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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1803 条记录，以下是181-190 订阅

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Detection of Corn Leaf Infection Using CNN with Various Optimizers 2

Detection of Corn Leaf Infection Using CNN with Various Opti...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sreekanth, G.R. Latha, R.S. Roja, R. Sankarnanth, S. Gayathri, A. Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350349221

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning: The Driving Force Behind Intelligent Systems and Predictive Analytics 2

Machine Learning: The Driving Force Behind Intelligent Syste...

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2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Virmani, Deepali Atheeq Sultan Ghori, Md. Tyagi, Nishant Ambilwade, R.P. Rajesh Patil, Priyanka Sharma, M.K. Vivekananda Institute of Professional Studies-Technical Campus School of Engineering and Technology India Telangana University Nizamabad Department of Computer Science and Engineering Telangana State India Management G.L. Bajaj Institute of Management and Research Greater Noida India Computer Science National Defence Academy Pune India Prof Ram Meghe College of Engineering and Management Department of Electronics and Telecommunication Engineering Badnera India Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

ISBN: (纸本)9798350384277

This technical analysis explores the profound impact of machine learning on intelligent systems and predictive analytics. It examines algorithmic fundamentals and explores models such as linear regression, support vector machines and neural networks, which are the pillars of supervised learning. The research explores practical applications in natural language processing, where advanced neural network architectures such as Transformers redefine, language understanding and computer vision using convolutional neural networks for image recognition and object recognition. In addition, the analysis covers the basic steps of data processing, including data cleaning, transformation and refinement, as well as feature design and model optimization. In addition, the research addresses the challenges of machine learning, emphasizing the importance of reducing bias and ethical considerations. The study concludes with a look into the future of machine learning, envisioning advances in quantum computing to solve complex problems, combinatorial learning for distributed data problems, and synthetic data generation as a solution to data protection problems. This comprehensive study provides an overview of the current state of machine learning, challenges and promising developments in intelligent system innovation and predictive analytics. © 2024 IEEE.

关键词： Federated learning

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CT and MRI fusion diagnosis technology based on recurrent neural network 2

CT and MRI fusion diagnosis technology based on recurrent ne...

引用

2nd international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2023

作者： Liu, Bo Ma, Zhanyao Department of Magnetic Rsonance Imaging Henan Province Hospital of Traditional Chinese Medicine The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine Henan Zhengzhou450002 China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510674684

With the continuous progress of digital medicine and smart medical technology, the application of computer vision technology in medical image processing is also constantly developing and advancing. image fusion technology in computer vision technology can complement the advantages of medical imaging through effective algorithms, discover useful and valuable information in medical diagnosis, and it is very effective in compensating for the shortcomings of medical image presentation technology and the lack of image information. The fusion of CT medical images and MRI medical images using computer vision technology can combine the advantages of CT images displaying clear bone information and high image resolution with the advantages of MRI images displaying clear soft structures, forming complementary advantages, thus making the display information of medical images more abundant. This article mainly utilizes wavelet transform algorithm to fuse CT and MRI anatomical imaging and proposes a medical image classification and diagnosis method based on local spatial sequence Recurrent Neural Network (RNN), extracting higher-level semantic features of images with better representativeness. This method extracts the local spatial sequence features of the samples and utilizes a temporal RNN to highly integrate and abstract the local spatial information. While obtaining high-level semantic features, it enhances the role of useful pixels in the local space and suppresses the influence of useless pixels. The experimental results show that the proposed method improves classification accuracy. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image fusion

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Myocardial segmentation of 2D echocardiography based on the improved self-attention mechanism 2

Myocardial segmentation of 2D echocardiography based on the ...

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2nd international conference on Machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Li, Delong Sun, Yixin Han, Xue Dong, Suyu College of Computer and Control Engineering Northeast Forestry University Harbin China The Department of Ultrasound Harbin Medical University Cancer Hospital Harbin China

ISBN: (纸本)9798331543037

The measurement of different clinical indicators, such as the myocardial wall's thickness and the ventricle's volume, depends on accurately segmenting the left ventricle in 2D echocardiography. This segmentation is essential for making various clinical decisions, such as whether surgery is necessary. However, segmenting the myocardium requires clinical expertise and makes accurate segmenting time-consuming due to the poor quality of echocardiograms. It is essential to develop an automatic myocardial segmentation method. Some deep learning methods, especially the U-net structure, were proposed to support the automatic myocardium segmentation in echocardiography. The model must simultaneously capture the local and global dependency relationships in the ultrasound images for myocardial segmentation in echocardiography. Given the inherent attribute of local operation of convolution operations, this poses significant limitations for U-Net based on the traditional CNN as the basic architecture. For example, when dealing with complex myocardial textures and structures, we need to accurately identify the myocardium's global morphological features and overall structural patterns. Moreover, this locality may lead to deviations and errors when segmenting the boundary regions or the myocardial parts closely related to the surrounding tissues. Our primary focus will be redefining the skip connection using the attention mechanism. More integrated attention mechanisms can further enhance the segmentation effect of myocardial in echocardiography. Therefore, we integrated the cross-attention mechanism when concatenating features in the skip connection. Through conducting rigorous ablation studies on multiple echocardiography segmentation datasets, the performance of the proposed mechanism was proven. ©2024 IEEE.

关键词： Echocardiography

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A Review of the Evolution and Applications of Convolutional Neural Network (CNN) 2

A Review of the Evolution and Applications of Convolutional ...

