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共 8543 条记录，以下是1-10 订阅

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ICAIP 2021 - 2021 5th international conference on advances in image processing

ICAIP 2021 - 2021 5th International Conference on Advances i...

引用

5th international conference on advances in image processing, ICAIP 2021

ISBN: (纸本)9781450385183

the proceedings contain 18 papers. the topics discussed include: scene classification using regional and nearest neighbors of local features;monocular multi-pose pedestrian ranging algorithm based on key point detection;attention augmented convolutional neural network for fine-grained plant disease classification and visualization using stochastic sample transformations;irregular ellipse detection method by confined space sweep;unsupervised learning for stereo depth estimation using efficient correspondence matching;characterization test for multivariate skew-t distributions;blind image restoration with defocus blur by estimating point spread function in frequency domain;and study on automatic shoreline extraction based on multi-spectral remote sensing images.

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A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. the rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural Networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning image processing Backpropagation algorithm Convolutional Neural Networks (CNNs) Model structures

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Leaf Disease Detection and Correction 5th

Leaf Disease Detection and Correction

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Indrani, P. Regulwar, Ganesh B. Ahmed, Md Sohel Reddy, K. Sneha Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Pests and plant conditions provide an essential part when assessing the yield as well as the quality of plants. Nowadays plant disease identification is carried out by the deep learning and image processing. Agriculture is the backbone of Indian government. We need to increase the yield in the crop as the population is increasing so the urbanization increases which cause in decrease of cultivation land. Because of the diseases that we observe frequently in plants, the crop production is being decreased. To reduce the disease in plant we need to use some pesticides which also helps to increase in production. In the recent times, deep learning is very useful in the field of image processing. How to exercise deep learning technology to study factory conditions and pests identification has come a exploration conclusion of great company to experimenters. Now we use image processing and find out the disease in plant and also provide the required pesticide suggestions which makes detection of solution for a disease easier and helps in good yield of crop for farmers.

关键词： Leaf disease detection Plant disease detection deep learning image processing convolutional neural networks TensorFlow Keras agricultural technology crop yield pesticide recommendations preprocessing model construction training testing performance evaluation

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer Vision Natural Language processing Attention Mechanism Performance

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2025 5th international conference on Advanced Research in Computing: Converging Horizons: Uniting Disciplines in Computing Research through AI Innovation, ICARC 2025 - proceedings

2025 5th International Conference on Advanced Research in Co...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

ISBN: (纸本)9798331530983

the proceedings contain 155 papers. the topics discussed include: enhancing systematic literature reviews: evaluating the performance of LLM-based tools across key systematic literature review stages;optical observation for body angles of cricket players’ motion to identify the batting shots;enhanced approaches to kingfisher species identification using machine learning and deep learning;BAHAGAP: design and development of an image processing-based detection and monitoring system for early flood warnings in high-risk zones;model optimization for personalized health metrics analysis;a secure protocol for computer-based assessments in disrupted environments;machine learning based enhanced remote power analysis attacks;and Indian sign language alphanumeric hand gestures recognition using deep learning techniques.

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Deep Learning Model Development for an Automatic Healthcare Edge Computing Application 5th

Deep Learning Model Development for an Automatic Healthcare ...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Talal, Hadi Khamis, Ruaa Ali Al-Frady, Laith Middle Tech Univ Baghdad Iraq

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning (DL) is assisting academicians and medical professionals in uncovering latent opportunities in data and enhancing the healthcare industry. the edge computing applications like smart healthcare systems where accurate decision-making is required for fast medical treatment. DL in healthcare allows clinicians to correctly analyze any ailment and treat it, resulting in improved medical decisions. We present a unique DL model for the autonomous healthcare edge computing application in this paper. Computer Aided Diagnosis (CAD) is an essential requirement of healthcare edge computing where the patient's medical data is used for fast and accurate disease prediction. Propose the DL-based CAD model for automatic disease classification from the input medical images. the model consists of pre-processing, DL-based feature engineering, and classification. Input medical image is first pre-processed for quality improvement and then automatic features are extracted using the pre-trained DL models (ResNet50 and Densenet201). the pre-trained models are improved by performing the feature scaling followed by a separate classification phase. the proposed CAD model is experimentally evaluated using the medical images dataset. the results reveal the efficiency of the proposed model compared to underlying solutions.

