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Design of Automatic Colouring of Sketch images Based on Deep Learning Algorithm

Design of Automatic Colouring of Sketch Images Based on Deep...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Lian Chen Chengjun Zhou School of Art and Design Hubei University of Technology China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In this paper, we have designed an innovative deep learning model using U-net with PatchGAN discriminator aiming at automatic colouring of sketch images, which provides a novel solution for sketch colouring. Firstly, the image is passed through the dataset to the generator, which receives the input image and tries to convert it into the expected output image. Subsequently, in each iteration, the loss functions of the generator and the discriminator are calculated and the model parameters are updated by the optimiser. Finally, predictions are made on the test dataset and the results are plotted. By passing new unseen data to the trained generator, it was observed that the generation results were as expected and the deep learning coloured images were very close to the actual coloured images. This research brings important implications for the fields of digital art and computer vision. Firstly, it provides a completely new approach to processing sketch images, opening up more possibilities for artists and designers. Second, the model expands new ideas and examples for the application of deep learning techniques in the field of image processing. Finally, in practical applications, this auto-colouring technique is expected to greatly improve productivity and can be widely used in animation, comics and other fields.

关键词： Deep learning Productivity Information science Computer vision image processing Computational modeling Digital art

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Realistic Sketch Face Generation via Sketch-Guided Incomplete Restoration 29

Realistic Sketch Face Generation via Sketch-Guided Incomplet...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Zijie Gu, Chunhua Xie, Ying Zhang, Huichen Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Ministry of Public Security for Road Traffic Safety Wuxi China

ISBN: (纸本)9798350330717

Generating realistic sketches of human faces is an important research direction in the fields of computer vision and computer graphics. However, generating high-quality sketch faces remains challenging, especially when dealing with incomplete input sketch images. In this paper, we propose a framework based on the U-Net generative network, which progressively optimizes network performance through a three-stage training strategy. We introduce the Sketch Guidance and Completion Network (SGCN), which is capable of restoring missing features, completing blurry edges, and handling occluded regions in sketch images. By employing multiple discriminators and adversarial training strategies, we enhance the realism and preserve fine details in the generated results. Through experiments on publicly available sketch face datasets, we demonstrate significant improvements in reconstructing incomplete sketch faces using our approach. The generated images exhibit enhanced completeness, realism, and retain fine details and important facial features. Quantitative evaluations validate the effectiveness and superiority of our method. © 2023 IEEE.

关键词： Face synthesis GAN Sketch-based image synthesis

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An Adaptive Cooperative Coevolutionary Algorithm for parallel Feature Selection in High-Dimensional Datasets 30

An Adaptive Cooperative Coevolutionary Algorithm for Paralle...

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30th Euromicro International conference on parallel, distributed and Network-Based processing, PDP 2022

作者： Firouznia, Marjan Trunfio, Giuseppe A. Amirkabir University of Technology Electrical Engineering Department Tehran Iran University of Sassari Department of Biomedical Sciences Italy

ISBN: (纸本)9781665469586

Nowadays, it is common in many disciplines and application fields to collect large volumes of data characterized by a high number of features. Such datasets are at the basis of modern applications of supervised Machine Learning, where the goal is to create a classifier for newly presented data. However, it is well known that the presence of irrelevant features in the dataset can lead to a harder learning phase and, above all, can produce suboptimal classifiers. For this reason, the ability to select an appropriate subset of the available features is becoming increasingly important. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features since common optimization algorithms often suffer from poor scalability with respect to the size of the search space. In this paper, the problem of feature subset optimization is successfully addressed by a cooperative coevolutionary algorithm based on Differential Evolution. In the proposed algorithm, parallelized for multi-Threaded execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adapting the population size during the optimization results in a significant performance. A numerical investigation on some high-dimensional datasets show that, in most cases, the proposed approach can achieve smaller feature subsets and higher classification performance than other state-of-The-Art methods. © 2022 IEEE.

关键词： Optimization

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Towards Optimal Communication Complexity in distributed Non-Convex Optimization 36

Towards Optimal Communication Complexity in Distributed Non-...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Patel, Kumar Kshitij Wang, Lingxiao Woodworth, Blake Bullins, Brian Srebro, Nati TTIC Chicago IL 60637 USA INRIA SIERRA Paris France Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781713871088

We study the problem of distributed stochastic non-convex optimization with intermittent communication. We consider the full participation setting where M machines work in parallel over R communication rounds and the partial participation setting where M machines are sampled independently every round from some meta-distribution over machines. We propose and analyze a new algorithm that improves existing methods by requiring fewer and lighter variance reduction operations. We also present lower bounds, showing our algorithm is either optimal or almost optimal in most settings. Numerical experiments demonstrate the superior performance of our algorithm.

关键词： Convex optimization

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Proceedings - 2022 23rd ACIS International Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Summer 2022

Proceedings - 2022 23rd ACIS International Summer Virtual Co...

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23rd ACIS International Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Summer 2022

ISBN: (纸本)9798350396379

The proceedings contain 27 papers. The topics discussed include: Tetris: automatic UAF exploit generation by manipulating layout based on reactivated paths;artificial neural network for processing fingerprint image noise;facial expression intensity estimation considering change characteristic of facial feature values for each facial expression;developing a gamification method based on motivation subscales for lifelogging applications;preliminary study of reasoning existing projects' descriptions based on classname word elements;construction and evaluation of a speech emotion classifier using LSTM;concurrency control program generation in genetic programming considering depth of the program tree;reconfiguration cost for reconfigurable computing architectures;and parallel binary search tree construction inspired by thread-level speculation.

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Research and application of image detection technology of pear tree pests and pests 24

Research and application of image detection technology of pe...

