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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 4811 条记录，以下是181-190 订阅

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NARV: An Efficient Noise-Adaptive ResNet VAE for Joint image Compression and Denoising

NARV: An Efficient Noise-Adaptive ResNet VAE for Joint Image...

引用

2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Huang, Yuning Duan, Zhihao Zhu, Fengqing Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States

ISBN: (纸本)9798350313154

image compression and denoising are two fundamental problems in image processing and have many real-world applications. In recent years, learning-based compression methods have achieved promising rate-distortion performance. However, most learning-based compression methods are trained with clean-to-clean image pairs in a noise-unaware manner, resulting in bits misallocation when input images contain noticeable noises. To tackle this problem, we propose an efficient end-to-end method, named Noise-Adaptive ResNet VAE (NARV), which is capable of handling both clean and noisy input images. Experimental results show that NARV achieves better rate-distortion performance under various noise levels while maintaining a small model size and fast processing time for practical implementation. The code of our method is publicly accessible at https://***/Eventhyn/NARV © 2023 ieee.

关键词： image compression

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Analysis and applications of 2D Discrete Fourier Transform in image Denoising and Edge Detection 4

Analysis and Applications of 2D Discrete Fourier Transform i...

引用

2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Li, Ziang Ridley College 2 Ridley Road St. CatharinesONL2R 7C3 Canada

ISBN: (纸本)9798350308877

The paper explores what is 2 Dimensional Fourier Transform and how it can be used for image processing, specifically denoising and edge enhancing. Starting from the Fourier theorem and Fourier series, the paper traverses through a detailed procedure for the derivation from the 1 dimensional continuous to 1 dimensional discrete to 2 dimensional discrete to 2 dimensional inverse discrete Fourier Transform. After the image is transformed to frequency domain with Fourier transform, highpass and lowpass filters can be applied to edit the information for the purpose of image processing. Such image processing techniques is then implemented on to two examples, a matrix example and an image example, and one accounts for the mathematics approach and another for analyzing the effect of the highpass and lowpass filters, respectively. © 2023 ieee.

关键词： Discrete Fourier transforms

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Research on Segmentation Technology of Process image Based on Segmentation Algorithm

Research on Segmentation Technology of Process Image Based o...

引用

2023 international conference on computer Simulation and Modeling, Information Security, CSMIS 2023

作者： Huang, Zhao School of Fine Arts Beijing Institute of Fashion Technology Beijing China

ISBN: (纸本)9798350343724

With the advent of the digital age, the fields of computer vision and digital image processing have developed rapidly. Process image plays an important role in the automatic production process. However, due to the complexity and diversity of process images, how to segment process images accurately and efficiently is still a challenging problem in practical applications. Process image segmentation refers to the segmentation of a complex process image into different regions or objects for subsequent analysis and processing. However, due to the complexity of the process image, including illumination changes, noise, occlusion and other factors, the segmentation process becomes difficult. In this paper, the process image segmentation technology based on segmentation algorithm is studied and discussed, in order to provide more innovation and breakthrough ideas for process image segmentation, and provide a more accurate and efficient solution for process image segmentation. © 2023 ieee.

关键词： image segmentation

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Quality Evaluation of Tone Mapping HDR Omnidirectional image with Multi-regions and Multi-levels 20

Quality Evaluation of Tone Mapping HDR Omnidirectional Image...

引用

20th ACS/ieee international conference on computer Systems and applications (AICCSA)

作者： Zheng, Xuelei Yu, Mei Jiang, Gangyi Ningbo Univ Fac Informat Sci & Engn Ningbo Peoples R China

ISBN: (纸本)9798350319439

Unlike ordinary omnidirectional imaging, which produces underexposure or overexposure of some areas under wide range of lighting conditions, tone mapping high dynamic range omnidirectional image (denoted as TM-HOI) presents more detailed information with high dynamic range (HDR) techniques. Aiming at the multiple distortions in TM-HOI processing, this paper proposes a blind TM-HOI quality metric with multi-regions and multi-levels analysis. Considering the conversion of image projection formats and the unique distortion caused by user behavior in immersive environments, feature extraction in the proposed metric is divided into the viewport based module and the Crasher parabolic projection based module. At the same time, considering the coding distortion, tone mapping (TM) distortion and mixed distortion in the TM-HOI and the different manifestations of this mixed distortion in different regions, this paper further extracts perceptual features to characterize image distortion by performing bit plane layer decomposition and detail/basic layer decomposition on different regions of TM-HOI. Finally, the extracted perceptual features are used as the input of Random forest to construct the nonlinear relationship between the feature space and subjective opinion scores. The experimental results indicate that the proposed metric has better consistency with the human visual system compared to representative blind quality metrics.

关键词： high dynamic range tone mapping high dynamic range omnidirectional image blind quality evaluation bit plane layer decomposition

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Histopathological Classification of Colon Tissue images with Self-Supervised Models 31

Histopathological Classification of Colon Tissue Images with...

引用

31st ieee conference on Signal processing and Communications applications (SIU)

作者： Erden, Mehmet Bahadir Cansiz, Selahattin Demir, Cigdem Gunduz Koc Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350343557

Deep learning techniques have demonstrated their ability to facilitate medical image diagnostics by offering more precise and accurate predictions. Convolutional neural network (CNN) architectures have been employed for a decade as the primary approach to enable automated diagnosis. On the other hand, recently proposed vision transformers (ViTs) based architectures have shown success in various computer vision tasks. However, their efficacy in medical image classification tasks remains largely unexplored due to their requirement for large datasets. Nevertheless, significant performance gains can be achieved by leveraging self-supervised learning techniques through pretraining. This paper analyzes performance of self-supervised pretrained networks in medical image classification tasks. Results on colon histopathology images revealed that CNN based architectures are more effective when trained from scratch, while pretrained models could achieve similar levels of performance with limited data.

