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检索条件"任意字段=11th International Conference on Graphics and Image Processing, ICGIP 2019"

共 597 条记录，以下是1-10 订阅

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16th international conference on Computer graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

16th International Conference on Computer Graphics, Visualiz...

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16th international conference on Computer graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

ISBN: (纸本)9789898704429

the proceedings contain 32 papers. the topics discussed include: professional course offered within a technical academy concerning the innovative BIM technology;heatmap matrix: using reordering, discretization and filtering resources to assist multidimensional data analysis;BIM teaching and programing in dynamo applied in the maintenance of buildings;3D pose estimation by grouped feature fusion and motion amplitude encoding;identifying wear particles with their shape attribute;using reorderable heatmaps as a support for data assimilation in petroleum reservoir management and development processes;detection of fashion landmarks based on pose estimation and human parsing;design elements extraction based on unsupervised segmentation and compact vectorization;domain adaptation in image dehazing: exploring the usage of images from virtual scenarios;and interpolation and context magnification framework for classification of scene images.

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Research on image Foreground Target Extraction Algorithm under Uneven Illumination 15

Research on Image Foreground Target Extraction Algorithm und...

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15th international conference on graphics and image processing, icgip 2023

作者： Jun, Yan Liang, Yi Ji Guilin University of Aerospace Technology School of Automotive Engineering Guangxi Guilin541004 China

ISBN: (纸本)9781510675018

In order to solve the problem that the image binarization segmentation under uneven illumination conditions can not be balanced in terms of processing speed and effect, this paper proposes a method of background estimation and threshold segmentation for images under uneven illumination conditions by using the second moving average method. this method uses the quadratic moving average prediction method to predict the trend of the gray value of each row of the image, so as to obtain the mutation point of the foreground target and the background to supplement the background image under the missing foreground target, and then uses the difference method to separate the foreground target and the background, and extract the target image for recognition. Experimental results show that the algorithm designed in this paper solves the phenomenon that the smaller neighborhood value of the local threshold algorithm will cause the hollowing out of the large foreground image to a certain extent, and increases the image processing rate to 140ms / frame, which can effectively improve the image background processing effect and accelerate the rate of image segmentation. It can basically be applied to the real-time detection scene of image anti-light interference under certain conditions. © 2024 SPIE. All rights reserved.

关键词： image segmentation

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DAPFormer: Dual-Attention and Pyramid-Aware Network for Medical image Segmentation 15

DAPFormer: Dual-Attention and Pyramid-Aware Network for Medi...

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15th international conference on graphics and image processing, icgip 2023

作者： Yuan, Yejin Zhang, Hao Xiong, Zhaoyu Qin, Jiacheng Xu, Dan Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9781510675018

Medical image segmentation aims to categorize pixels into different regions according to their corresponding tissues / organs in medical image. In recent years, due to Transformer's outstanding ability in the field of computer vision, various visual Transformers has been exploited in this task. However, these models often suffer from quadratic complexity in the self-attention and multi-scale information interaction. In this paper, we propose a novel dual attention and pyramid-aware network, DAPFormer, to solve the aforementioned limitations. It effectively combines efficient and channel attention into a dual attention mechanism to capture spatial and inter-channel relationships in the feature dimensions, meanwhile maintains computational efficiency. Additionally, we use pyramid-aware module to redesign the skip connection, modeling the cross-scale dependencies and addressing complex scale variations. Experiments on multi-organ cardiac and skin lesion segmentation datasets demonstrate that DAPFormer outperforms state-of-the-art methods. © 2024 SPIE. All rights reserved.

关键词： Computational efficiency

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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th international conference on graphics and image processing, icgip 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. this paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. the student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. the process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. this model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the imageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Foreground Prediction for image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th international conference on graphics and image processing, icgip 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

this paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. the proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. the extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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Pathological image Segmentation of Gastric Cancer Based on Deep Learning 15

Pathological Image Segmentation of Gastric Cancer Based on D...

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15th international conference on graphics and image processing, icgip 2023

作者： Zhou, Hehu Pan, Jingshan Na, Li Jing, Ge Zhou, Chengjun Du, Wantong Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology the Second Hospital of Shandong University China

ISBN: (纸本)9781510675018

Gastric cancer is a serious health threat and pathological images are an important criterion in its diagnosis. these images can help doctors accurately identify cancerous regions and provide important evidence for clinical decision-making. thanks to the remarkable achievements of deep learning technology in the field of image processing, an increasing number of superior image segmentation models have emerged. the Swin-Unet model has achieved great success in the field of image segmentation. However, when applied to the image segmentation of gastric cancer pathological section data, the segmentation boundary appears jagged. We have put forth two potential solutions. Initially, we devised an attention connection module to supplant the skip connections within the model, thereby enhancing the model’s predictive precision. Subsequently, we engineered a prognostic processing unit that inputs the model’s predictive outcomes and employs a Conditional Random Field (CRF) for further predictive computations. the enhanced model increases the DSC by 2% and decreases the HD by 17%. Additionally, the issue of jagged boundaries in prediction results has been better optimized. We conducted comparative and ablation experiments, and the results showed that our improved method increased the accuracy of the model’s predictions and reduced the jaggedness of the results at the boundary. © 2024 SPIE. All rights reserved.