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6th International conference on Telecommunications and Communication Engineering

作者： Feng, Yuanyuan Xu, Zhihan Yang, Xiaoyu Chengdu Univ Technol Coll Mech & Elect Engn Chengdu Peoples R China

ISBN: (纸本)9798400709630

This paper aims to achieve precise identification of diseases and pests affecting pear trees through the integration of YOLOv5, Jetson Nano, big data, and deep learning techniques. The objective is to facilitate timely detection of these issues, thereby enabling early prevention and control measures for pear tree health. The study employs YOLOv5 as the primary model, which is implemented on the embedded device jetson Nano. By leveraging the GPU parallel processing capabilities of jetson Nano's deep learning framework, this approach enhances picture analysis and detection speed while improving quality and efficiency. [1] Furthermore, it integrates big data with deep learning methodologies to bolster the accuracy of disease detection and identification. Utilizing these advanced technologies allows for accurate recognition of diseases and pests associated with pear trees through image analysis. This significantly reduces both the complexity involved in detecting such conditions and lowers operational thresholds for practitioners in the field. In comparison to traditional detection methods, YOLOvI technology exhibits no stringent requirements regarding environmental conditions or backgrounds;thus, it remains less susceptible to variations caused by weather factors making it a superior choice for pest and disease detection in agricultural settings.

关键词： image analysis Big data Embedded equipment Plant diseases and insect pests

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Collaborative Segmentation Model for Colonoscopy Ulcers based on Fuzzy Labeling 9

Collaborative Segmentation Model for Colonoscopy Ulcers base...

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9th IEEE International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Lin, Yanning Chen, Jie Ding, Ziqian Chen, Linxu Li, Le Zhu, Zhongsheng Wang, Zhaoxia Li, Jianqiang Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518000 Peoples R China Shenzhen Childrens Hosp Gastroenterol Shenzhen 518000 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China Shenzhen Childrens Hosp Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Inflammatory Bowel Disease (IBD) is a global chronic intestinal inflammatory disease, and its incidence rate increases year by year with the progress of economic globalization. Currently, the diagnosis of IBD in children mainly relies on endoscopic examination, but scoring endoscopic images is a challenging issue, especially in distinguishing different types of ulcers. To address this issue, this article designs a mobile application to accelerate data annotation processing and may provide reference for other unlabeled datasets. In the context of image segmentation, blurring labels has become an important issue. Deep learning methods are widely used in medical image segmentation, but their accuracy depends on high-quality annotated data. However, there are low-quality noise areas in the annotated data, and obtaining accurate and high-quality annotations becomes more time-consuming with limited annotation budgets. This article proposes a collaborative training framework to improve learning of noisy pixels. This framework determines the label confidence of an image by calculating the similarity between image pixels and surrounding pixels. Then, two parallel deep networks were constructed for semantic prediction, which aimed to guide each other on pixels that may have noise. By applying consistency in dual network prediction, the semantic information of uncertain pixels is corrected as much as possible. Experimental results have shown that this framework is slightly superior to models trained with pixel level precise labels, thus more effectively utilizing existing annotated data in the case of fuzzy labels.

关键词： image segmentation fuzzy label data annotation

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GPU Accelerated Modelling and Real-time Rendering of Fluid Motion 38

GPU Accelerated Modelling and Real-time Rendering of Fluid M...

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38th International conference on image and Vision Computing New Zealand, IVCNZ 2023

作者： Valentine, William Mukundan, Ramakrishnan University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes a fluid rendering pipeline that uses OpenGL-4 shaders to employ the parallel processing capabilities of the GPU. The fluid's surface mesh is produced using tessellation shader stages where the input patches are assigned tessellation levels based on the fluid heightmap's curvature. The curvature is stored using a texture buffer object which allows access by shaders, thus allowing the tessellation calculations to be carried out in parallel. Use of this adaptive tessellation method increases both the simulation's framerate as well as its capacity to handle a greater number of primitives. Furthermore, it more optimally distributes the mesh's vertices to effectively increase the level of detail without using more primitives. Polygon culling using the geometry shader further optimises the number of primitives used to define the fluid surface. The Phong-Blinn model is used for surface lighting. We propose two GPU-based fluid surface flow visualisation methods. Texture buffer objects can be used to store and update a surface texture. Alternatively, particle positions are updated each frame using the geometry shader and stored in a buffer object using transform feedback. These flow visualisation techniques are particularly effective for communicating the swirling motion of vortices. © 2023 IEEE.

关键词： Graphics processing unit

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Residual Hybrid Attention Enhanced Video Super-Resolution with Cross Convolution 7th

Residual Hybrid Attention Enhanced Video Super-Resolution wi...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Yuan, Shiqian Li, Boyue Zhao, Xin Lan, Rushi Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Int Joint Res Lab Spatiotemporal Informat & Intel Guilin 541004 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

In video super-resolution reconstruction, traditional methods often fall short in capturing details, particularly in edges and occluded areas, which affects the realism and clarity of the images. To address this issue, we propose a novel model-the Residual Hybrid Attention-Enhanced Video Super-Resolution Model, augmented by Cross Convolution techniques, denoted as RCVSR. The model ingeniously integrates a residual hybrid attention mechanism, refining the learning of global and local features through parallel channel attention and self-attention mechanisms. Simultaneously, our model introduces overlapping cross-attention blocks to enhance dynamic interactions between frames, thereby boosting the model's performance. Furthermore, the design of the cross-convolution blocks allows for parallel processing of vertical and horizontal gradient information in images, effectively extracting edge details. In multiple benchmark tests, the RCVSR model demonstrated its excellent reconstruction effects and outstanding performance.

关键词： Video super-resolution Hybrid attention Cross convolution

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FERI: Feature Enhancement and Relational Interaction for image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： Computer vision Fuses image matching Semantics Feature extraction Natural language processing

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