关键词： deep learning histopathological image classification self-supervised learning pretrained models

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Construction of image processing Model Based on computer Vision Algorithm

Construction of Image Processing Model Based on Computer Vis...

引用

image processing and computer applications (icipca), ieee international conference on

作者： Lin Shao School of Media & Communication Wuhan Textile University Wuhan Hubei China

As an important part of the mobile robot platform to perceive the external environment, the computer transmits the collected real-time images to the processing unit. After the image information is analyzed and processed, different functions are realized according to the actual needs. image processing technology is a widely used technology type derived from the continuous development of computer technology. Using it in fruit detection and grading can improve the efficiency of fruit detection and grading, optimize the standard of fruit detection and grading, achieve the purpose of controlling the cost of manual detection and grading, and ensure the profit of fruit industry. We know that the human brain is the most effective biological intelligence system known, and the ability of human visual system to recognize, process and process images far exceeds any existing computer and information processing system. This paper implements image processing technology based on computer vision algorithm, so as to express the actual coordinates of objects in 3D space through 3D voxels, and can also correct the distorted images caused by projection. Compared with traditional BP neural network, image processing technology based on computer vision algorithm has more advantages and higher accuracy.

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Poster: A Distributed Deep Reinforcement Learning System for Medical image Segmentation 8

Poster: A Distributed Deep Reinforcement Learning System for...

引用

8th ieee/ACM international conference on Connected Health - applications, Systems and Engineering Technologies (CHASE)

作者： Xu, Lanyu Oakland Univ Rochester MI 48063 USA

ISBN: (纸本)9798400701023

Multi-institutional collaboration is an emerging deployment of medical imaging processing with the goal to address the scarce annotation problem. While most of the efforts in this domain focus on the supervised machine learning models and the model performance improvement, there lack the discussion about the distributed system performance, such as the trade-off between collaboration and efficiency, i.e., communication cost and processing time. In this work, we propose a distributed system based on deep reinforcement learning for medical image segmentation. Preliminary experiments are conducted on single and multiple CPU and GPU environments to demonstrate the system performance and the trade-off. We highlight some insights for better designs of multi-institutional collaboration in the future.

关键词： Medical image segmentation deep reinforcement learning distributed machine learning

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Research on Data Science processing System Under computer Virtual Reality Technology

Research on Data Science Processing System Under Computer Vi...

引用

image processing and computer applications (icipca), ieee international conference on

作者： Andrew Liang Hong Beier Sun Ziyuan An Shanghai American School Shanghai China San Domenico School San Anselmo USA Sierra Canyon High School CA USA

This paper designs a digital signal processor based on computer virtual reality technology. The system uses multi-core cooperative work to complete data processing and storage. The system consists of four layers: hardware abstraction layer, system core layer, distributed message communication mechanism and system service layer. Its multi-task kernel implements preemptive scheduling based on priority, synchronization and communication primitives between tasks, real-time interrupt processing and application-oriented cache management mechanism. The system also designed LCD module and keyboard module as man-machine interaction interface. The hardware interface diagram is given. The realization of software programming is also discussed. The experimental results show that multi-core collaborative processing is used to complete data processing and storage functions. The single-core resource utilization rate is effectively reduced by 26%.

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Segmenting Sonar images with an Enhanced OTSU Algorithm 2

Segmenting Sonar Images with an Enhanced OTSU Algorithm

引用

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

作者： Yang, Fan University of Jinan School of Electrical Engineering Jinan250022 China

ISBN: (纸本)9798350331417

Sonar image segmentation technique is crucial for underwater target tracking, among other things. Due to the undersea environment's influence, noise is easily absorbed, which leads to a poor tracking performance. In order to address the issues with the traditional one-dimensional and two-dimensional OTSU algorithms, which have a slow computation time and poor anti-noise performance, this paper proposes a fast OTSU segmentation algorithm based on wavelet boosting and analyses the algorithmic principles and implementation steps of one-dimensional OTSU algorithm, two-dimensional OTSU algorithm and its fast algorithm, fast algorithm with the variance as the eigenvalue, and maximum entropy algorithm. On the basis of this, the aforementioned algorithms are used to perform image segmentation on four photos. The segmentation findings demonstrate the method's quick processing speed and strong noise resistance, which have significant implications for sonar image segmentation applications. © 2023 ieee.

关键词： image segmentation OTSU second generation wavelet transform sonar images

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Research on Multimodal image Fusion Method Based on Deep Learning

Research on Multimodal Image Fusion Method Based on Deep Lea...

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image processing and computer applications (icipca), ieee international conference on

作者： Wang Jing Zhang Luqiao Chengdu University of Information Technology Chengdu China

Accurate assessment of medical image quality is of paramount importance as it forms the cornerstone of diagnoses, where even minor discrepancies can lead to misdiagnoses. To achieve this, two key aspects, realism, and comprehensibility, are employed to evaluate the quality of medical images. Factors such as image richness, clarity, and saliency are analyzed to precisely reflect the image's reliability. However, due to variations in data originating from different hospitals, medical professionals, and equipment, internal structures may exhibit significant differences. To address this issue, this study proposes a multimodal data-based medical image fusion model that captures the correlation and complementary information between heterogeneous data. By combining the regional Laplacian pyramid (LLP) with convolutional neural networks, the convolutional network is enhanced. The source images are decomposed using LLP, and an improved deep convolutional neural network generates an optimal weight map to guide the fusion process, resulting in a fused image. Experimental results demonstrate that the proposed method achieves favorable fusion effects in both subjective visual assessments and objective evaluation metrics.

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