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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15618 条记录，以下是811-820 订阅

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Research on Computer image Recognition Technology Based on Artificial Intelligence

Research on Computer Image Recognition Technology Based on A...

引用

International conference on systems and Informatics (ICSAI)

作者： Jing Li Yuxi Liu Yahong Ma Feng Liu School of Electronic Information Xijing University Xian China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In order to solve the problem of fast processing for ultra-high-resolution images in embedded systems, this paper proposes a multi-mode tracking and recognition method based on embedded processing architecture, through the construction of a multi-GPU hardware processing system, the large-scale image parallel processing algorithm is studied, and a variety of image processing modes such as full-frame, full-window tracking and tracking scanning are designed, and experimental verification is carried out on ultra-high-resolution images. The multi-GPU architecture and large-scale image parallel processing algorithm described in this paper have certain advantages over traditional processing methods.

关键词： image recognition Software design image resolution Software algorithms Computer architecture Switches Parallel processing Hardware Artificial intelligence Informatics

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

A Machine Learning Framework for Skin Cancer Detection using...

引用

International image processing, Applications and systems conference (IPAS)

作者： Mary Linda Panakal Augustine Sotirios Spanogianopoulos Mhd Saeed Sharif Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London London UK School of Electrical and Mechanical Engineering University of Portsmouth Portsmouth UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

关键词： Deep learning Machine learning algorithms image processing Transfer learning Medical diagnosis Reliability Skin cancer image classification

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Guo, Li Xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： image enhancement

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Contour Extraction of Surgical Stoma Surfaces Using 2.5D images from Smartphone 3D Scanning 26

Contour Extraction of Surgical Stoma Surfaces Using 2.5D Ima...

引用

26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Monroy, Francisco Luquin Hussein, Rania University of Washington Electrical and Computer Engineering Department Seattle United States

ISBN: (纸本)9798350304985

In this paper, we present a technique for extracting stoma outlines from 2.5D images acquired through smartphone-based 3D scanning. Accurate stoma outlining plays a crucial role in tailoring ostomy wafers, thereby minimizing stoma damage, preserving the health of surrounding skin, and ultimately enhancing the health-related quality of life for stoma patients. Our proposed method leverages a widely used contour algorithm and incorporates error analysis to assess its effectiveness and precision. Through a series of experiments, we investigate the potential and limitations of smartphone 3D scanning in achieving precise and personalized stoma contouring. Additionally, we introduce an error metric to evaluate the performance of the technique quantitatively. Our findings shed light on the possibilities for advancements in 3D scanning technology and image processing techniques, marking a significant step towards harnessing smartphone technology for improved ostomy management and patient care. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： image segmentation

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Attention-Based Grayscale image Colorization 20

Attention-Based Grayscale Image Colorization

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Sun, Lei Ju, Dongjie Ke, Shi School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Binshui Xidao Extension 391 Tianjin300384 China

ISBN: (纸本)9798350320831

image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional neural networks (CNN) emerge in an endless stream, including automatic colorization methods and A coloring method based on a reference image. In the coloring method based on the reference image, an existing problem is that an algorithm for calculating the similarity between the grayscale image and the reference image needs to be designed to map the color blocks in the reference image to the grayscale image. And we use the attention mechanism to spread color implicitly, and propose an attention-guided image colorization network, which extracts features and corresponding color information from the reference image through a reference encoding sub-network, and simultaneously passes a target encoding sub-network The network extracts grayscale features from grayscale images, uses attention to calculate the correlation between the two types of features, and then restores the features and reconstructs colors through the decoding network. Our method can quickly capture the color of the reference feature, accurately transfer it to the target feature, and effectively solve the problem of small target loss during downsampling. By validating on multiple datasets, the results show that our method is effective, outperforming multiple state-of-the-art image colorization algorithms. © 2023 IEEE.

关键词： Color

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S2P: State-conditioned image Synthesis for Data Augmentation in Offline Reinforcement Learning 36

S2P: State-conditioned Image Synthesis for Data Augmentation...

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Cho, Daesol Shim, Dongseok Kim, H. Jin Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul South Korea Seoul Natl Univ Interdisciplinary Program AI Seoul South Korea Seoul Natl Univ Artificial Intelligence Inst Seoul Natl Univ AIIS Seoul South Korea

ISBN: (纸本)9781713871088

Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks.

关键词： Reinforcement learning

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Hole repair of 3D terrain mesh in satellite stereo image reconstruction

Hole repair of 3D terrain mesh in satellite stereo image rec...

