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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1688 条记录，以下是81-90 订阅

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processing landslide hazard prediction using satellite image segmentation 3

Processing landslide hazard prediction using satellite image...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Chiu, Chenyuan Hsu, Sung-Chi Huang, Yishuo Zhang, Nan Peng, Chen Labib Ul Islam, Syed School of Civil Engineering Jiaying University Guangdong Meizhou514015 China Dept. of Construction Engineering Chaoyang Univ. of Technology 168 Jifong East Rd. Wufong Taichung41349 Taiwan School of Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Dept. of Civil Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781510689077

Remote sensing technology has become one of the indispensable techniques for the investigation of geologic disasters at home and abroad. However, the interpretation of bare ground classification by satellite multispectral images is often interfered by atmospheric clouds in mountainous areas, shadows on the mountains, and the phenomenon of Speckle Signals, which seriously affects the correctness of the interpretation images, and is prone to errors such as misjudgment or omission. In this study, the SPOT 5 satellite images of the study area were segmented by applying the Multiphase Level Set Framework using MATLAB computation. The results show that the problem of the speckle signals can be eliminated efficiently and quickly, and the complex terrain and features can be segmented into simple objects, which can solve the problem of the spatial recognition rate without being affected by the resolution of the image itself, and improve the image accuracy. This can solve the problem of spatial recognition without being affected by the resolution of the image itself, and improve the accuracy of satellite image interpretation, which can be used as a possible landslide trend. © 2025 SPIE.

关键词： Landslides

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improved clothing segmentation algorithm based on Deeplabv3 + network 3

Improved clothing segmentation algorithm based on Deeplabv3 ...

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3rd international conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Shi, Xiujin Wu, Danxin Donghua University Songjiang Shanghai China

ISBN: (纸本)9781510667563

In recent years, the fashion industry has developed rapidly, and the image is one of the main carriers for people to obtain clothing information. Therefore, the analysis and processing of clothing image through the technology in the field of artificial intelligence has become a major research content in the field of clothing development. image segmentation technology can be used to segment different clothing regions from the clothing image in order to facilitate the subsequent analysis and processing of clothing image, which is one of the basic research directions of clothing image analysis. At present, most of the methods belong to the traditional segmentation methods, or are based on the deep convolutional neural network (DCNN). Especially for Deeplab, FCN and their improved network segmentation models, although these network models have different degrees of improvement compared with the traditional methods, there are still some problems such as the fitting degree of clothing region segmentation is not high and the effect of clothing edge segmentation is poor. To solve these problems, this paper adds object context information extraction module and edge optimization module based on Deeplab v3+ network. The object context information extraction module focuses on the context information aggregation method in semantic segmentation, and enhances the feature representation of pixel points with the help of the feature information of the corresponding clothing region, so as to improve the label classification effect of pixel points in the clothing image and improve the fitting degree of clothing region segmentation. The edge optimization module makes the pixels in the edge area directly use the label prediction of the pixels in the same category area to greatly improve the edge segmentation effect. Our method performs well on the DeepFashion-MultiModal dataset, and experiments show that the improved Deeplabv3 + network outperforms the original Deeplabv3 + network and oth

关键词： Pixels

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Deep Learning and Medical image Analysis: Epistemology and Ethical Issues

Deep Learning and Medical Image Analysis: Epistemology and E...

引用

3rd international conference on image processing and vision engineering, improve 2023

作者： Lizzi, Francesca Retico, Alessandra Fantacci, Maria Evelina National Institute for Nuclear Physics Pisa Division Pisa Italy Department of Physics University of Pisa Pisa Italy

