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检索条件"任意字段=1st International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011"

共 114 条记录，以下是1-10 订阅

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2nd international Workshop on Recent Advances in digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

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2nd international Workshop on Recent Advances in digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

ISBN: (纸本)9783030307530

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in digital Security. The topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text recognition;improving Ancient Cham Glyph recognition from Cham Inscription images Using Data Augmentation and Transfer Learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video Analysis of Historical Films;the Epistle to Cangrande Through the Lens of Computational Authorship Verification;a Cockpit of Measures for image Quality Assessment in digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A Learning Approach for Informative-Frame Selection in US Rheumatology images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail Data Using a Deep Learning Identification Experience;a Large Scale Trajectory Dataset for Shopper Behaviour Understanding;Improving Multi-scale Face recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep Learning for Consumer Research.

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Research on the Implementation and Application of Improved Canny Algorithm for image Edge Detection Based on FPGA 24

Research on the Implementation and Application of Improved C...

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1st international conference on image processing Machine Learning and pattern recognition

作者： Huang, Haojie Luo, Junzhe Beijing Inst Technol Comp Coll Zhuhai Peoples R China

ISBN: (纸本)9798400707032

With the continuous development of digital image processing technology, edge detection technology is playing an increasingly important role in the field of image processing. The Canny algorithm is a classical gradient-based edge detection algorithm with excellent performance and robustness. However, due to its large computational amount and poor real-time performance, the traditional software implementation method has been unable to meet the demands of modern high-speed image processing. Therefore, the Canny algorithm hardware is turned into a popular research direction. As a programmable logic device, FPGA has the advantages of high flexibility, short development period and strong parallel computing power, which is widely used in the field of digital signal processing. At present, there has been a lot of research work on FPGA in implementing the Canny algorithm, but most of the schemes have some problems, such as slow speed and high resource occupancy rate. Therefore, this paper presents an improved scheme for the hardware design of Canny algorithm based on FPGA, aiming to improve the speed and efficiency of image processing while reducing the utilization of hardware resources.

关键词： FPGA Canny algorithm Hardware design digital image processing technology

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Research on distribution inspection planning strategy based on artificial intelligence image recognition 24

Research on distribution inspection planning strategy based ...

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1st international conference on image processing Machine Learning and pattern recognition

作者： Gao, Fengxi Wu, Zhuohang Han, Zhentao Wang, Qixiang He, Xin Sun, Yan Zhao, Dewei Zhao, Wengang Sun, Mingze State Grid Liaoning Elect Power Compant Bcon Res Inst Shenyang Liaoning Peoples R China

ISBN: (纸本)9798400707032

In order to actively promote the construction of new power systems and the digital transformation and upgrading of power grid companies, and explore the application of artificial intelligence technology, the power grid company has built artificial intelligence "two libraries and one platform", that is, artificial intelligence platform, sample library, model library, and has carried out a lot of work in the application of artificial intelligence technology in equipment management, safe operation, marketing customer service and other business fields, and has precipitated rich samples, algorithms, and model achievements. With the increasing demand for intelligent application services and the increasing demand for samples and models in various business departments, it is urgent to introduce capabilities such as intelligent identification of electrical components and defect detection in scarce scenes on top of the existing artificial intelligence basic support capabilities to realize model training, iteration, optimization and improvement of accuracy, and deeply integrate the basic capabilities of artificial intelligence with the core business applications of the power grid. Further improve the ability to empower the profession and serve the grassroots.

关键词： New power systems Drone inspection Artificial intelligence Defect detection

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image Generation Algorithm of Text Description Based on styleGAN 24

Image Generation Algorithm of Text Description Based on Styl...

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1st international conference on image processing Machine Learning and pattern recognition

