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

共 13626 条记录，以下是161-170 订阅

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Compact colorful compressive spectral imager based on deep learning reconstruction

Compact colorful compressive spectral imager based on deep l...

引用

2024 International conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Li, Jinshan Ma, Xu Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510680425

Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressive *** CCSI systems enhance the modulation freedom through the combination of colorful coding mask (CCM) and dispersive element, but this kind of system has complex structure that limits the miniaturization of ***, the reconstruction quality of CCSI systems can be further improved by using deep learning *** paper proposes a compact CCSI method based on deep learning reconstruction, which tries to reduce the volume of system by attaching the CCM to the *** combination of CCM and RGB detector enhances the modulation ***, a Transformer-based deep learning algorithm is used to obtain promising reconstruction results of the target spectral *** of both simulations and experiments demonstrate the effectiveness of the proposed compact CSSI method. © 2024 SPIE.

关键词： image compression

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MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA STORAGE 30

MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA ST...

引用

30th IEEE International conference on image processing (ICIP)

作者： Pic, Xavier Dimopoulou, Melpomeni San Antonio, Eva Gil Antonini, Marc Cote Azur Univ I3S Lab Nice France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9781728198354

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.

关键词： image compression DNA JPEG 2000 arithmetic coder

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Joint Dehazing and Low-light image Enhancement with Complete Atmospheric Model 16

Joint Dehazing and Low-light Image Enhancement with Complete...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Wu, Yong Xia, Kexiang Ge, Liyong Ye, Jiongkai Zhejiang Institute of Communications Hangzhou311112 China The School of Automation Hangzhou Dianzi University Hangzhou310000 China

ISBN: (纸本)9781510689251

Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model that simultaneously considers haze and low-light conditions. This model incorporates a global darkness factor as a disturbance variable alongside the atmospheric scattering model, enabling adaptive luminance balancing without the need to estimate the global atmospheric light or the global darkness factor. Building on the principle of object color rendition, This paper justifies the use of the dark channel prior for haze removal and extends the bright channel prior for low-light image enhancement. Furthermore, we explore the mapping relationship between normal and inverted images based on the complete atmospheric model. By solving for the transmission rates of the global atmospheric light and global darkness factor, we propose an image enhancement algorithm capable of concurrently processing haze and low-light images. The proposed algorithm effectively mitigates issues of contrast reduction and color distortion. Experimental results demonstrate the efficacy of our approach, showing that the visual outcomes of simultaneous haze removal and low-light enhancement surpass those achieved through single-task haze removal. © 2025 SPIE.

关键词： Demulsification

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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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Optimizing Breast Cancer Detection: Machine Learning for Pectoral Muscle Segmentation in Mammograms 2

Optimizing Breast Cancer Detection: Machine Learning for Pec...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Priyanka, S. Sivakumar, Shreya Selvam, Prabu VIT University School of Computer Science and Engg. Chennai India

ISBN: (纸本)9798350317558

For timely identification and treatment in the field of medical imaging, accurate breast cancer segmentation from mammograms is crucial. To improve the identification of breast cancer from mammography images, this paper propose an ensemble model by combining various various machine learning models. In the first step, a series of pre-processing steps such as image enhancement, normalization, noise removal and artifact removal techniques are applied to improve image quality. Then, a variety of machine learning algorithms, such as Support Vector Machines (SVM) with linear, polynomial, and Radial Basis Function (RBF) kernels, Random Forest, XGBoost, and K-Nearest Neighbors (KNN). Effectiveness of the proposed ensemble model in segmenting and identifying breast cancer is evaluated using MIAS benchmark dataset. The proposed model achieves an accuracy of 93.27%. The results show that ensemble model has a great deal of potential to improve mammography image segmentation and analysis, which could help radiologists diagnose breast cancer. The groundwork for further investigation into advanced modeling methods is laid by this study, which emphasizes the revolutionary power of machine learning in improving oncology outcomes for patients and diagnostic procedures. © 2024 IEEE.

关键词： image normalization machine learning mammographic image analysis medical image processing Pectoral muscle segmentation

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Smart equipment failure detection with machine learning applied to thermography inspection data in modern power systems 11

Smart equipment failure detection with machine learning appl...