ISBN: (纸本)9789897586422

Machine and deep learning methods applied to medicine seem to be a promising way to improve the performance in solving many issues from the diagnosis of a disease to the prediction of personalized therapies by analyzing many and diverse types of data. However, developing an algorithm with the aim of applying it in clinical practice is a complex task which should take into account the context in which the software is developed and should be used. In the first report of the World Health Organization (WHO) about the ethics and governance of Artificial Intelligence (AI) for health published in 2021, it has been stated that AI may improve healthcare and medicine all over the world only if ethics and human rights are a main part of its development. Involving ethics in technology development means to take into account several issues that should be discussed also inside the scientific community: the epistemological changes, population stratification issues, the opacity of deep learning algorithms, data complexity and accessibility, health processes and so on. In this work, some of the mentioned issues will be discussed in order to open a discussion on whether and how it is possible to address them. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Edge Cloud Collaboration Intelligent Assistive Cane for Visually Impaired People 3

Edge Cloud Collaboration Intelligent Assistive Cane for Visu...

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3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Veerasamy, B. Kumar Reddy, A.Sai Chandu, Animgi Sankar Reddy, K.Siva Venkata Naga Gopi Manikanta, K. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9781665464871

The market for assistive technology for the blind and visually impaired is plagued by high prices and a lack of usefulness. These people face numerous challenges in their daily lives. This study has developed and deployed a low-cost smart assistive cane based on computer vision, sensors, and a local cloud cooperation system, specifically for people with visual impairments. Obstacle detection, fall detection, and visitor light detection features have also been developed and included to make moving easier for those with visual impairments. As part of a part-cloud cooperation strategy to improve the user experience, a photo captioning tool and an object recognition function with high-speed processing power were also developed. It shows the characteristics of low energy consumption, potent real-time performance, flexibility to a few circumstances, and convenience, which can protect the safety of visually impaired individuals while travelling and may enable them to more effectively perceive and interpret their environment. © 2023 IEEE.

关键词： image enhancement

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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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Neighborhood image processing Using Verilog HDL 3

Neighborhood Image Processing Using Verilog HDL

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3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Selarka, Trushti Viradiya, Yash Shah, Dhaval Institute of Technology Nirma University Electronics and Communication Engineering Department Ahmedabad India

ISBN: (纸本)9798350330724

This paper explores the utilization of a customized hardware processor implemented on FPGA using Verilog HDL for image enhancement techniques. The processor applies a moving window filter mechanism to perform various operations including Low-Pass Filtering (smoothening), High-Pass Filtering (sharpening), and Sobel Operation (edge detection). By leveraging the power of Verilog HDL, grayscale images are effectively enhanced in the spatial domain using neighborhood processing operations. The utilization of Verilog HDL offers significant advantages, such as high-speed operation and parallel processing, surpassing the capabilities of traditional software-based simulations. This research showcases the potential of FPGA-based hardware implementations for achieving real-time image enhancement with superior performance and efficiency. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Fuzzy Inference System in a Local Eigenvector Based Color image Smoothing Framework

Fuzzy Inference System in a Local Eigenvector Based Color Im...

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3rd international conference on image processing and vision engineering, improve 2023

作者： Almutairi, Khleef Morillas, Samuel Latorre-Carmona, Pedro Instituto Universitario de Matematica Pura y Aplicada Universitat Politécnica de Valéncia Camino de Vera s/n Valencia46022 Spain Mathematics Department Faculty of Science Albaha University Saudi Arabia Departmento de Ingenierıa Informatica Universidad de Burgos Avda. Cantabria s/n Burgos09006 Spain

ISBN: (纸本)9789897586422

Noise filtering in colour images is a complex task since it is essential to distinguish between structural and noise information in the image. It would therefore be important to simultaneously remove noise while keeping the original image details. This paper proposes a method based on a fuzzy inference system to eliminate noise and retrieve original image details. images are transformed from an RGB space to an eigenvector based space and this transformation is fed to the fuzzy system. Results confirm the validity of the approach, its superior performance when compared to the eigenvector based framework it is based on, and its competitive behaviour when compared to other state-of-the-art methods. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Noise abatement

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Research on improved diffusion algorithm based on gradient quantization in target localization algorithm 3

Research on improved diffusion algorithm based on gradient q...