作者： Dai, Hui Hao, Pan Wuhan Univ Technol Wuhan Peoples R China

ISBN: (纸本)9798400707032

The training of the text-to-image task relies on a large number of image-text pairs, and the problems of insufficient sample size of the dataset and inprominent image subject content will greatly affect the overall performance of the model. In addition, most of the existing methods are stacked multi-stage generation structure, and the quality of generated images is difficult to meet the actual application requirements. Aiming at the above problems, this paper enhances the dataset and designs a single-stage text generation image model based on styleGAN. Based on the analysis of global semantic features and semantic diversity expressions, this paper carries out adaptive perturbation text data enhancement, and designs an image cropping method that fuses boundary information, and adopts random cropping and rotation to complete image data enhancement. In the generator, the conditional style construction network fuses global semantic information and hidden space vectors to obtain conditional style control vectors that control different visual features of the image. The generation network adopts a progressive generation structure, accepts the control of conditional style in the way of AdaIN, and adds random noise to each layer at the same time, which improves the authenticity of the image and enriches the details. At the same time, the semantic inner product is added to the discriminator, and the loss function of cyclic consistency loss and visual semantic matching loss are introduced to further improve the semantic consistency of graphics and texts. The IS, FID and R-precision values of this model on the CUB-200-2011 dataset reached 4.96, 14.11 and 75.20%, respectively.

关键词： Cross-modal Generative adversarial network image generation Text-generated images Perceived loss

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Abnormal recognition Technology of 3D Virtual Scene image Based on Wireless Network Sensor 1

Abnormal Recognition Technology of 3D Virtual Scene Image Ba...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Guangwei Hexi University Gansu Zhangye China

ISBN: (纸本)9798350316469

The performance and reliability requirements of wireless network sensors are getting higher and higher. In the complex 3D virtualization scene, abnormal recognition of sensor data becomes a challenging task. Therefore, this study aims to explore an effective technique for identifying anomalies in wireless network sensor images in 3D virtualized scenarios. In this study, deep learning techniques were used, combined with image processing and pattern recognition methods, to achieve accurate identification of anomalies in wireless network sensor images. Through training and testing on a large number of 3D virtualized scene images, the experimental results show that the proposed method has high accuracy in identifying anomalies in wireless network sensor images. The recall rate of the algorithm in this paper is 77%-85%, compared with the traditional method, the method in this paper can more accurately detect and classify various types of abnormal conditions, including signal interference, equipment failure and abnormal data. In addition, the method studied in this paper also shows good real-time performance, which can detect and identify anomalies in real time when processing large-scale sensor data. © 2023 IEEE.

关键词： 3D Virtual Scene image recognition Network Sensor Wireless Network

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High Speed Multiplier using Embedded Approximate 4-2 Compressor for image Multiplication 1

High Speed Multiplier using Embedded Approximate 4-2 Compres...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Teja, Thogiti Sai Aditya Sai Teja, G. Ravindra, Jvr Maddisetti, Lavanya Vardhaman College of Engineering Department of Ece India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Multiplication is the most widely used operation in a variety of applications such as digital filters and neural networks. In certain applications (image processing), precise computation is not required and, even low-accuracy computing can yield meaningful results. Since approximate computing is less complex and consumes less resources, it can be used in image processing and multimedia applications. Regular adders have a longer critical delay than compressor adders, making them suitable for use in multiplier architectures. Compressors can be employed in multiplication operations to reduce number of partial products, delay and power dissipation. We presented a 8×8 Dadda multiplier with an approximated 4:2 compressor in this research. On a pixel-by-pixel basis, this multiplier is used to multiply two images. © 2022 IEEE.

关键词： Adders

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Felis catus disease detection in the digital era: Combining CNN and Random Forest 1

Felis catus disease detection in the digital era: Combining ...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Srivastava, Prateek Bhattacherjee, Abhishek Rawat, Ramesh Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350330915

Cat skin illnesses have traditionally been diagnosed manually, which can be laborious and arbitrary. Researchers offer a novel solution to this problem that utilizes Max Pooling layers and neural networks based on convolution (CNNs) for autonomous disease recognition. To extract complex features while boosting generalization, the study focuses on combining two convolutional layers into a CNN architecture, before adding a Max Pooling layer. By combining the effectiveness of Max Pooling and CNNs, this integration accelerates processing while learning significant texture and pattern information from photos. They show the efficacy of the method in correctly detecting a wide range of cat skin illnesses through careful experimentation and performance evaluation. Utilizing a selected dataset of medical photos, data enhancement techniques, and several measures like precision, recall, precision, and F1-score, they test the efficacy of our methodology. The findings demonstrate the potential of combining Max Pooling and convolutional feature extraction for automating and enhancing the accuracy of cat skin disease diagnosis. This study offers a strong tool that improves efficiency and accuracy and advances veterinary dermatology diagnosis. The suggested method follows the current state of digital diagnostics and holds out the prospect of an entirely novel age of streamlined identification of illnesses for feline health and veterinary care. © 2023 IEEE.