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2024 International conference on Remote Sensing, Mapping, and Geographic Information systems, RSMG 2024

作者： Bao, Qiwei Liu, Jun Liu, Xining Zhao, Limin Yuan, Zhanliang Chen, Xingfeng Li, Jiaguo Li, Huafu Zhang, Haixiang School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo454003 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Sanya Institute of South China Sea Geology Guangzhou Marine Geological Survey China Geological Survey Sanya572024 China

ISBN: (纸本)9781510685826

When constructing a 3D surface grid using point cloud data closely matched with high-resolution satellite stereo images, holes often appear due to matching failures or limitations of triangulation algorithms, affecting the grid's integrity and the accuracy of subsequent applications. To solve this problem, a boundary contraction algorithm is studied for repairing mesh holes. The research focuses on different terrain conditions such as mountains, plains, and cities, and evaluates the effectiveness of boundary contraction algorithms in repairing holes. Using visual inspection methods, the quality of the repaired mesh is checked to ensure its integrity, smoothness, continuity, and topological correctness. The experimental results of GF-7 satellite images show that the boundary contraction algorithm can effectively fill holes in various terrain environments, significantly improving the geometric quality and overall integrity of the mesh, verifying its feasibility as a reliable mesh repair method, and providing strong support and verification for the application of the boundary contraction algorithm in actual terrain data processing. © 2024 SPIE.

关键词： Mesh generation

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A Robotic Approach for Crack Detection through the Integration of Cameras and LiDARs

A Robotic Approach for Crack Detection through the Integrati...

引用

conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace systems

作者： Alamdari, Ali Ghadimzadeh Ebrahimkhanlou, Arvin Drexel Univ Dept Mech Engn & Mech 3141 Chestnut St Philadelphia PA 19104 USA

ISBN: (纸本)9781510660793;9781510660809

Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, image processing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.

关键词： Structural health monitoring cameras LiDAR crack detection data fusion image processing algorithms intelligent systems

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Reconstruction quality evaluation of Compressed Sensing image Mapping Spectrometer

Reconstruction quality evaluation of Compressed Sensing Imag...

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conference on AOPC - Computing Imaging Technology

作者： Yang, Shuya Ding, Xiaoming Yuan, Hao Lu, Dunqiang Yan, Qiangqiang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Shaanxi Key Lab Opt Remote Sensing & Intelligent Xian 710119 Peoples R China Xian Inst Opt & Precis Mech CAS Key Lab Spectral Imaging Technol Xian 710119 Peoples R China

ISBN: (纸本)9781510672413;9781510672406

This paper uses traditional algorithms and deep learning algorithms to recover datacube obtained by CASSI and CSIMS in order to verify that CSIMS outperforms CASSI by comparing the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Relative spectral Quadratic Error (RQE) of the reconstructed datacube. The experimental results show that the datacube of CASSI and CSIMS can be both reconstructed by ADMM- TV algorithm which is the most effective among the traditional algorithms. PSNR of the reconstructed datacube of CASSI is 32.50 dB, while that of CSIMS is 35.53 dB, with an increase of 3.03 dB. By using deep learning algorithm, both systems improve substantially under the PnP-HSI network, with PSNR of CASSI growing to 38.85 dB and that of CSIMS growing to 41.97 dB, which can be seen that CSIMS is still 3.12 dB higher than CASSI.

关键词： Snapshot spectral imaging Compression sensing image mapper Deep learning

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Semantic-Layer: A High-Level Semantic Ontology For Data-Driven Approaches

Semantic-Layer: A High-Level Semantic Ontology For Data-Driv...

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2023 International conference on Decision Aid Sciences and Applications, DASA 2023

作者： Belkebir, Malak Maarouk, Toufik Messaoud Nini, Brahim Belkebir, Djalila University of Oum El Bouaghi Research Laboratory on Computer Science's Complex Systems Dept. Mathematics and Computer Science Oum El Bouaghi Algeria Khenchela University Icosi Laboratory Dept. Nof Mathematics and Computer Science Khenchela Algeria Kasdi Merbah University Dept. Mathematics and Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350342055

Recent years have seen a breakthrough in object detection in computer vision thanks to the development of deep learning algorithms. Although neural approaches appear to perform on par with or even better than human judgment for several benchmarks, issues remain for identifying random concepts in random fields. In this paper, we propose and construct a high-level semantic ontology called the 'semantic-layer', which can be used as an inference engine as well as in training for data-driven models, such as, computer vision, image retrieval, and NLP (Natural language processing) systems. In addition, the proposed layer is also destined to query generation systems, which aim to modify or enhance the users' queries before being transmitted to the server of the research engines. © 2023 IEEE.

关键词： Computer vision

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