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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3293 条记录，以下是21-30 订阅

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A New video Summarization Approach Using Object Density Based image Similarity for Smart City applications 29

A New Video Summarization Approach Using Object Density Base...

引用

29th International conference on Information Technology, IT 2025

作者： Altundogan, Turan Goktug Karakose, Mehmet Mert, Fatih Manisa Celal Bayar University Computer Engineering Department Manisa Turkey Firat University Computer Engineering Department Elazig Turkey Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Turkey

ISBN: (纸本)9798331517649

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches. © 2025 IEEE.

关键词： image Similarity Object Detection Smart City video Summarization

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Facial Deblurring and Recognition Using image processing and machine Learning Techniques 8th

Facial Deblurring and Recognition Using Image Processing and...

引用

8th IFIP TC 12 International conference on Computer, Communication and Signal processing, ICCCSP 2024

作者： Subrahmanyam, B.R. Janavi, T.S. Keerthana, V. Serajudeen, Ayesha Jumana Arunnagiri, A.M. Department of Electronics and Communication Engineering College of Engineering Guindy Anna University Chennai600025 India

ISBN: (纸本)9783031736162

The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement. © IFIP International Federation for Information processing 2025.

关键词： Optical transfer function

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Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

引用

1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial Intelligence, and machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： machine vision

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In-domain Self-supervised Learning for Plankton image Classification on a Budget

In-domain Self-supervised Learning for Plankton Image Classi...

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2025 IEEE/CVF Winter conference on applications of Computer vision Workshops, WACVW 2025

作者： Ciranni, Massimiliano Gjergji, Ani Maracani, Andrea Murino, vittorio Pastore, vito Paolo University of Genoa MaLGa Dibris Italy Istituto Italiano di Tecnologia Genoa Italy University of Verona Italy

ISBN: (纸本)9798331536626

In the last few years, the abundance of available plank-ton images has significantly increased due to advancements in acquisition system technology. Consequently, a growing interest in automatic plankton image classification has surged. machine learning algorithms have recently emerged to assist in the analysis of this vast quantity of data, supporting traditional manual processing. However, annotating such data is costly and demands significant time and resources, thus requiring data-efficient machine learning solutions. The typical framework for tackling this issue has been the adoption of supervised imageNet pre-trained models, and fine-tuning them on the plankton classification downstream task. Nonetheless, self-supervised pre-training protocols may provide an effective alternative to the supervised approaches using imageNet, while allowing the exploitation of the increasingly large amount of unanno-tated plankton data. To the best of our knowledge, no work systematically analyzes the impact of self-supervised pre-training protocols for plankton image classification. To fill this gap, in this paper, we present a thorough comparison between in-domain (plankton images) and out-of-domain (imageNet) supervised and self-supervised pre-training, in terms of the quality of the corresponding embeddings for plankton image classification. We believe that this work may pave the way for further research in self-supervised protocols for the plankton domain, providing a valuable alternative to imageNet, and exploiting the vast amount of unannotated available plankton images. © 2025 IEEE.

关键词： Self-supervised learning

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Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th International conference on Data Science, machine Learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net Dataset

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Electoral Symbols and Vote detection in Paper Ballots - A Case from Nepal's Election 17

Electoral Symbols and Vote detection in Paper Ballots - A Ca...

引用

17th International conference on machine vision, ICMV 2024

作者： Shrestha, Raju Acharya, Suraj Department of Computer Science Oslo Metropolitan University Oslo Norway

ISBN: (纸本)9781510688278

This paper investigates the potential of advanced object detection technologies to automate and enhance the accuracy and efficiency of the vote counting process in democratic elections that utilize paper-based ballots with electoral symbols. The study focuses on detecting electoral symbols and votes on paper ballots by utilizing two state-of-the-art object detection models: Faster R-CNN, and YOLO. These models were fine-tuned by training them with the ballot papers created using the dataset prepared from the electoral symbols used in Nepal's general election to ensure high accuracy and reliability in recognizing and validating votes. The system's effectiveness was demonstrated through a comparison of the models, highlighting the superior performance of Faster R-CNN in terms of precision, despite its slower processing speed compared to YOLO. The results indicate that incorporating object detection technologies into electoral systems can significantly improve efficiency of vote counting process. The study underscores the potential for broader applications of these technologies in promoting transparent and fair elections, especially in countries like Nepal, where traditional paper ballots are still prevalent. This innovative approach ensures a more reliable and efficient electoral process, reducing human error and increasing trust in election outcomes. © 2025 SPIE.

关键词： Object detection Systems modeling image processing Performance modeling Transparency Data modeling Artificial intelligence Deep learning

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Research on visual Target Detection and Recognition of Shopping Robots Based on Improved YOLO Algorithm 7th

Research on Visual Target Detection and Recognition of Shopp...

引用

7th International conference on Wireless Communications, Networking and applications, WCNA 2023

作者： Lu, Yufan Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9789819624089

This research aims to improve the visual target detection and recognition capabilities of shopping robots in various sales environments by optimizing and improving the YOLO algorithm, in order to improve accuracy and real-time performance. The research method involves embedded spatial hierarchical sampling technology and it adapts to image processing of different sizes, uses a separate convolutional neural network structure to reduce computational complexity, and cultivates a more concise network model by refining the effective data of complex models. Experimental results show that the improved YOLO algorithm performs well in weak Its average accuracy has been significantly improved under light, medium light and strong light environments, especially in the detection of small items. A study shows that improved programming significantly improved the vision of shopping assistance robots. Recognition capabilities enable robots to provide more accurate and faster services in real shopping environments. © The Author(s) 2025.

关键词： machine vision

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FPGA-based image signal processing CEPST module design 2

FPGA-based image signal processing CEPST module design

引用

2nd International conference on Frontiers of Applied Optics and Computer Engineering, AOCE 2025

作者： Tsai, Lifen Chen, Chinta Yang, Chihchung Tang, Lijie Wang, Xiahui Xue, Wuyang Ma, Huan Zhaoqing University Guangdong China Zhaoqing University Guangdong China

ISBN: (纸本)9781510689404

CEPST algorithm is an important step in image processing, which is widely used in computer vision, pattern recognition, and machine learning. In order to improve the efficiency and performance of CEPST algorithm, more and more studies have implemented the task of CEPST algorithm on FPGA boards in recent years. FPGA boards offer parallel computing capabilities, low power consumption, and low latency, making them suitable for high-performance computing and real-time applications. In this paper, the vivado tool is used to implement the method of CEPST algorithm. vivado has a complete toolchain and debugging environment, supports a variety of hardware description languages, and provides a rich set of IP cores and acceleration libraries to facilitate FPGA design and development. This paper details the steps of CEPST algorithm using vivado, from algorithm design to simulation. The CEPST algorithm was designed to improve the accuracy and speed, and further improve the performance of the overall system. © The Authors.

关键词： image enhancement

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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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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Srinivasan, Srivanth Nischitha, P. Chavan, Akshita Mohana Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (纸本)9798331509828

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%. © 2025 IEEE.

关键词： Adversarial machine learning

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LoG-VMamba: Local-Global vision Mamba for Medical image Segmentation 17th

LoG-VMamba: Local-Global Vision Mamba for Medical Image Seg...

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17th Asian conference on Computer vision, ACCV 2024

作者： Dang, Trung DQ. Nguyen, Huy Hoang Tiulpin, Aleksei University of Oulu Oulu Finland

ISBN: (纸本)9789819609000

Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer vision tasks, including medical image segmentation (MIS). vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at https://***/Oulu-IMEDS/LoG-VMamba. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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