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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13605 条记录，以下是81-90 订阅

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A Novel Artistic image Compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

引用

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

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance.

关键词： image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain Compression algorithms Frequency-domain analysis Collaborative intelligence

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processing and Classification algorithms of Medical images

Processing and Classification Algorithms of Medical Images

引用

International image processing, Applications and systems conference (IPAS)

作者： Oltiana Seitaj Department of Industrial Engineering Electronics Mechanics University of Roma Tre Rome Italy

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional Neural Networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation.

关键词： Deep learning image segmentation Adaptation models Accuracy Thresholding (Imaging) Streaming media Reliability MATLAB Biomedical imaging Tumors

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A Study on multi-scale landscape character extraction combining GIS and graphic processing algorithms 3

A Study on multi-scale landscape character extraction combin...

引用

3rd International conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Wang, Shuwei Liu, Yang Li, Guangqi Su, Xueying Wang, Sen Bai, Xuyong Center for Geophysical Survey China Geological Survey Langfang China Technology Innovation Center for Earth Near Surface Detection China Geological Survey China

ISBN: (纸本)9781510690219

This research presents an innovative approach to accurately extract multi-scale landscape characters by combining geographic information systems (GIS) and graphic processing algorithms. It is essential to recognize and analyze landscape characters at different scales. This study uses GIS advantages in spatial analysis and the K-Means clustering algorithm in image enhancement, feature extraction, image segmentation, and color quantification. Through image interpretation and deep learning, the distribution characteristics of buildings in Langfang City are obtained to provide guidance and suggestions for the future construction and development of the city. The proposed method covers data preparation, algorithm design, and system implementation. In addition, the future research direction of improving the method is also discussed, and the possibility of its application in other fields is explored. © 2025 SPIE.

关键词： deep learning K-Means multi-scale landscape analysis spatial analysis. GIS

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OCTA-Based Biomarker Characterization in nAMD

OCTA-Based Biomarker Characterization in nAMD

引用

International image processing, Applications and systems conference (IPAS)

作者： Maria Simona Tivadar Ioana Damian Adrian Petru Groza Simona Delia Nicoara Department of Computer Science Technical University of Cluj-Napoca Romania European University of Technology European Union Department of Ophthalmology "Iuliu Hatieganu" University of Medicine and Pharmacy Emergency County Hospital Cluj-Napoca Romania

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

We aim to enhance ophthalmologists’ decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.

关键词： Three-dimensional displays Accuracy Optical coherence tomography image processing Angiography Decision making Medical services Machine learning Biomarkers Glass box

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Sclera Segmentation on the Half-UNet Neural Network

Sclera Segmentation on the Half-UNet Neural Network

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Sergei Chernyadyev Natalya Kharina Department of the Radio Electronic Means Vyatka State University Kirov Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Nowadays, technology for biometric authentication technologies is actively implemented, users can usually authenticate themselves by their face, voice, fingerprint, vascular bed of the finger, palm, iris. Using images of sclera vessels for authentication is mathematically and technically more complex, so it is used less often. In addition, existing sclera segmentation algorithms often do not allow ensuring the required quality of authentication systems. The paper proposes an algorithm for sclera segmentation based on the lightweight convolutional neural network Half-UNet, and compares the obtained results with known algorithms for sclera segmentation. Analysis of the obtained results shows that the developed algorithm effectively highlights the eye sclera region, surpassing known modern algorithms, and can be used in implementation of biometric authentication system as a stage of processing scanner images before the sclera vessels segmentation stage.

关键词： Performance evaluation image segmentation Neural networks Memory management Signal processing algorithms Authentication Biometric authentication Mobile handsets System implementation Iris recognition

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Productivity Estimation Based on Optical Remote Sensing image Spatiotemporal Fusion Algorithm 6th

Productivity Estimation Based on Optical Remote Sensing Imag...

