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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 6071 条记录，以下是41-50 订阅

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Finding Identity through Gait 6

Finding Identity through Gait

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Srinivasan, Lavanya University of West London School of Computing and Engineering London United Kingdom

ISBN: (纸本)9798331506520

Human gait identification is the recognition of a person from a series of walking images. In contrast to fingerprint or iris-based identification methods, gait identification offers the significant benefit of remote execution. Gait detection is emerging as one of the most promising biometric identification techniques. Traditional methods to identify the identity of gait are Background subtraction, Gait Energy image, Gait Entropy, etc., human gait identification is a potential new tool for identifying individuals beyond traditional methods. The objectives of this study were to develop an automated gait detection system and examine the unique aspects of gait. This study encompassed the following: gait predictions and estimation based on HRNet 17 joint positions (locating body joints of humans in an image/video), better predictions with higher confidence scores by eliminating poor predictions, and the 14 joint coordinates and 17 joint points are fed into Graph Convolutional Network by eliminating low confidence pose to classify labels. Semi-supervised learning on graph-structured data can be accomplished via Graph Convolutional networks. Graph Convolutional networks is a very effective version of convolutional neural networks, which function directly on graphs. The experimental results show that the Graphical Convolutional Network identifies gait subjects with higher accuracy of normal walking and carrying a backpack with 98% and 96% respectively. This work will be extended in the future by fusing face and gait to identify various gait subjects and the gait in forensic identifications. © 2025 IEEE.

关键词： Convolutional neural networks

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ActivMedica—MRI Brain image Detection & Captioning with ActivGPT Integration

ActivMedica—MRI Brain Image Detection & Captioning with Act...

引用

International conference on Advances and applications of Artificial Intelligence and machine learning, ICAAAIML 2023

作者： Malayil, Shanid Krishna, P. Sudeep Pattupara, Rabeen Mohamed, P. M. Rishil Faris, Salman Suhair, K. Muhammed Ahmed, P. Jemsheer MEA Engineering College Kerala Perinthelmanna India

ISBN: (纸本)9789819795062

ActivMedica is an innovator in brain tumor diagnosis in the rapidly changing healthcare industry, employing cutting edge AI tools including deep learning and natural language processing (NLP). The shortcomings of existing manual approaches for medical imaging data analysis include difficulty in identifying diverse brain tumors and increasing medical expenditures. Our approach, which at first is based on transfer learning and GIT architecture, uses the GIT convolutional neural network, or CNN, model. A carefully chosen and preprocessed diverse dataset, enhanced for variety and improved by transfer learning from the imageNet dataset for feature extraction, is essential for successful model training. Using the GIT-based methodology, ActivGPT, an AI chatbot built on OpenAI, enables real-time image analysis through natural language conversations. In addition to brain tumor detection, our research generates brief medical reports based on detected brain tumors, providing medical experts with comprehensive information. Brain tumor diagnosis is ethically improved by ActivMedica, a comprehensive healthcare innovation that combines AI, deep learning, and natural language processing. By doing this, diagnosis accuracy is greatly increased while moral and ethical requirements for AI use in healthcare are maintained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Approach to Deploying Convolutional neural networks Based on FPGA Technology for image Recognition 3rd

An Approach to Deploying Convolutional Neural Networks Based...

引用

3rd International conference on Advances in Information and Communication Technology, ICTA 2024

作者： Phuong, Huy Nguyen Mai, Thuong Duong Thi Thai Nguyen University of Technology Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

Nowadays, deep learning architectures like CNN have proven their superiority in image recognition tasks. To effectively deploy CNN networks in practice, especially for AIoT applications, it is essential to find a network model that offers good recognition performance with a small size and a limited number of parameters. Additionally, when deploying to hardware, to ensure rapid task execution, an FPGA-based approach is a suitable choice due to its parallel processing capabilities, low power consumption, low latency, and reconfigurability. In this paper, the authors propose a solution for searching and implementing an optimal CNN model on an FPGA chip for handwritten digit recognition using the MNIST dataset. The comparative results of recognizing 10,000 MNIST image samples on FPGA, CPU, and Raspberry Pi 4 microcontroller demonstrate that the proposed solution achieves high accuracy (98.78%) while maintaining low execution time (36 s). This indicates the potential application of the proposed solution in deploying deep learning tasks in practical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Attention-Based Recurrent neural networks for Medical image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

引用

5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent neural networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Classification of Brain Tumor images Optimizing the Convolutional neural Network EfficientNet 3rd

Classification of Brain Tumor Images Optimizing the Convolut...

