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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 6125 条记录，以下是311-320 订阅

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Towards Digitisation of Technical Drawings in Architecture: Evaluation of CNN Classification on the Perdaw Dataset 25th

Towards Digitisation of Technical Drawings in Architecture: ...

引用

25th International conference on Engineering applications of neural networks (EANN)

作者： Filip, Alexandru Grasshof, Stella IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031624940;9783031624957

In a highly digitalised world, this paper aims at closing the gap towards automatic digitisation from 2D architectural drawings. We present the new image dataset Plan, and Elevation Representations of Doors And Windows (Perdaw) which provides a baseline for different classification problems with varying complexity. We investigate the performance of three machine learning models in distinguishing different types of doors and windows in their plan and elevation views. Our findings show that Inception V3 slightly outperforms MobileNet V2, which suggests that the latter solves the same classification tasks with less computational resources with only a minimal compromise in accuracy. Among the three investigated models, ResNet50 yields the lowest quality metrics within a small margin. Overall, all models perform better at classifying building components in their elevation views compared to their plan views. We consistently observed that the models yield the best results with 100% accuracy for the binary classification problems, and dropped to close to 70% accuracy for the 40-class classification problems.

关键词： Deep learning Convolutional neural networks Object Classification Technical Symbols Architectural Symbols

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

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

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional neural networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Investigating the Use of Generative Adversarial networks and Recurrent neural networks for Medical image Segmentation 5th

Investigating the Use of Generative Adversarial Networks and...

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

作者： Kumar, Deepak Wadhwa, Bhawna Krishnamoorthy, Ramkumar Agarwal, Ankita Department of Mathematics Vivekananda Global University Jaipur India 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

ISBN: (纸本)9789819780426

This paper investigates the usage of generative opposed networks (GANs) and recurrent neural networks (RNNs) for medical photo segmentation. First, clinical picture segmentation is described and discussed, after which the two fundamental methods of segmentation, particularly supervised and unsupervised studying, are reviewed. Most generative models are reviewed, including generative opposed networks (GANs) and recursive neural networks (RNNs). Primarily, the paper specializes in utilizing GANs and RNNs for scientific image segmentation. The contribution of this paper is threefold. First, it provides an in-depth review of the nation of the artwork in generative fashions, focusing on the software of GANs and RNNs for scientific photograph segmentation. 2nd, it provides a discussion of the challenges and possibilities of the usage of GANs and RNNs for medical image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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learning color constancy: 30 years later 31

Learning color constancy: 30 years later

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31st Color and Imaging conference - Color Science and Engineering Systems, Technologies, and applications, CIC 2023

作者： Buzzelli, Marco Schettini, Raimondo Bianco, Simone Department of Informatics Systems and Communication University of Milano - Bicocca Milan20126 Italy

ISBN: (纸本)9780892083640

The first paper investigating the use of machine learning to learn the relationship between an image of a scene and the color of the scene illuminant was published by Funt et al. in 1996. Specifically, they investigated if such a relationship could be learned by a neural network. During the last 30 years we have witnessed a remarkable series of advancements in machine learning, and in particular deep learning approaches based on artificial neural networks. In this paper we want to update the method by Funt et al. by including recent techniques introduced to train deep neural networks. Experimental results on a standard dataset show how the updated version can improve the median angular error in illuminant estimation by almost 51% with respect to its original formulation, even outperforming recent illuminant estimation methods. © 2023 Society for Imaging Science and Technology.

关键词： Deep neural networks

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(POSTER) Visual Transformer Models on UAV-Captured images for Person Detection 20

(POSTER) Visual Transformer Models on UAV-Captured Images fo...

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20th Annual International conference on Distributed Computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Zahia, Ionel-Gabriel Popescu, Dan Ichim, Loretta Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9798350369458;9798350369441

One of the most important applications of UAVs is person detection for security or rescue tasks. The goal of the proposed paper is to develop, experiment, and compare the performance of two new neural networks based on the transformer architecture, Detection Transformer and Vision Transformer. Two datasets were used, an own one for testing and COCO for learning. The results are promising to take into account the difficulties of person detection at a distance.

