检索结果-内蒙古大学图书馆

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Zhang, Zhilu Zhang, Shuohao Wu, Renlong Zuo, Wangmeng Timofte, Radu Xing, Xiaoxia Park, Hyunhee Song, Sejun Kim, Changho Kong, Xiangyu Wu, Jinlong Zhang, Jianxing Tan, Jingfan Liu, Zikun Luo, Wenhan Lin, Wenjie Jiang, Chengzhi Han, Mingyan Liu, Zhen Jiang, Ting Luo, Jinting Cheng, Shen Li, Linze Niu, Xinhan Liu, Shuaicheng Dai, Kexin Yang, Kangzhen Hu, Tao Chen, Xiangyu Cao, Yu Yan, Qingsen Zhang, Yanning Chen, Genggeng Yang, Yongqing Dong, Wei Dai, Xinwei Zhou, Yuanbo Qiu, Xintao Tang, Hui Deng, Wei Gao, Qingquan Tong, Tong Zhang, Peng Chen, Yifei Xiong, Wenbo Song, Zhijun Cheng, Pu Feng, Taolue He, Yunqing Zhou, Daiguo Huang, Ying Ma, Xiaowen Wu, Peng Harbin Inst Technol Harbin Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Samsung Res China Beijing SRC B Beijing Peoples R China Samsung Elect Dept Camera Innovat Grp Suwon South Korea Sun Yat Sen Univ Guangzhou Peoples R China Megvii Technol Beijing Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Beijing Peoples R China Shenzhen Inst Adv Technol Shenzhen Peoples R China Xian Univ Architecture & Technol Xian Peoples R China Fuzhou Univ Fuzhou Peoples R China Mi Lab Fuzhou Peoples R China

ISBN: (纸本)9798350365474

Low-light photography presents significant challenges. Multi-image processing.methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.

关键词： Challenge Exposure Bracketing image Restoration and Enhancement

来源：评论

学校读者我要写书评

暂无评论

Credibility Metrics for Student-Assigned Labels of Textual Comments 54

Credibility Metrics for Student-Assigned Labels of Textual C...

引用

54th ieee Frontiers in Education conference, FIE 2024

作者： Akinepalli, Varsha Rao Pardeshi, Sourabh Patel, Dhruv Mukesh Datta, Arnab Chhabra, Banpreet Singh Gehringer, Edward F. North Carolina State University Department of Computer Science RaleighNC United States

ISBN: (纸本)9798350351507

This research paper describes how pattern recognition can be used to validate student-assigned labels and improve a training dataset natural language processing. In our preceding research, we attempted to enhance peer assessment by harnessing the power of natural language processing.(NLP) and machine learning (ML) to critically evaluate the substance and quality of peer-review comments. This required training data, which we had students create by labeling review comments they had received from peers on various aspects of their work. Our previous paper delineated strategies for the automatic validation of labels ('tags') applied by students, aiming to enhance the reliability of the data fueling our ML algorithms. This paper examines the metrics introduced in our previous work, and studies how effective they are in the evaluation of actual labels assigned by students. © 2024 ieee.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Wang, Yuqing Wang, Yizhi Yu, Longhui Zhu, Yuesheng Lian, Zhouhui Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Vector font synthesis is a challenging and ongoing problem in the fields of computer Vision and computer Graphics. The recently-proposed DeepVecFont [27] achieved state-of-the-art performance by exploiting information of both the image and sequence modalities of vector fonts. However, it has limited capability for handling long sequence data and heavily relies on an image-guided outline refinement post-processing. Thus, vector glyphs synthesized by DeepVecFont still often contain some distortions and artifacts and cannot rival human-designed results. To address the above problems, this paper proposes an enhanced version of DeepVecFont mainly by making the following three novel technical contributions. First, we adopt Transformers instead of RNNs to process sequential data and design a relaxation representation for vector outlines, markedly improving the model's capability and stability of synthesizing long and complex outlines. Second, we propose to sample auxiliary points in addition to control points to precisely align the generated and target Bezier curves or lines. Finally, to alleviate error accumulation in the sequential generation process, we develop a context-based self-refinement module based on another Transformer-based decoder to remove artifacts in the initially synthesized glyphs. Both qualitative and quantitative results demonstrate that the proposed method effectively resolves those intrinsic problems of the original DeepVecFont and outperforms existing approaches in generating English and Chinese vector fonts with complicated structures and diverse styles.