引用

6th International conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Chu, Jingyi UC Santa Barbara Santa BarbaraCA93117 United States

ISBN: (纸本)9783031882869

With the rapid development of remote sensing technology, the application of optical remote sensing images in productivity estimation is becoming increasingly widespread. However, due to the limited temporal and spatial resolution of remote sensing images, accurately estimating productivity still faces challenges. This study improves the spatiotemporal resolution of optical remote sensing images through spatiotemporal fusion algorithms to achieve more accurate productivity estimation. We use advanced spatiotemporal fusion technology to combine high temporal resolution but low spatial resolution images with high spatial resolution but low temporal resolution images to generate composite images with high spatiotemporal resolution. These composite images can more accurately capture the dynamic changes in surface productivity. Even at the longest time scale of 3 months, the spectral fidelity remains at a high level of 0.87, indicating that the model still has good spectral fidelity performance when processing remote sensing images with long time intervals. This study not only improves the accuracy of productivity estimation, but also provides new data support for resource management and decision-making in industries such as agriculture and forestry, which has important theoretical and practical significance. In addition, our method can also provide reference for other fields and promote the widespread application and development of remote sensing technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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Integration of Digital Technologies and Artificial Intelligence in the Ecomonitoring System of the Black Sea Coast

Integration of Digital Technologies and Artificial Intellige...

引用

International conference on Applied Innovations in IT, ICAIIT 2024

作者： Surguladze, Gia Topuria, Nino Mirtskhulava, Lela Petriashvili, Lily Georgian Technical University Kostava Str. 77 Tbilisi0160 Georgia Iv. Javakhishvili Tbilisi State University/ San Diego State University Ilia Tchavtchavadze Avenue 1 Tbilisi0179 Georgia

ISBN: (纸本)9783031892950

The existence of the Black Sea water area of Georgia is an important factor in its geographical location, which determines the country’s political, social, and economic potential, especially in terms of foreign relations, marine industry, and tourism development. It has a great influence on the formation of the country’s climate, a scientific study of the diversity of the Black Sea and the coastal region, which from a multidisciplinary point of view will study the condition of hydro resources (rivers, lakes, etc.), existing seaports and oil terminals in this region, probable risks and their impact on the quality of the Black Sea water, especially in the background of climate change, it is very important and relevant for the blue development of the Black Sea. Within the framework of the project, the tasks of creating the concept of the information system supporting eco-monitoring of the mentioned region, automated design, and software implementation were addressed. This was based on the system analysis of the region’s hydro sources and sensitive areas, along with new digital technologies. Object- and process-oriented models of their research were created, with automated initial data collection devices (IoT sensors) and processing methods based on appropriate machine learning algorithms and programs. In addition, a service-oriented web portal creation methodology was developed. The portal served experts of the Black Sea eco-monitoring system and managers working on artificial intelligence in the decision-making process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marine industry

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

引用

International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making Signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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A Comparative Review on Design and Development of Automation System for Grading of Onion

A Comparative Review on Design and Development of Automation...

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Rahul Suryawanshi Jayesh Jagtap Dnyaneshwari Shegokar Siddhesh Jaju Chandan M. N Avinash M. Badadhe Department of Automation and Robotics JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researchers because of the rising need for quality produce. Thus, the findings highlighted in this work show the progress and current approaches and methodologies designed for the design and implementation of automation systems for grading onions. This work outlines various methods such as image processing, gas sensor technology, Near Infra-Red (NIR) spectroscopy, X-ray imaging, Laser Doppler Vibrometry (LDV), and some artificial intelligence approaches that have been used in improving onion sorting systems. In addition, it reveals the drawbacks of these technologies, such as the realization of high image processing, the availability of small data sets, and the stability of the sortation systems. Based on the findings of this review, some recommendations for future research are suggested, such as the integration of multi-sensor systems and the enhancement of the efficiency of the automated onion grading systems using different types of algorithms.

关键词： Automation Reviews image processing X-ray lasers Laser stability Doppler effect Gas lasers Artificial intelligence X-ray imaging Sorting

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