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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1642 条记录，以下是11-20 订阅

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Design and Implementation of image Description Model Using Artificial Intelligence Based Techniques 3rd

Design and Implementation of Image Description Model Using A...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ingale, Sumedh Bamnote, G.R. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9789819719457

The process that produces written descriptions that effectively represent the meaning and context of an image is known as image captioning. To integrate visual and textual data, it needs to blend computer vision and natural language processing methods. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such as long-short-term memory (LSTM) networks, are two methods used for captioning images. High-level visual features from the input image are extracted by the CNN, and the RNN uses those features to generate the matching captions. Residual Network is a deep CNN architecture with exceptional results in a range of computer vision tasks, including the classification of images. ResNet has been used as the foundation for extracting picture features. ResNet and CNN abstracts an image's visual information via feature extraction and uses neural networks with recurrent architecture to provide meaningful, contextually appropriate captions. Machines are now able to comprehend and speak about the visual world in a manner similar to humans because of the integration of computer vision and natural language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Automated Dental Cavity Detection in Front Teeth using image processing 3

Automated Dental Cavity Detection in Front Teeth using Image...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hampiholi, Amogh G. Kusanur, Vrunda Sujaya, B.L. R N S Institute of Technology Bengaluru India B N M Institute of Technology Bengaluru India

ISBN: (纸本)9798331515911

The increase in number of cases in dental caries has entailed the need for sophisticated diagnostic tools for timely detection and treatment. This work presents an innovative dental caries detection methodology using OpenCV, a well-known open-source computer vision library. By utilizing image processing techniques, this method accurately identifies and analyzes caries. The four stages involved in caries discovery involve image pre-processing, segmentation, feature extraction and classification. image preprocessing enhances quality of the image and removes noise, followed by segmentation, that identifies and isolates the specific region of interest. Next, feature extraction approaches identify potential caries regions by analyzing their morphological characteristics. In the next step, classification algorithm differentiates accurately between healthy and caries-affected areas. Experimental outcomes show the efficiency of this approach, presenting a high accuracy in caries detection. This automated system can not only assist in early diagnosis, but also supports dental professionals in providing improved patient care by reducing the time and subjectivity associated with traditional investigation techniques using own-trained haarcascades files. The novelty of this work is the development of the new, own-trained model in the OpenCV haarcascades toolbox as it does not contain any explicit model for detecting dental caries. © 2025 IEEE.

关键词： image segmentation

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Deep Learning for Automated image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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Faster-RCNN in Human Detecting on Thermal images 3rd

Faster-RCNN in Human Detecting on Thermal Images

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

作者： Anh, Le Tuan Ngan, Tran Thi Dung, Vu Viet Luc, Do Dinh Nguyen, To Huu Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam International School Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9783031809422

By the development of Artificial Intelligence, many applications of deep learning and computer vision algorithms in object detection on color images were introduced. The obtained results get higher accuracy than other classical methods. However, detecting objects on thermal images has not received much research attention. In this paper, we focus on researching and proposing the use of Faster-RCNN deep learning model in order to detect people on thermal images. The proposed model consists of two phases: Data processing and model applying. The experiments on a thermal image dataset, namely as Deep Thermal Outdoor, are performed to evaluate performance of the proposed model and compare it to YOLO and SSD models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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TrOCR-Enhanced Language Chains: Bridging image-Based Text and Single Document Question Answering 3rd

TrOCR-Enhanced Language Chains: Bridging Image-Based Text an...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Althaf, Patan Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

This research aims to integrate TrOCR, an advanced Optical Character Recognition (OCR) technology, with the Langchain framework for Document question answering on image-based queries. The college has many number of students and they face many problems when it comes to the preparation for exams. There may be many question papers they have to prepare manually by searching the answers in books. Our model uses TrOCR to extract text from input images and the large text data is split using Langchain. Vector indexing is to process the text. This integration helps in information retrieval and question answering. The VectorstoreIndexCreator facilitates text vectorization and indexing, and HuggingFaceEmbeddings for semantic understanding. This research aims for information retrieval, document analysis, and natural language processing, offering a connection between image-based text and advanced language technology where users can have easy handling of the model and provides comprehensive answers for given complex questions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

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Tumor Detection and Analysis from Brain MRI images Using Deep Learning 3rd

Tumor Detection and Analysis from Brain MRI Images Using Dee...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Singh, Shweta Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi Uttar Pradesh Varanasi India

ISBN: (纸本)9789819743582

Establishment of appropriate diagnosis plan for brain tumors is imperative and influences patient survival rate ultimately. Misdiagnosis of some brain tumors though hinders response to treatment and decreases success rate. Traditional ways of discerning brain tumors based on images taken via an MRI machine can be slow and full of mistakes, particularly when processing huge datasets and various shapes of tumor. It is proposed in this study that through such an innovative machine-learning and image processing hybrid technique, brain tumor detection can be enhanced. More precisely, we undertook bilateral filtering and normalization to improve quality of MR images, so as to decrease human errors in tumors’ identification. Therefore, the ResNet-50 model was employed to my significant patterns of data from the preprocessed dataset for further investigation. We did a comparative evaluation of different algorithms for getting the best classifier. Hence, based on the data, one can say with 91.40% accuracy that SVC outperformed all other models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental Learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

Data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental Learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Deep Learning Technique for computer vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Adaptive Vehicle Trajectory Clustering Based on computer vision 3rd

Adaptive Vehicle Trajectory Clustering Based on Computer Vis...

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3rd international conference on Cognitive Mobility, CogMob 2024

作者： Agg, Áron Dávid Péter, Bence Gábor Horváth, András Doctoral School of Multidisciplinary Engineering Sciences Széchenyi István University Győr Győr Hungary Széchenyi István University Győr Győr Hungary Department of Physics and Chemistry Széchenyi István University Győr Győr Hungary

ISBN: (纸本)9783031817984

Intelligent Transportation Systems is a rapidly evolving and extensively researched field. The development of tools and methods necessitated the appearance of a new concept, Cognitive Mobility, covers greater integration of transport related areas. Our research goes beyond simple image processing and statistical analysis and includes cognitive elements of mobility, for example, gives methods for adaptive analysis of typical vehicle behavior in junctions and helps in decision making for better utilization of infrastructure. In this work, we present an adaptive approach to interpreting traffic scenes. This approach uses the YOLOv7 object detector and DeepSORT tracking algorithm to generate trajectories of moving vehicles in video recordings captured from a stationary viewpoint. The paper examines and compares various techniques for clustering the vehicle trajectories, such as using clustering algorithms to group vehicles based on their entry and exit points within the video scene. Furthermore, we report the results of clustering parameter sensitivity tests performed on the video dataset we created. This control over the granularity of the clustering enables generating street-level or lane-level traffic data from the same recordings. Finally, we demonstrate some practical applicability of the method through illustrative examples, showcasing how the generated traffic data can be used to monitor and analyze traffic patterns, identify congestion points, and inform transportation planning and decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer vision Intelligent Transportation Systems Traffic Monitoring Trajectory Clustering

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

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Disruptive Technologies (ICDT), international conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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