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

共 16443 条记录，以下是1001-1010 订阅

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Deep Reinforcement Learning-Based Recommender Algorithm Optimization and Intelligent systems Construction for Business Data Analysis

Deep Reinforcement Learning-Based Recommender Algorithm Opti...

引用

2022 IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： xingyu, Fan Yang, Lin Xidian University Xi'an China Marquette University MilwaukeeWI United States

ISBN: (纸本)9781665409025

Business analysis plays an essential role in numerous companies and industries, which involves data collection, data analysis, and the construction of intelligent systems to make critical business decisions. By using a data model to filter through consumers' data, it is easier than ever to make recommendations to consumers for what they would enjoy using or purchasing. Added a real-time product Recommendation System framework, the Reinforcement Learning model is used to automatically learn the optimal recommendation strategy. Also, the Deep Q-Network (DQN) algorithm is modified by replacing the convolutional neural network with the Long Short-Term Memory (LSTM). The solution analyzes and predicts consumers' preferences in real-time, and makes up for the lack of iterative optimization of traditional Recommendation systems in real scenarios. © 2022 IEEE.

关键词： Long short-term memory

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The image Segmentation of Osmanthus fragrans Based on Optimization algorithms 4

The Image Segmentation of Osmanthus fragrans Based on Optimi...

引用

4th International conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： xiao, Han Cheng, Yuzhu Suzhou University School of Electronic and Information Engineering Suzhou China Nanjing Forestry University College of Mechanical and Electrical Engineering Nanjing China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

image segmentation is of great importance in many areas including artificial intelligence, image processing, computer vision, etc. The optimization of image segmentation can be converted to seek the best threshold values to divide an image into specific objects and background. In this study, we have used the newly proposed AO, G-NHGWO, GBO, LPB and LA optimization algorithms to identify the optimal threshold values for image segmentation of Osmanthus fragrans flowers. The widely used Kapur, Shanbhag, weighted Otsu, Kittler, Yen and Li methods were utilized as objective functions for thresholding selection. The image segmentation results have exhibited the ability and superiority of employed optimization algorithms and thresholding strategies. The research of efficient image segmentation of Osmanthus fragrans in this study is helpful to the machine recognition and classification of the widely cultivated plants, which will improve the automatic flower picking and industrialization of Osmanthus fragrans. © 2022 IEEE.

关键词： image segmentation

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An Improved Gaussian-Mapping Algorithm for Edge Detection Based on FPGA

An Improved Gaussian-Mapping Algorithm for Edge Detection Ba...

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International conference on systems and Informatics (ICSAI)

作者： Dihong xie Jin Wu Bo Zhao Wenting Pang Dept. School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an China

As a method of image processing, edge detection is widely used in object detection, image recognition, machine vision and other fields. At present, the research of edge detection algorithm is still a hot problem in the field of image processing. At the same time, in order to solve the problem that the edge detection operator is difficult to identify due to noise interference and low edge contrast in the image, in this paper, a contrast enhancement processing algorithm, based on Gaussian filter, is used to preprocess the image. In this paper, an FPGA-based grayscale conversion algorithm is used to convert the original image into a grayscale image, and then an improved algorithm based on Gaussian filtering is used to successfully enhance the slope of the edge in the original image. In order to process the edge detection of the image faster, use FPGA to realize the algorithm of edge detection of the image, and the result is output and displayed through VGA display. Finally, by comparing the experimental results of Roberts, Prewitt and Sobel operators, the advantages and disadvantages of several different algorithms are obtained. After enhancing the slope, using the edge detection algorithm, it is found that the three edge detection operators after enhancing have better detection results without wasting extra time.

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Improving The Hiding Capacity of image Steganography with Stego-Analysis

Improving The Hiding Capacity of Image Steganography with St...