关键词： Automatic classification deep learning edge computing features engineering healthcare image processing

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Research on the Application of Deep Learning-Based image Recognition Algorithm in Signal processing 5

Research on the Application of Deep Learning-Based Image Rec...

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Lv, Jing Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (纸本)9798331533694

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learning the signal image. then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data. © 2025 IEEE.

关键词： image denoising

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proceedings of the 2020 4th international conference on advances in image processing, ICAIP 2020

Proceedings of the 2020 4th International Conference on Adva...

引用

4th international conference on advances in image processing, ICAIP 2020

ISBN: (纸本)9781450388368

the proceedings contain 31 papers. the topics discussed include: another parallelism technique of GLCM implementation using CUDA programming;3D object detection based on feature pyramid network;a novel infrared small target detection algorithm based on deep learning;context-based feature fusion network for object detection;fully convolutional one-stage circular object detector on medical images;object detection with auto-learning anchor algorithm;horse breed classification based on transfer learning;room classification in floor plan recognition;multi-dimensional spatial attention residual U-Net (Msaru-Net) for low-dose lung CT image restoration;and feature based deep retinex for low-light image enhancement.

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Hand Gesture Recognition for Smart Television Using GRU 5th

Hand Gesture Recognition for Smart Television Using GRU

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Suresh, Garugu Yaswanth Lakshmi Sravani, Ravinuthala Gayatri Venkata VIT AP Univ Sch Comp Sci & Engn Inavolu AP Secretariat Amaravati Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Even with the rapid advancement of technology, using a television still requires a physical remote control. Apart from occasionally losing sight of the television remote control, we also sometimes run out of batteries. the goal is to discover an effective way of controlling television and develop 3D hand gesture-based smart television control using Gated Recurrent Unit (GRU). the results of the research show that hand gesture recognition-based interface technology is capable of performing the majority of smart TV operations. It is a comfortable and delightful experience for consumers. the existing research features static image gesture recognition with predefined models for training in addition to the existing research the proposed model features sample video dataset, Custom design with Gated Recurrent Unit. the suggested model is trained using five hand gestures. the camera positioned on the TV continually records the motions. Each gesture is associated with a certain command. the proposed model has achieved an accuracy of about 94% in recognizing the gestures.

关键词： Smart Television image processing Deep Learning Convolutional Neural Networks Gated Recurrent Unit (GRU)

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5th international Workshop on advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

5th International Workshop on Advances in Simplifying Medica...

引用

5th international Workshop on advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031736469

the proceedings contain 21 papers. the special focus in this conference is on advances in Simplifying Medical Ultrasound. the topics include: Do High-Performance image-to-image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer;PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement;PIPsUS: Self-supervised Point Tracking in Ultrasound;structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train;an Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy;Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound;automatic Facial Axes Standardization of 3D Fetal Ultrasound images;C-TRUS: A Novel Dataset and Initial Benchmark for Colon Wall Segmentation in Transabdominal Ultrasound;label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets with Domain Shift and Partial Labelling;introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography;interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images;Enhanced Uncertainty Estimation in Ultrasound image Segmentation with MSU-Net;multi-site Class-Incremental Learning with Weighted Experts in Echocardiography;Masked Autoencoders for Medical Ultrasound Videos Using ROI-Aware Masking;uncertainty-Based Multi-modal Learning for Myocardial Infarction Diagnosis Using Echocardiography and Electrocardiograms;fetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery;LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound images;unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models;diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.

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