引用

11th International conference on Smart Grid (IcSmartGrid)

作者： Maria Garzon, Ana Laiton, Natalia Sicacha, Victor Celeita, David F. Trung Dung Le Univ Rosario Sch Engn Sci & Technol Bogota Colombia Univ Paris Saclay Cent Supelec CNRS Grp Elect Engn Paris GeePs Gif Sur Yvette France

ISBN: (纸本)9798350310665

This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.

关键词： Thermography inspection image segmentation predictive fault identification machine learning neural network

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Automated Recognition of Yoga Mudras through image processing

Automated Recognition of Yoga Mudras through Image Processin...

引用

2024 International conference on Distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Holla, Deeksha Shambbavi, S. Harish, H. Spoorthi, K. Bai, T Varshita Maharani Lakshmi Ammanni College for Women Autonomous Bengaluru India

ISBN: (纸本)9798350375442

Yoga, an ancient practice, uses specific hand gestures called mudras, to reduce stress, enhance focus, and improve mental health. However, existing systems for Yoga Mudra Recognition have been limited in scope, recognizing only a small number of mudras and often lacking accuracy under diverse and challenging conditions. To address these shortcomings, this study introduces a novel approach that overcomes these limitations by employing advanced image processing techniques. By leveraging cutting-edge computer vision algorithms, the system is capable of accurately identifying 22 different yoga mudras, marking a significant expansion from previous models. The methodology involves capturing detailed hand landmarks, applying sophisticated preprocessing steps, extracting critical features such as finger positions, and utilizing SVM for precise classification. The refined dataset, combined with extensive experiments considering various hand shapes, lighting conditions, and backgrounds, resulted in an impressive accuracy of 99.87%. This gesture recognition technology enhances mindfulness and holistic wellness across various environments. © 2024 IEEE.

关键词： Gesture recognition

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Unravelling Visual Narratives with Deep Learning for image Caption Generation 1

Unravelling Visual Narratives with Deep Learning for Image C...

引用

1st IEEE International conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Yoga, M. Ramyasri, M.M. Bhavatharini, N. Harini, M. NivethaSri, A. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers a comprehensive overview of key concepts, methodologies, and challenges in ICG. The process involves developing algorithms for the automatic generation of contextually relevant captions, utilizing deep neural networks for feature extraction, and employing natural language processing techniques for coherent composition. Recent advancements, particularly in convolutional neural networks for image processing and recurrent neural networks for language modelling, have significantly elevated the performance of image captioning systems. The study delves into the core components of an ICG system, including pre-processing techniques for image data, feature extraction mechanisms, and the integration of language models. Attention mechanisms, a key innovation in this field, enable the model to focus on relevant image regions while generating captions, closely mirroring human attention patterns. Despite notable progress, ICG faces several challenges, such as handling diverse and complex visual scenes, ensuring cross-modal coherence between images and captions, and addressing biases present in training data. Ethical considerations, particularly in applications like automated content generation, are also discussed. The study concludes by highlighting potential future directions in ICG research, including the incorporation of multimodal learning approaches, enhancing the interpretability of generated captions, and addressing societal concerns related to bias and fairness. As ICG continues to evolve, it holds promise for various applications, ranging from accessibility for the visually impaired to improving content indexing and retrieval in multimedia databases. The research also underscores the significance of the accuracy attainments, showcasing the success of the pr

关键词： Feature extraction

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A Critical Analysis Toward Sustainable Farming: The Role of AI in Weed Identification and Management 5th

A Critical Analysis Toward Sustainable Farming: The Role of ...

引用

5th International conference on Communication and Intelligent systems (ICCIS)

作者： Singh, Shweta Yadav, Pritaj Rabindranath Tagore Univ Bhopal India

ISBN: (纸本)9789819720521;9789819720538

An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. The weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. These images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. This paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. Through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep Learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. The ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.

关键词： image processing image enhancement Weed detection Machine learning

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Hybrid Machine Learning Techniques for image Denoising Based on Wavelet Transform 6

Hybrid Machine Learning Techniques for Image Denoising Based...

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6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Yuan, Qikun University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350374315

Wavelet theory has been widely applied in image processing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. This method significantly enhanced image clarity without increasing computational demand. This article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024. © 2024 IEEE.

关键词： Adversarial machine learning

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