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2023 IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2023

作者： Grandhe, Padmaja Mallikarjuna Reddy, A. Chillapalli, Kavyasri Koppera, Kavya Thambabathula, Manasa Reddy Surasani, L.P. Pscmrcet Cse Department Vijayawada India Anurag University Department of Cse Hyderabad India

ISBN: (纸本)9798350398465

Communicating online without fearing third-party interventions is becoming a challenge in the modern world. Especially the sectors like the military, and government organizations or private companies sharing sensitive information. They invest a lot of effort and cost into obtaining the advancement of safe communication techniques. image processing encryption techniques using various algorithms promote security over communication channels and using different analysis methods make the tool stand out in providing security to the information. In today's world, there are various steganographic mechanisms that convert the secret message into stego medium and send it across various communication channels. Using algorithms like Blind Hide promotes the security of the message along with using multiple analysis methods that will further improve the tool in giving out information of encoded accuracy, size of stego of the secret message. The aim is to generate a tool that will give out a benchmark value of how precisely the message is stored in the cover file. Using Stego and bulk analysis the information about the presence of the stego medium in the message can be known to the user. All these analysis methods make the tool more enhanced and secure. © 2023 IEEE.

关键词： Steganography

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BCD-Unet:A Novel Water Areas Segmentation Structure for Remote Sensing image 7

BCD-Unet:A Novel Water Areas Segmentation Structure for Remo...

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7th International conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Jonnala, Naga Surekha Gupta, Neha Vasantrao, Chafle Pratiksha Mishra, Anoop Kumar VIT-AP University School of Electronics Engineering Amaravati India

ISBN: (纸本)9798350397253

Surface water resource identification is one of the main techniques used in remote sensing image analysis. This is necessary to stop calamities like floods and droughts. Feature selection based on prior information and complex spectrum analysis are required by machine learning algorithms for extracting waterbodies. The use of deep learning-based algorithms, which have just lately been considered, has eliminated the need for manually extracting features, but they require a large amount of training data and processing power to achieve outstanding performance. Because of this, each deep learning architecture that has been demonstrated may recognize some of the patterns that are present in the given situations. To develop a reliable model for identifying surface water, this research trains and optimises deep learning architecture that have been presented in different disciplines using surface water data. This is accomplished by the model's output more accurately by extracting the finalized segmented mask of water bodies. Our suggested approach was tested using a Kaggle dataset. This dataset included Sentine1-2 satellite images of numerous water bodies. The suggested method outperforms current best practises in semantic segmentation performance. © 2023 IEEE.

关键词： Surface waters

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A fatigue driving detection algorithm based YOLOv5 14

A fatigue driving detection algorithm based YOLOv5

引用

14th International conference on Graphics and image processing, ICGIP 2022

作者： Li, Zhanli Jia, Ni Jin, Hongmei College of Computer Science and Technology Xi'an University of Science and Technology China

ISBN: (纸本)9781510666313

Aiming at the problems of slow detection speed and low detection accuracy in existing fatigue driving detection algorithms, a fatigue driving detection algorithm based on YOLOv5 is proposed. In order to improve the feature extraction ability of the network, the convolution module is used to replace the slice structure in Backbone;the algorithm combines the data features of fatigue driving images to simplify and optimize the Neck structure of the YOLOv5 model, which will be suitable for detecting 19×19 features of larger size objects. Graph branch pruning to reduce model complexity and improve real-time detection. Finally, on the basis of facial feature extraction, the algorithm determines the state of fatigue features according to PERCLOS and POM parameters combined with thresholds and outputs the results. The experimental results on the NHTU-DDD data set show that the accuracy of the detection model reaches 95.25%, the model size is only 10MB, and the single-frame detection speed is 9ms, which is 21.5% higher than the original YOLOv5 algorithm. At the same time, the model parameters are greatly reduced, which can better meet the real-time requirements of fatigue detection application scenarios. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Feature Interaction Prototypical Network for Few-Shot Fine-Grained image Classification 4

Feature Interaction Prototypical Network for Few-Shot Fine-G...

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4th International conference on Natural Language processing, ICNLP 2022

作者： Bai, Yilun Liu, Ying Zhang, Weidong Rashid, Sheikh Faisal Xi'an University of Posts and Telecommunications Center for Image and Information Processing Xi'an China Pakistan