关键词： unmanned aerial vehicle person detection image processing neural networks performance metrics

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Brain Tumour Detection using Deep learning: A Review 15

Brain Tumour Detection using Deep Learning: A Review

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavyasri, Nela Akkala, Varsha Srivani Pabboju, Suresh Chaitanya Bharathi Institute of Technology Information Technology Hyderabad India

ISBN: (纸本)9798331300579

Brain tumour to be effectively treated, it is essential to identify and diagnose *** image processing has advanced tremendously in recent years due to machine techniques for learning. Convolutional neural networks (CNNs) are deep learning models that have shown remarkable performance in several health care applications, such as the identification of brain tumours that are malignant. CNNs are a useful tool for clinical image analysis because they are capable of extracting pertinent features from enormous datasets without the necessity of feature engineering But problems including the absence of standardised datasets, the requirement for substantial volumes of annotations, and the comprehensibility of the models continue to be issues. Despite these difficulties, machine learning algorithms have tremendous possibilities for healthcare picture interpretation, and recent developments like explainable artificial intelligence, federated training, and transferred learning show promise for overcoming these restrictions. Additionally, new potential for machine learning algorithms to enhance brain tumour detection and diagnosis have been created by developments in imaging technologies including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). A patient's health can be shown in greater detail by the combination of digital health records and other sources of healthcare data, which can help healthcare providers make better treatment decisions. In conclusion, the identification and evaluation of brain tumours have demonstrated encouraging results when using machine learning algorithms, notably CNNs. However, further research is necessary to address the current challenges and limitations and to fully realise the potential of this technology in improving patient outcomes. © Grenze Scientific Society, 2024.

关键词： Positron emission tomography

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Advanced CNN-SVM machine learning Techniques for Facial Skin Ultrasound image Analysis 19th

Advanced CNN-SVM Machine Learning Techniques for Facial Skin...

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19th International conference on Artificial Intelligence: Methodology, Systems, and applications, AIMSA 2024

作者： Nabeel, Aayad Ragheb, Mostafa Momcheva, Galina Kamar, Issa Hamady, Mohamad Al-Mansour University College Baghdad Iraq Faculty of Sciences Lebanese University Tripoli Lebanon Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria Arts Sciences and Technology University in Lebanon Beirut Lebanon

ISBN: (纸本)9783031815416

This study investigates the machine learning techniques for unsupervised image classification and quality assessment in the domain of ultrasound imaging. Leveraging Convolutional neural networks (CNNs) for feature extraction and subsequent integration into a Support Vector machine (SVM) model, we explored a novel approach aimed at accurate image classification. The dataset comprises high-frequency images in the form of image sequences depicting the facial skin of females. The study's primary emphasis was to categorize ultrasound images based on learned deep features, offering a distinctive framework for unsupervised image classification. The investigation employed CNNs to extract deep features from images, enhancing the SVM model's performance in accurately categorizing images. The incorporation of gamma correction as a preprocessing step further augmented the accuracy and sensitivity of the models. The SVM model exhibited exceptional performance, achieving accuracy rates exceeding 95.43% in the training phase and approximately 94.72% during testing, that is a significant milestone in the precise classification of ultrasound images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Rosette Plant Centre Detection and Tracking using YOLO: An Efficient Deep learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd International conference on Computing and machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer Vision image processing Deep learning neural networks

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ShadowGAN for Line Drawings Shadow Generation 32nd

ShadowGAN for Line Drawings Shadow Generation

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32nd International conference on Artificial neural networks (ICANN)

作者： Xue, Huanhuan Kang, Chunmeng Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9783031442001;9783031442018

Shadow performs an important role in the image, which can enhance the image effect and convey important visual clues. We propose a method based on deep learning to automatically generate stylized shadows for line drawings. Based on StarGAN, a shadow generation adversarial network (ShadowGAN) is designed, which can automate the creation of stylized shadows with different light directions. This method defines eight light directions. Users can select one of the eight light directions around the 2D image to specify the light source according to the encoding of the light direction, and generate the shadow corresponding to the light direction. We use a new dataset containing line drawings with shadows and label information corresponding to the light direction. Experiments show that our method can generate stylized shadows for line drawing with satisfactory quality, which can simplify the user's workflow, and save the time of drawing line drawing shadows.

关键词： Stylized shadow Line drawings Deep learning ShadowGAN

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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...

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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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