关键词： image and video synthesis and generation

来源：评论

学校读者我要写书评

暂无评论

Single image Dehazing Using CNN 2nd

Single Image Dehazing Using CNN

引用

2nd International conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Bhadane, Samarth Bidwe, Ranjeet Vasant Zope, Bhushan Pune Institute of Computer Technology Maharashtra Pune India Maharashtra Lavale Pune India

ISBN: (纸本)9789819979530

Particulate matter in the atmosphere obscures the visibility of the atmosphere, causing a condition known as haze. Other natural phenomena like mist, fog, and dust also obscure the vision;this is because of scattering of light which attenuates the light intensity. All these instances are responsible for the degradation of image quality. Hazy images are problematic because these images cannot be used for computer vision and image processing.applications like pattern and object recognition. Dehazing images improve the clarity and contrast of the images making them more suitable for computer vision and image processing. This paper presents a method of dehazing images using CNN. The proposed model is trained on D-HAZY (Ancuti et al. in 2016 ieee international conference on image processing.(ICIP), 2016) and SOTS (Li et al. in ieee Trans image Process 28:492–505, 2019) datasets which contain a mix of natural and synthesized hazy images. To assess the model’s performance, we employ PSNR and SSIM metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Deep-TLBO: Achieving Robust Deformable Medical image Registration Leveraging Deep Learning and Teaching Learning-Based Optimization

引用

APPLIED MAGNETIC RESONANCE 2025年第6期56卷 769-801页

作者： Arora, Paluck Mehta, Rajesh Ahuja, Rohit Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India

For a doctor to diagnose a patient and conduct quantitative analysis, medical images must be accurately registered. Deep learning-based image registration methods have been explored extensively, but accurate registration of medical images still remains a major concern. To address the issue of accurate alignment in medical images, this paper presents an approach employing unsupervised learning algorithm U-shaped convolution neural network (U-Net) model, followed by teaching learning-based optimization (TLBO) along with affine transformation for grayscale as well as colored medical image registration. The combined two-channel image generated using moving and fixed image is given as an input to encoder and decoder phase of U-Net model to learn image features and generate displacement field. To predict the spatial transformation parameters, a set of control points are generated to define the deformation field and moving image feature to produce the warped image. To improve the quality of warped image, TLBO with rigid transformation parameter (RTP) on fixed and U-Net warped image is applied by detecting the optimum value of transformation parameters. The proposed approach is implemented and evaluated using five different datasets for 2D and 3D monomodal medical MR and CT image modalities as well as multimodal clinical datasets. For 3D representation, transfer learning is used to obtain the warped images using the 3D pre-trained weights of VoxelMorph U-Net model. In comparison to state-of-the-art approaches like symmetric image normalization (SyN) and VoxelMorph, the Dice score value increases from 0.742 as reported by Balakrishnan (proceedings of the ieee computer society conference on computer Vision and pattern recognition, 2018) to 0.9542 and 0.710 as reported by Zhou (ieee 18th International Symposium on Biomedical Imaging (ISBI), 2021) to 0.9024, while the Hausdorff distance decreases from 2.0521 Zhang (Appl Intell 12:1-18) to 1.5607 and 2.0446 Zhang (Appl Intell 12

关键词： Affine transforms

来源：评论

学校读者我要写书评

暂无评论

Pyramid Ensemble Structure for High Resolution image Shadow Removal

Pyramid Ensemble Structure for High Resolution Image Shadow ...

引用

2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Meituan Group China

ISBN: (纸本)9798350302493

Existing methods for shadow removal in high-resolution images may not be effective due to challenges such as the time-consuming nature of training and the loss of visual data during image cropping or resizing, highlighting the necessity for the development of more efficient methods. In this paper, we propose a novel Pyramid Ensemble Structure (PES) for High Resolution image Shadow Removal. Our approach takes advantage of multiple scales by constructing pyramid inputs that allow for the capturing of a wide range of shadow sizes and shapes. We then train the network in pyramid stages to enhance global information processing. Furthermore, an ensemble of different shadow removal models is employed, and the maximum value is chosen to indicate the least amount of remaining shadow in the output. Experiments on both validation and testing data sets confirm the effectiveness of our method. In the image Shadow Removal Challenge competition, our method obtained 22.36 PSNR score (1st place) and 0.70 SSIM score (2nd place) on the test sets. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Beyond the Fourth Paradigm - the Rise of AI 19