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In recent years, although the task of fine-grained image classification has achieved remarkable results, these algorithms need to be trained on large datasets in order to obtain good results, otherwise it is easy to cause model overfitting and reduce the effect. However, fine-grained image datasets need to distinguish different subcategories under the same category. It is difficult to collect and label fine-grained images in practical application, so it is not easy to obtain a large number of good training data. Few-shot learning provides a solution for this. However, the traditional few-shot learning algorithm has limited feature extraction ability, and it is difficult to effectively classify fine-grained images with large intra class differences and small inter class differences. To this end, this paper utilizes the idea of model fine-tuning to extract features using a pre-trained model. In addition, the SimAM non-parametric attention mechanism is introduced to enhance features and filter backgrounds while reducing model parameters. Secondly, feature maps of different convolutional layers are fused using hierarchical bilinear pooling (HBP), thus effectively utilizing information from different convolutional layers to further enhance feature representation. Finally, the classification is completed by calculating the distance between the embedding vectors of the test samples and the well-distributed prototypes generated using the fused features. Experiments show that this algorithm performs well on few-shot fine-grained image classification tasks and achieves excellent performance on traditional few-shot image classification. © 2022 IEEE.

关键词： image classification

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Robust Non-convex Model-Based Approach for Deep Learning-Based image processing 4th

Robust Non-convex Model-Based Approach for Deep Learning-Ba...

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4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Morotti, Elena Evangelista, Davide Loli Piccolomini, Elena Department of Political and Social Sciences University of Bologna Bologna Italy Department of Mathematics University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9783031812408

Total p-norm Variation (TpV) is a well-established technique in image processing, used to denoise and preserve edges. However, the related non-convex minimization is still a challenging task in optimization, both for the computational cost and for convergence guarantees toward a good local minimum. We propose a framework, called (TpV)2, embedding a convolutional neural network to speed up the iterative reconstruction while preserving converging features. The resulting hybrid method is robust and accurate. Verifications and comparisons illustrate that the proposed method is effective and promising. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse Problems Neural Network Non-convex optimization

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Increasing the accuracy of object boundaries detection and the quality of machine vision system data in terms of machine processing by a collaborative complex 32

Increasing the accuracy of object boundaries detection and t...

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conference on Signal processing, Sensor/Information Fusion, and Target Recognition XXxiI

作者： Semenishchev, Evgenii Shraifel, Igor Makov, Sergey Surkov, Egor Zelensky, Aleksandr Voronin, Viacheslav Tula State Univ TulSU 92 Sq Lenina Tula 300012 Tula Russia Don State Tech Univ DSTU Inst Serv & Entrepreneurship 147 St Shevchenko Shakhty 346500 Rostov Russia Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

To article proposes an approach to improve the quality of data used in various processes based on machine vision systems. The paper proposes a combined approach to applying the method of multi-criteria processing based on the use of a combined criterion in order to implement an edge detector, smoothing and separation areas of the background / object in the image. The application of the method allows eliminating the noise caused by external factors (such as dust and water suspension on the lens or space). The generated data make it possible to form an adaptive criterion for changing the correction parameters for a non-linear change in color balance in areas of increased detail or selected masks of changes blocks. The proposed algorithms make it possible to increase the visibility of small elements, reduce the noise component, while maintaining the boundaries of objects, increase the accuracy of selecting the boundaries of objects and the visual quality of data. As test data used to evaluate the effectiveness, nature data and expert evaluation results for test images obtained by a machine vision system with a sensor with a resolution of 1024x768 (8-bit, color image, visible range) are used. images of simple shapes are used as analyzed objects.

关键词： boundaries detection image machine vision image smoothing

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CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders 37

CROMA: Remote Sensing Representations with Contrastive Radar...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Fuller, Anthony Millard, Koreen Green, James R. Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Carleton Univ Dept Geog & Environm Studies Ottawa ON Canada

ISBN: (纸本)9781713899921

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data;this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimodal representations. Our method separately encodes masked-out multispectral optical and synthetic aperture radar samples-aligned in space and time-and performs cross-modal contrastive learning. Another encoder fuses these sensors, producing joint multimodal encodings that are used to predict the masked patches via a lightweight decoder. We show that these objectives are complementary when leveraged on spatially aligned multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our cross- and self-attention matrices. These strategies improve representations and allow our models to effectively extrapolate to images up to 17.6x larger at test-time. CROMA outperforms the current SoTA multispectral model, evaluated on: four classification benchmarks-finetuning (*** arrow 1.8%), linear (*** arrow 2.4%) and nonlinear (*** arrow 1.4%) probing, kNN classification (*** arrow 3.5%), and K-means clustering (*** arrow 8.4%);and three segmentation benchmarks (*** arrow 6.4%). CROMA's rich, optionally multimodal representations can be widely leveraged across remote sensing applications.

关键词： image enhancement

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