关键词： cat health feline welfare image processing precision veterinary care

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1st international conference on Artificial Intelligence: Theories and Applications, ICAITA 2022

1st International Conference on Artificial Intelligence: The...

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1st international conference on Artificial Intelligence: Theories and Applications, ICAITA 2022

ISBN: (纸本)9783031285394

The proceedings contain 23 papers. The special focus in this conference is on Artificial Intelligence: Theories and Applications. The topics include: Convolution Neural Network Based Approach for Glaucoma Disease Detection;Deep Multi-Scale Hashing for image Retrieval (DMSH);genetic Programming for Screen Content image Quality Assessment;A Progressive Deep Transfer Learning for the Diagnosis of Alzheimer’s Disease on Brain MRI images;An Optimized MSER Using Bat Algorithm for Skin Lesion Detection;accurate Detection of Brain Tumor Using Compound Filter and Deep Neural Network;Bi-ESRGAN: A New Approach of Document image Super-Resolution Based on Dual Deep Transfer Learning;preface;expanding Convolutional Neural Network Kernel for Facial Expression recognition;a Novel Classification Method for Group Decision-Making Dimensions;intelligent Cache Placement in Multi-cache and One-Tenant Networks;enhanced Grey Wolf Optimizer for Data Clustering;artificial Orca Algorithm for Solving University Course Timetabling Issue;global Automatic Tuning of Fuzzy Sliding Mode Controller for an Inverted Pendulum: A Genetic Solution;Intelligent ITSC Fault Detection in PMSG Using the Machine Learning Technique;Series Elastic Actuator Cascade PID Controller Design Using Genetic Algorithm Method;Quantum Natural Language processing: A New and Promising Way to Solve NLP Problems;learning More with Less Data: Reaching the Power-Law Limits in steganalysis Using Larger Batch Sizes;offline Text-Independent Writer Identification Using Local Black pattern Histograms;Robust Method for Breast Cancer Classification Based on Feature Selection Using RGWO Algorithm;digital Modulation Classification Based on Automatic K-Nearest Neighbors Classifier.

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Intelligent image recognition and Classification Technology in digital Media 1st

Intelligent Image Recognition and Classification Technology ...

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1st international conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Huan Guangzhou International Economics College Guangzhou510540 China

ISBN: (纸本)9789819633487

With the increasing popularity of digital media along with the fact that massive image data has already gained popularity, the automatic image recognition and classification have become a very critical and urgent problem, this study investigates and tests intelligent image recognition and classification techniques for digital media based on major methods such as CNN, SVM and RNN. Different algorithms and models are applied to training and test image data to evaluate the performance of various methods in terms of accuracy and processing time. The experimental results shows that the CNN method can achieve high accuracy in image classification task and the multi-layer convolution and pooling operations can effectively extract image features. The SVM method is better than the CNN in terms of processing time but have slightly lower accuracy. The RNN method is ranked between the CNN and SVM in terms of accuracy and processing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image representation

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Comparative Analysis of Edge Detectors Applying on the Noisy image Using Edge-Preserving Filter 1st

Comparative Analysis of Edge Detectors Applying on the Noisy...

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1st international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Kumar, Tapendra Bardhan, Shawli Indian Inst Informat Technol Sch Comp Una 177220 Himachal Prades India

ISBN: (纸本)9783030960407;9783030960391

In computer vision, edge detection is a fundamental technique. It is used as a pre-processing technique to make image segmentation, pattern recognition, and feature extraction more comfortable. digital images are often corrupted by the noise that causes the detection of spurious edges during edge detection. Thus, we'd like to suppress the maximum amount of noise as potential while retaining important image features such as edges, corners, and other sharp structures. This research compares multiple edge detection methods applied to a filtered image by adding speckle noise. In this paper, four edge detection operators have been applied to an image denoised by various edge-preserving filters, and their performance is evaluated based on the performance metrics peak signal-to-noise ratio (PSNR) and mean squared error (MSE). images from the Barcelona images for Perceptual Edge Detection Dataset (BIPED) are used for performance evaluation of filters and edge detection techniques. The experimental results show that a bilateral filter with a Canny edge detection operator is the most optimized method for edge detection of speckle-noise-affected images.

关键词： image processing Edge detection Noise Edge-preserving filters PSNR MSE BIPED

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