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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4813 条记录，以下是421-430 订阅

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Bi-Cubic Interpolation image Scaling Algorithm Based on FPGA Implementation 3

Bi-Cubic Interpolation Image Scaling Algorithm Based on FPGA...

引用

3rd international conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhou, Zeng Wang, Yan Xu, Conghao Zhang, Yang University of South China College of Electrical Engineering Hengyang Hunan China

ISBN: (纸本)9798350372052

image resizing plays a very important role in image processing, offering good scalability and extensibility. Currently, there are some issues with existing scaling interpolation algorithms, such as complex hardware implementation, high resource consumption, long operation times, and poor realtime performance. To address these challenges, this paper introduces a design method based on the image bicubic interpolation scaling algorithm and optimizes the floating-point operation aspect of image processing. Utilizing the FPGA platform, a step-by-step scaling approach is employed, first performing vertical scaling and then horizontal scaling to achieve parallel processing. This design significantly improves scaling efficiency, and its feasibility is demonstrated through functional simulation. © 2023 ieee.

关键词： Field programmable gate arrays (FPGA)

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Enhancing Scene Text Segmentation through Subtask Decomposition 2

Enhancing Scene Text Segmentation through Subtask Decomposit...

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Wang, Yong Chen, Youguang East China Normal University School of Data Science and Engineering Shanghai China

ISBN: (纸本)9798350331417

The field of image processing widely utilizes scene text segmentation technology, with applications extending to image editing and font style transfer. These applications enhance image understanding quality and aid in boosting the performance of numerous computer vision tasks. The advent and progression of deep learning have led to substantial advancements in scene text segmentation technology. However, the limited size of existing scene text segmentation datasets constrains the performance of models. Therefore, we propose an algorithm for synthetic segmentation data. We first pretrain the model using large-scale synthetic data, then fine-tune it on the target dataset to address the issue of limited dataset size. Existing models employ end-to-end segmentation, which presents challenges in segmentation. We propose a scene text segmentation method. By decomposing the segmentation task into subtasks and solving them one by one, the complexity of the task can be reduced compared to direct segmentation of the entire image significantly improving the segmentation effect. The proposed method consists of three modules: a fragment crop module, a fragment segmentation module, and a fragment combination module. The fragment crop module is composed of an additional corp layer added after DBnet. The fragment segmentation module can be embedded with various segmentation methods. The fragment combination module uses the maximum pixel value pasting algorithm to combine the segmented fragments. We call this method Crop-Segmentation-Combination Framework (CSCF). We conducted experiments on the ICDAR 2013 and TextSeg datasets. The CSCF, embedded in Unet within the segment segmentation module, enhanced the text segmentation IoU by 5.80% on the ICDAR 2013 test dataset. Our suggested approach has been shown to notably enhance the efficiency of scene text segmentation. © 2023 ieee.

关键词： image segmentation Scene text segmentation Synthetic data synthetic image generations Text segmentation

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Building Annotated image Dataset for Myanmar Text to image Synthesis

Building Annotated Image Dataset for Myanmar Text to Image S...

引用

2023 ieee conference on computer applications, ICCA 2023

作者： Htwe, Nan Kham Pa, Win Pa University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar

ISBN: (纸本)9781665435994

Text to image synthesis is the translation of images from the input language text. The learning process can become easier when the spoken words can visualize with the images. It is one of the popular research field in combination of NLP and computer vision. Generative Adversarial Networks (GAN) have growth in the generation of images from text descriptions. We build the baseline system of Myanmar text to image synthesis and a type of annotated images dataset because there is not efficient annotated image dataset to be used in this implementation. It was created by using partial part of Oxford-102 flowers dataset. Word2Vec algorithms is used to convert word to vectors for the input sentence to GAN. GAN is applied for generation of images from Myanmar language text. This is the first text to image generation using GAN in Myanmar. The two-evaluation metrics are used to measure the quality of images. The quality of the generated images is evaluated using Inception score. The Frechet Inception Distance (FID) is used to measure the distance between the real images (images from original dataset) and the generated images from the model. © 2023 ieee.

关键词： Generative adversarial networks

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A Model for Content-Based image Retrieval Using Machine Learning 1

A Model for Content-Based Image Retrieval Using Machine Lear...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Mahajan, Yash Batta, Priya Sharma, Muskan Saxena, Devansh Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350315769

With the value of digital data increasing in recent years, substantial improvements have been made in image processing. It is now simpler to produce large image datasets. picture processing is carrying out a variety of processes to improve a picture or extract informative data from it. With several theories and models relating to the retrieval of images from the collection of images called datasets, it has developed into a crucial study subject within the disciplines of engineering and computer science. A well-liked paradigm for the retrieval of images, image retrieval based on content is quicker and more advantageous than textual indexation. The authors of this study provide a paradigm for feature-based picture retrieval. Based on a user query, the model searches a database for the most relevant photographs and presents them with the least amount of vector distance. The suggested model can greatly enhance image retrieval systems and the applications of such systems in numerous sectors. Overall, this study makes a positive contribution to attempts to advance image processing technologies and their usefulness in the contemporary world. © 2023 ieee.