关键词： image segmentation

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Programming Interfaces for Cross-platform image Rendering and Deep Learning GPGPU 11

Programming Interfaces for Cross-platform Image Rendering an...

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11th international Scientific conference on Computer Science, COMSCI 2023

作者： Georgiev, Georgi Georgiev Lazarova, Milena Kirilova Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

GPU programming can present difficulties in regards to the availability of interfaces on all device compatible platforms. Furthermore, APIs designed specifically with cross-platform use in mind have shown tendencies to sacrifice abstraction for performance or vice versa, leading to overall difficulties in development of GPU-accelerated applications. the purpose of the paper is to present various application programming interfaces (APIs) for high-performance image rendering and parallel computations for training deep learning models utilizing graphics processing units (GPUs) that allow cross-platform deployment. © 2023 IEEE.

关键词： graphics processing unit

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Model Inference Using TVM on AMD GPU: image Classification and LLM Case Studies 11

Model Inference Using TVM on AMD GPU: Image Classification a...

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11th international conference on Electrical, Electronic and Computing Engineering, IcETRAN 2024

作者： Malbaša, Andrija Petrović, Nenad Arsić, Branko Syrmia LLC University of Niš Faculty of Electornic Engineering Serbia Syrmia LLC University of Kragujevac Faculty of Science Serbia

ISBN: (纸本)9798350386998

Deep learning models have revolutionized the field of artificial intelligence by exhibiting exceptional capabilities in understanding complex patterns and making accurate predictions. However, the growing complexity of these models presents a challenge in achieving real-time inference, which is crucial for practical applications. As deep learning models become more popular, there is an increasing demand for faster inference times to enable real-time decision-making in critical scenarios, such as fire detection and classification. In recent years, the advancement in parallel computing hardware, such as graphics processing units (GPUs), specialized tensor processing units (TPUs) and application-specific integrated circuits (ASICs), has provided a significant boost to deep learning inference. One of the challenges in accelerating deep learning inference on diverse hardware platforms is the lack of optimized implementations of mathematical operations specifically tailored for each platform. this limitation can significantly hinder the performance of deep learning models on these platforms. In this paper, we focus on Advanced Micro Devices (AMD) GPU graphics cards and explore the utilization of the Apache Tensor Virtual Machine (TVM) compiler framework to accelerate deep learning inference for Generative Pre-trained Transformer 2 (GPT-2) large language model (LLM) and ResNet-18 image classification model, represented in PyTorch and Open Neural Network Exchange (ONNX) formats, respectively. © 2024 IEEE.

关键词： Application specific integrated circuits

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image Restoration Method based on Adaptive Multiple Priors Fusion in Scattering Scenes 14

Image Restoration Method based on Adaptive Multiple Priors F...

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14th international conference on graphics and image processing (icgip)

作者： Ruan, Rui Zeng, Weihui Lei, Yu Guo, Yangyang Liang, Zheng Anhui Univ Sch Internet Hefei 230039 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

image is usually characterized by low contrast, blurry detail and distorted color due to complex imaging mechanism of scattering scene, which veils many valuable image information. To improve images quality in scattering scenes, we propose a novel image restoration method based on adaptive multiple priors fusion, which mainly includes two components: a multiple priors constraint-based transmission estimation component and an adaptive multiple priors fusion-based backscattered light estimation component. Firstly, we proposed a scoring formula by fusing lightness prior, contrast prior and saturation prior to locate the backscattered light, which can effectively avoid the limitation of using any single priors. Afterward, we explore a new prior called extended dark channel prior (EDCP), and then adaptive combining EDCP, dark channel prior (DCP) and underwater dark channel prior (UDCP) to estimate the transmission robustly. Finally, Extensive experiments on images of different scattering scenes demonstrate that the proposed method is effective and superior for image restoration.

关键词： image restoration multiple priors scattering medium

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Blind Restoration of Vertical Motion Blurred image Based on Point Spread Function Estimation 13

Blind Restoration of Vertical Motion Blurred Image Based on ...

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13th international conference on graphics and image processing (icgip)

作者： Qin, Fengqing Xie, Yuxin Jiang, Li Wen, Xingdong Li, Chaorong Zeng, Jiao Cao, Lilan Yibin Univ Fac Artificial Intelligence & Big Data Yibin 644000 Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Blind image restoration has always been a hot and difficult problem in the field of image processing. As the vertical motion blur image has obvious characteristics in the frequency domain, the distance between the center point and the nearest horizontal black strip is automatically estimated by setting thresholds, and the estimated vertical motion point spread function is obtained;the image is blindly restored by the constrained least squares algorithm. Experimental results show that the proposed vertical motion point spread function estimation algorithm has high accuracy, and the restored image has better visual effects and higher PSNR.

关键词： image blind restoration point spread function frequency domain center constrained least squares

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