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2nd international conference on Edge Computing and Applications, ICECAA 2023

作者： Joshi, Sandeep Manu, Mehul Mittal, Amit Graphic Era Hill University Department of Computer Science and Engineering Bhimtal India Graphic Era Hill University Department of Allied Sciences Bhimtal India

ISBN: (纸本)9798350347579

Within this literary document, a panoramic insight is presented on the progression and practical uses of Convolutional Neural Networks (CNNs), an influential technique in deep learning that serves as a major element within computer vision research alongside other areas. Through a comprehensive analysis of the literature, this research study presents thehistorical development of CNNs from early work on perceptrons to current state-of-the-art architectures like VGGNet, ResNet, and EfficientNet. The review highlights the key contributions of CNNs in various fields, such as image and video recognition, natural language processing, and audio analysis. Furthermore, it discusses the potential for further research and development of CNNs, including the challenges in training and optimizing CNNs and the future directions of CNNs. Overall, this review underscores the importance of CNNs in enabling breakthroughsin diverse fields and their potential for continued impact on the scientific community. © 2023 IEEE.

关键词： Convolutional neural networks

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Feature-Attentive Multimodal Emotion Analyzer with CLIP 2

Feature-Attentive Multimodal Emotion Analyzer with CLIP

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Nie, Zichao Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China

ISBN: (纸本)9798350331417

Sentiment analysis has evolved into a multidimensional field with the advent of various data modalities, such as text and images. Traditional methods often struggle to effectively integrate these diverse data types. This paper introduces a model based on Contrastive Language-image Pre-Training (CLIP), named the Feature-Attentive Multimodal Emotion Analyzer with CLIP (FAMEAC), which is a novel framework designed to address the challenges of multimodal sentiment analysis. By utilizing state-of-the-art algorithms such as vision Transformer (ViT), CLIP, and robustly optimized bidirectional encoder representations from Transformers approach (RoBERTa), the FAMEAC model excelled in feature extraction from both text and image modalities. An attention-based layer was incorporated for feature fusion, enabling the model to capture intricate relationships between different modalities. Experimental results derived from Twitter datasets reveal that the proposed model outperforms existing methodologies in both accuracy and robustness. The FAMEAC model holds promise for various applications, ranging from commercial advertising to social science research, while also raising important ethical considerations. This research serves as a seminal contribution to the burgeoning field of multimodal sentiment analysis, establishing a novel paradigm that holds significant promise for guiding future scholarly investigations. © 2023 IEEE.

关键词： deep learning multi-modalities sentiment analysis

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An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 IEEE.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

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One Shot Face image Style Transfer Method Based on GAN 2

One Shot Face Image Style Transfer Method Based on GAN

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： He, Wenyin Wuhan University Department of Computer Science Hubei Wuhan430072 China

ISBN: (纸本)9798350331417

Style transformation on face images has traditionally been a popular research area in the field of computer vision, and its applications are quite extensive. Currently, the more mainstream schemes include Generative Adversarial Network (GAN)-based image generation as well as style transformation and Stable diffusion method. In 2019, the NVIDIA team proposed StyleGAN, which is a relatively mature scheme for generating real faces as well as face feature blending. The whole StyleGAN model is trained based on the Flickr-Faces-HQ Dataset (FFHQ) dataset, the This is a large dataset, so the model takes a long time to train. My aim is to form a One-shot stylized face image generator, which means that only one reference face and one stylized face need to be input, and a brand-new face with a mixture of features can be generated in a short training time. This is inspired by the existing research result JoJoGAN, which learns a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial paired dataset from a single example of the style. This paper will make improvements to JoJoGAN, including improving the encoder that utilizes the GAN Inversion method to generate latent codes for image features, and the random mixing of latent codes to produce a more refined paired dataset. © 2023 IEEE.

关键词： deep learning GAN style transformation

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Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis 2

Detecting the Foreground Dynamic Scenes using Gaussian Mixtu...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Baswaraju, Swathi New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350325799

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing, presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods. © 2023 IEEE.

关键词： Principal component analysis

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Precision Agriculture Advancements: A Comprehensive Integrated System for Disease Prediction and Crop Yield Estimation Using image Analysis and Environmental Data 2

Precision Agriculture Advancements: A Comprehensive Integrat...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Nithiya, A. Navina, N. Thoshitha, D. Suvetha, R. Thirilosana, J. M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur639113 India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The primary problem facing agriculture, which is essential to ensuring the world's food security, is maximizing crop productivity while reducing the effects of plant diseases. Advanced technologies have the potential to completely transform agricultural methods, particularly in the parts of computer vision and machine learning. This study uses meteorological datasets and fruit image analysis to create an integrated agricultural decision support system for crop yield estimation and disease prediction. By offering early plant disease detection and precise crop yield estimates, the system seeks to improve precision agriculture techniques. A variety of datasets with plant photos labelled with disease information are gathered for the study, and meteorological data is integrated to capture environmental variables. The technology includes advanced image processing techniques to extract relevant features from plant pictures. The suggested method analyses images using a convolutional neural network technique to forecast the disease in impacted fruits. Make recommendations for natural fertilizers based on the ailment being suffered. The Multilayer Perceptron algorithm is used to train the model using a large dataset that contains historical meteorological data, allowing it to identify patterns and connectionsbetween environmental conditions. Lastly, farmers receive an SMS notice with prediction specifics. © 2024 IEEE.

关键词： Fruits

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