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3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Liu, Yuanheng Yin, Yong School of Information Engineering Wuhan University of Technology Wuhan China Chongqing Research Institute Wuhan University of Technology Chongqing China

ISBN: (纸本)9798350359961

As the industrial automation field rapidly advances, the application of image processing in practical engineering becomes increasingly sophisticated. This study focuses on the target localization problem in industrial production lines, utilizing the Histogram of Oriented Gradient (HOG) algorithm to extract the gradient direction features from images. Building upon this, we propose an improved gradient diffusion algorithm. The algorithm enhances the precision and detail of each pixel's gradient direction by identifying primary gradient directions within the pixel's neighborhood and adjusting the central pixel's gradient information. This algorithm does not require a timeconsuming training phase, enhancing the robustness of image processing. It not only addresses interference from various image acquisition environments but also improves the reliability of the localization algorithm when dealing with irregularly shaped workpieces. Our experimental results have confirmed the effectiveness of the proposed method in the precise detection and localization within industrial production. When faced with various scenarios, the accuracy of its positioning exceeds 99%, and the execution time does not exceed 0.05 seconds. © 2023 IEEE.

关键词： Computer vision Gradient direction diffusion Gradient feature extraction HOG

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On Real-Time Object Recognition by Single image Dehazing Method Using Deep Learning Approach 3

On Real-Time Object Recognition by Single Image Dehazing Met...

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3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Pathak, Shruti Doegar, Amit National Institute of Technical Teachers Training and Research Department of Computer Science Chandigarh160019 India

ISBN: (纸本)9798350330724

In the past decade, various haze removal techniques have been widely reported for object recognition. But hitherto little has been identified on the use of single image dehazing using transfer learning approach for object detection. Single image dehazing is an emerging computer vision technology which offers some of the extreme benefits over the existing techniques such as consumes less processing time, requires less space for real time dehaze purpose, etc. In this study, we combine both the object detection and image dehazing methods for real time applications such as-remote sensing, video surveillances, driverless automatic vehicles, etc. This paper presents an effective and efficient image dehazing method using transfer learning which helps to recognize objects in real time with more clarity and that can automatically detect objects with a high recognition rate and lesser probability of error. Our tests show that object detection becomes less accurate as the haze intensity increases;yet, under all haze circumstances (low, medium, or heavy), our jointly trained model AOD-net +YOLO v3 consistently outperforms non-joint and naïve YOLO v3 techniques. © 2023 IEEE.

关键词： Object detection

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Efficient vision transformer for dynamic embedding of multiscale features 3

Efficient vision transformer for dynamic embedding of multis...

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3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Zhang, Mingrui Wang, Ronggui Yang, Juan Xue, Lixia School of Computer and Information Hefei University of Technology Anhui230601 China

ISBN: (纸本)9781510681880

vision Transformer (ViT) fully demonstrates the potential of the transformer architecture in the field of computer vision. However, the computational complexity is proportional to the length of the input sequence, thus limiting the application of transformers to high-resolution images. In order to improve the overall performance of vision Transformer, this paper proposes an efficient vision Transformer (MLVT) with dynamic embedding of multi-scale features, adopting the pyramid architecture, replacing the self-attention operation with linear self-attention, proposing a local attention enhancement module to address the problem of the dispersal of linear self-attention scores that ignores local correlation, and supplementing the local attention with the convolution of the self-attention-like computation. operation of self-attention-like computation is utilized to supplement the local attention. Aiming at the increase of feature dimension in pyramid architecture, the bottleneck of linear self-attention computation is changed from sequence length to feature dimension, and the linear self-attention with compressed feature dimension is proposed. In addition, since multi-scale inputs are crucial for processing image information, this paper proposes a flexible and learnable dynamic multi-scale feature embedding module, which dynamically adjusts the weights of different scale features according to the input image for fusion. A large number of experiments on image classification and target detection tasks show that competitive results are achieved while reducing the computational effort. © 2024 SPIE.

关键词： image fusion

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