引用

3rd International conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wicker, Alberto Hanne, Thomas Dornberger, Rolf Medical Informatics University of Applied Sciences and Arts Northwestern Switzerland Muttenz Switzerland Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Olten Switzerland

ISBN: (纸本)9789819624676

This study investigates medical image classification employing Convolutional neural networks (CNNs), Support Vector machine (SVM) and Genetic Algorithm (GA) focusing on hyperparameter optimization for the task of Brain Tumor diagnosis with Magnetic Resonance Imaging (MRI) scans. Different data categories and attributes determine the most effective CNN model for categorizing images correctly according to pre-established classification features. The aim is to employ a hybrid CNN-SVM model to facilitate the exhaustive task of multi-class classification and determine optimal hyperparameters suitable for the diagnostic task using a GA. This paper analyzes critical factors affecting CNN performance, focusing on parameters that substantially influence the model effectiveness. The considered models (CNN, CNN-SVM, CNN-SVM-GA) are compared to contribute to the knowledge of CNN applications in image analysis in the medical radiological field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot learning Approach 6

Low-Data Classification of Historical Music Manuscripts: A F...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Shatri, Elona Raymond, Daniel Fazekas, Gyorgy Queen Mary University of London Centre for Digital Music London United Kingdom Queen Mary University of London Big Data Science London United Kingdom

ISBN: (纸本)9798331506520

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques. © 2025 IEEE.

关键词： Self-supervised learning

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Attentional Mechanisms for Enhancement in the Convolutional neural Network Task of Recognizing Handwritten Chinese Character Styles

Attentional Mechanisms for Enhancement in the Convolutional ...

引用

2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Peng, Yuwei School of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9798331504205

This paper investigates the enhancement of attention mechanism in the task of recognizing handwritten Chinese character styles by convolutional neural networks (CNNs). With the wide application of deep learning in image recognition, convolutional neural network has become a mainstream method for processing image tasks due to its excellent feature extraction capability. However, in complex handwritten Chinese character recognition, the performance of traditional CNN models is still somewhat limited, especially in the task of recognizing different styles of calligraphic glyphs. For this reason, this paper proposes an attention mechanism, that can improve the model's ability to pay attention to important information, thus improving the accuracy of the model in recognizing different handwritten Chinese character patterns. By conducting experiments on the Chinese Calligraphy Styles by Calligraphers dataset, the paper shows that adding an attention mechanism significantly improves the functionality of CNN models on the task of recognizing different fonts. The result demonstrates that the attention mechanism can effectively mitigate the recognition difficulties due to the variation of calligraphic fonts, and provides new ideas for future applications in more complex scenarios. © 2025 IEEE.

关键词： Convolutional neural networks

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Dynamic-Feature-Based Object Tracking Using Real-Time image processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

引用

5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. The proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. The proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Building Damage Assessment in Conflict Zones: A Deep learning Approach Using Geospatial Sub-Meter Resolution Data 6

Building Damage Assessment in Conflict Zones: A Deep Learnin...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Risso, Matteo Goffi, Alessia Motetti, Beatrice Alessandra Burrello, Alessio Bove, Jean Baptiste Macii, Enrico Poncino, Massimo Pagliari, Daniele Jahier Maffeis, Giuseppe Politecnico di Torino Italy TerrAria S.R.L. Italy Croce Rossa Italiana Italy

ISBN: (纸本)9798331506520

Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large areas is time-consuming and requires domain expertise. Thanks to their accuracy, generalization capabilities, and highly parallelizable workload, Deep neural networks (DNNs) provide an excellent way to automate this task. Nevertheless, there is a scarcity of VHR data pertaining to conflict situations, and consequently, of studies on the effectiveness of DNNs in those scenarios. Motivated by this, our work extensively studies the applicability of a collection of state-of-the-art Convolutional neural networks (CNNs) originally developed for natural disasters damage assessment in a war scenario. To this end, we build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios, demonstrating their potential and limitations. To the best of our knowledge, this is the first study to use sub-meter resolution imagery to assess building damage in combat zones. © 2025 IEEE.

关键词： Zero-shot learning

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Integrating Long Short-Time Period Reminiscence networks for Medical Photograph Classification 5th

Integrating Long Short-Time Period Reminiscence Networks for...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Agarwal, Chhaya Kamalraj, R. Singh, Vaishali Dadhich, Atul Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

L lengthy short-time period memory (LSTM) networks are present-day recurrent neural networks that have been proven effective in numerous regions such as natural language processing, speech popularity, and computer vision. Recently, there was a growing hobby within the software cutting-edge LSTM networks to scientific image type. The gain of ultra-modern usage of LSTM networks in the scientific image category is that they must remember the temporal styles modern day information and effectively integrate the local and worldwide representations of today's photograph. Moreover, they have been proven to be a powerful device to capture temporal patterns in longitudinal, multi-modal information. In this paper, we overview the current methods for clinical picture type and LSTM network usage and the demanding situations and possibilities related to this approach. We additionally provide a top-level view of cutting-edge ability packages for modern LSTM networks in clinical imaging and provide some critical guidelines for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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