional convolution neural networks (CNNs); however, their reliance on human involvement makes them labor-intensive, thus limiting their applications. To address this, we propose to leverage DL-based automatic segmentation mask generation to prepare image data as input for the GNN. This technique uses the segmentation mask as the attention information to guide the classifier. The results demonstrate that the proposed segmentation-aided classification model surpasses conventional CNN models and delivers the same performance as the existing supplementary data-aided techniques while reducing manual labor.

关键词： image segmentation Computer vision Limiting Pulmonary diseases Manuals Gaze tracking Graph neural networks Medical diagnostic imaging X-ray imaging image classification

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Satellite image Classification Using a Lightweight CNN-SVM Hybrid Model

Satellite Image Classification Using a Lightweight CNN-SVM H...

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International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Nowshad Hasan Md. Saiful Islam Md. Tanvir Hayat Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Satellite image classification holds significant importance across various applications, including agriculture, urban planning, and disaster management, due to the necessity for effective data analysis. Conventional CNN models require learning a large number of parameters, which can be time-consuming and often too complex. This study addresses the issues by introducing an effective feature selection technique that combines a lightweight convolutional neural network (CNN) with a Support Vector machine (SVM) classifier. The aim of this proposed lightweight CNN is to attain maximum accuracy using a smaller number of parameters. The proposed method consists of three distinct sections: pre-processing, feature extraction, and classification. Firstly, images are preprocessed through resizing, histogram equalization, and a sharpening filter to increase the quality of the image. Then the lightweight CNN is used for effective feature extraction, and finally SVM is used for classifying the satellite images. The proposed lightweight CNN-SVM model achieves a high accuracy of 98.0% on the publicly available RSI-CB256 dataset.

关键词： Support vector machines Histograms Accuracy Computational modeling Urban planning Disaster management Feature extraction Satellite images Convolutional neural networks Remote sensing

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Leveraging Artificial neural networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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Computational, Communication and Information Technology (ICCCIT), International conference on

作者： Aadya Goel Pallavi Mishra Rachna Bhatia School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial neural networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses.

关键词： Accuracy Mortality Artificial neural networks machine learning Breast cancer Planning Information and communication technology Reliability Prognostics and health management Medical diagnostic imaging

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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

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Computational Intelligence, Communication Technology and Networking (CICTN), International conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes machine learning algorithms Generative AI neural networks Digital forensics Signal processing algorithms Authentication Media Feature extraction Information integrity

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Segmentation of Ship Propeller Cavitation Area Using image-Based Anomaly Detection Network

Segmentation of Ship Propeller Cavitation Area Using Image-B...

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International image processing, applications and Systems conference (IPAS)

作者： Wonje Jeong Yong-Jin Shin Soon-Yong Park School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea Samsung Heavy Industries Co. Ltd. Daejeon Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. The pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. The proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision.

关键词： Vibrations Deep learning Pressure sensors image segmentation Propellers Annotations Artificial neural networks Transformers Marine vehicles Anomaly detection

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Enhancing the Compressed Video Quality through a neural Network Approach

Enhancing the Compressed Video Quality through a Neural Netw...

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Sentiment Analysis and Deep learning (ICSADL), International conference on

作者： Karthick Panneerselvam K. Rajalakshmi P. Sornalatha Department of Computer Science & Engineering KPR Institute of Engineering and Technology Uthupalayam Tamil Nadu India Department of MCA CMR Institute of Technology Bengaluru Karnataka India Department of Computer Applications Alagappa University Karaikudi Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. The main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other neural networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. The researchers are also proposing a new data pre-processing method based on the Deep Recurrent neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video Coding (AVC). The results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC.

关键词： Video coding Recurrent neural networks PSNR image coding Superresolution Graphics processing units Video compression Generative adversarial networks Encoding Standards

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Deepfake image and Video Detection Using Deep learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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A Hybrid Deep learning Model for Dance Recognition with applications in Robotic Vision and AI Automation

A Hybrid Deep Learning Model for Dance Recognition with Appl...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Anthonia Oluwatosin Adediran Adefemi Ayodele Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Malaysia Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia University of East London London UK

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition has gained significant attention due to advancements in artificial intelligence (AI) and machine learning. Traditional dance instruction often relies on subjective feedback, leading to inconsistencies in assessment. This paper presents a hybrid deep learning model integrating Convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks for dance movement recognition. CNNs extract spatial features, while LSTMs capture temporal dependencies, enabling accurate recognition of dance sequences. The dataset used combines ballet, locking, and popping dance genres, with preprocessing techniques such as augmentation and normalization. The hybrid model achieves 99.52% accuracy, surpassing standalone CNN and LSTM models. Evaluation metrics, including Matthews Correlation Coefficient (MCC) and AUC-ROC, validate the model's ability to distinguish complex movement patterns with high reliability. Beyond dance recognition, precise motion classification plays a crucial role in robotic vision and AI-driven automation, where accurate pose estimation and movement tracking enable applications in humanoid robot motion imitation, gesture-based human-robot interaction, and precision automation in industrial workflows. Future work will focus on real-time deployment, dataset expansion, and integration with augmented reality to enhance both dance training and humanoid robot motion learning.

关键词： Deep learning Humanities Automation Accuracy Service robots Humanoid robots Human-robot interaction Real-time systems Convolutional neural networks Long short term memory

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Deep learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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Computational, Communication and Information Technology (ICCCIT), International conference on

作者： RVS Praveen Meenakshi Maindola Anandhi R J Nagarjuna Thandra Saloni Bansal Vishal Sharma Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Information Science Engineering New Horizon College of Engineering Bangalore Dept. Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged.

关键词： Deep learning Autonomous underwater vehicles Accuracy Transfer learning Object detection Data augmentation Real-time systems Data models Environmental monitoring Convolutional neural networks

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