Beyond the Fourth Paradigm - the Rise of AI

引用

19th ieee International conference on e-Science, e-Science 2023

作者： Marek, Andreas Rampp, Markus Reuter, Klaus Laure, Erwin Max Planck Computing and Data Facility Garching Germany

ISBN: (纸本)9798350322231

Thanks to the availability of huge amounts of data and improved computational resources, AI methods are gaining importance in scientific workflows, from image recognition and natural language processing.to materials science. In many domains the usage of AI is under active investigation and first results show a tremendous potential, suggesting that AI will have significant impact way beyond the currently dominating examples of image and language processing. © 2023 ieee.

关键词： image recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Feature Consolidation Network for Burst Super-Resolution

Adaptive Feature Consolidation Network for Burst Super-Resol...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Mehta, Nancy Dudhane, Akshay Murala, Subrahmanyam Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Indian Inst Technol Ropar CVPR Lab Bara Phool Punjab India Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Modern digital cameras generally count on image signal processing.(ISP) pipelines for producing naturalistic RGB images. Nevertheless, in comparison to DSLR cameras, low-quality images are generally output from portable mobile devices due to their physical limitations. The synthesized low-quality images usually have multiple degradations - low-resolution owing to small camera sensors, mosaic patterns on account of camera filter array and subpixel shifts due to camera motion. Such degradation usually restrain the performance of single image super-resolution methodologies for retrieving high-resolution (HR) image from a single low-resolution (LR) image. Burst image super-resolution aims at restoring a photo-realistic HR image by capturing the abundant information from multiple LR images. Lately, the soaring popularity of burst photography has made multi-frame processing.an attractive solution for overcoming the limitations of single image processing. In our work, we thus aim to propose a generic architecture, adaptive feature consolidation network (AFCNet) for multi-frame processing. To alleviate the challenge of effectively modelling the long-range dependency problem, that multi-frame approaches struggle to solve, we utilize encoder-decoder based transformer backbone which learns multi-scale local-global representations. We propose feature alignment module to align LR burst frame features. Further, the aligned features are fused and reconstructed by abridged pseudo-burst fusion module and adaptive group upsampling modules, respectively. Our proposed approach clearly outperforms the other existing state-of-the-art techniques on benchmark datasets. The experimental results illustrate the effectiveness and generality of our proposed framework in upgrading the visual quality of HR images.

关键词： Degradation Visualization Adaptive systems Superresolution Signal processing Feature extraction Transformers

来源：评论

学校读者我要写书评

暂无评论

Low-Power pattern recognition System Using Spintronics Compute-in-memory Architecture 18

Low-Power Pattern Recognition System Using Spintronics Compu...

引用

18th ieee Nanotechnology Materials and Devices conference, NMDC 2023

作者： Shreya, Sonal Rezaeiyan, Yaseer Sadeghi, Maryam Kaushik, Brajesh Moradi, Farshad Aarhus University Electrical and Computer Engineering Department Aarhus8200 Denmark Indian Institute of Technology Electronics and Communication Engineering Department Roorkee India

ISBN: (纸本)9798350335460

pattern recognition plays an important role in image recognition, classification, and information processing. In this work, energy energy-efficient spintronics-based pattern recognition design using in-memory computing architecture is presented. The paper presents a faster, energy-efficient, and area-efficient pattern recognition approach with three computational steps per pixel recognition process using the training and mean image size. The reduction in critical switching current owing to the voltage-controlled switching mechanism results in a 43% reduction in power consumption as compared to all-spin logic-based design approach for recognizing a 3× 3 pixel image size pattern. This circuitry saves 33% area overhead when compared to conventional compute-in-memory (CiM) architectures based on spin orbit torque. The proposed CiM architectures can further be used for real-time pattern recognition for several applications. © 2023 ieee.

关键词： Magnetic anisotropy

来源：评论

学校读者我要写书评

暂无评论

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing.applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing.attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 ieee.

关键词： Hamming distance

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：