关键词： Machine learning

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Single-Photon Cameras image Reconstruction Using Vision Transformer 3

Single-Photon Cameras Image Reconstruction Using Vision Tran...

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3rd ieee international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Wang, Xingzheng Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350335262

Single-photon camera is a novel camera type that utilizes image sensor with photon-counting capability. Recently, the potential of such sensors to achieve high spatial resolutions (e.g., 10/chip) and frame rates (e.g., 10/sec) is well-established. However, there is a significant difference in the output data between single-photon cameras and traditional CMOS sensor cameras. Therefore, conventional image reconstruction algorithms cannot be utilized. Vison Transformer has impressive performance on image processing tasks, and this paper will design a plug-and-play algorithm for reconstructing images from single-photon cameras and integrate image processing algorithms based on deep neural networks like ViT. The results demonstrate our methods can achieve improvement both in PSNR and SSIM. © 2023 ieee.

关键词： Cameras

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TRANSFORMATION CONSISTENCY FOR REMOTE SENSING image SUPER-RESOLUTION 30

TRANSFORMATION CONSISTENCY FOR REMOTE SENSING IMAGE SUPER-RE...

引用

30th ieee international conference on image processing (ICIP)

作者： Deng, Kai Yao, Ping Cheng, Siyuan Bi, Junyu Zhang, Kun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China INRIA Saclay Ile De France Palaiseau France Inst Polytech Paris Paris France

ISBN: (纸本)9781728198354

Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt the regular data augmentation approaches (such as flip) in natural images to improve model performance. Considering the fact that remote sensing images are all taken from a bird's-eye view and objects appear in multiple directions, we first introduce rotation augmentation method in remote sensing images to promote diversity of samples dramatically, as rotation does not cause semantic problems like people standing upside down in natural images. However, image rotation at various angles implemented by interpolation will cause the inconsistent pixel distribution problem for the pixel level task. Thus, we propose Transformation Consistency Loss Function (TCLF) to narrow the gap between the augmented and original distribution, while expanding the feature space with rotation augmentation method. Extensive experiments are performed on UC-Merced Land-use dataset of 21 remote sensing scenes, and the results as well as ablation studies demonstrate our proposed method outperforms mainstream methods.

关键词： image processing Super-resolution Transformation consistency

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MFA-DAF: Unsupervised Multimodal Medical image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy 2

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Xie, Xinyu Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Electrical Engineering Hengyang China University of South China School of Computing/Software Hengyang China

ISBN: (纸本)9798350331417

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality. © 2023 ieee.

关键词： Detail-aware Fine-grained information Fourier Transform Multimodal medical image fusion

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Energy-Efficient ReRAM-Based High-Precision Discrete Fourier Transform for image Reconstruction

Energy-Efficient ReRAM-Based High-Precision Discrete Fourier...

引用

2023 ieee international conference on Integrated Circuits, Technologies and applications, ICTA 2023

作者： Lu, Fei-Xiong Li, Yi Li, Jian-Cong Xue, Yi-Bai Zhou, Zhi-Wei Miao, Xiang-Shui School of Integrated Circuits Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350344288

In this work, we implemented the discrete Fourier transform (DFT) using a Pt/Al2O3/AlOx/W resistive random-access memory (ReRAM) for high-precision signal processing. By introducing the bit-slicing method, the ReRAM devices are capable of overcoming the imprecise analog computing limitation and can achieve software-comparable processing precision. Validated through the image reconstruction tasks, the ReRAM technology is demonstrated to achieve reconstruction quality virtually equivalent to 8-bit integer precision. Furthermore, this work is evaluated to achieve an energy efficiency of 622 GOPS/W, which is 22× higher than the state-of-the-art digital processors. We expect the ReRAM-technology can be further employed to perform more complex signal processing tasks with high efficiency in practical applications. © 2023 ieee.

关键词： Discrete Fourier transforms

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Hypernetwork-Based Adaptive image Restoration 48

Hypernetwork-Based Adaptive Image Restoration

引用

48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Aharon, Shai Ben-Artzi, Gil Ariel University Department of Computer Science Israel

ISBN: (纸本)9781728163277

Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution. © 2023 ieee.

关键词： DeJPEG image Denoising super-resolution

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Identification of ID Card Number Based on BP Neural Network 7

Identification of ID Card Number Based on BP Neural Network

引用

7th international conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Fang, Legeng Fang, Xinhao Zuo, Zhaoyang Song, Guiliang School of Mechanical Engineering Xijing University Shanxi Xi'an710123 China

ISBN: (纸本)9798350382877

BP neural network has been applied extensively in image processing, with ID card recognition systems being utilized in every facet of everyday life. To reduce the error rate and enhance the effectiveness of use, we employ BP neural network to refine the precision of identity recognition. To begin, the ID card image is pre-processed, then the number is pinpointed and divided, and the pertinent parameters are set to be trained through BP neural network. The output is produced once the precise value is matched, and subsequently the parameters are fine-tuned to attain a greater level of accuracy. © 2023 ieee.

关键词： image processing

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