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

共 16444 条记录，以下是1491-1500 订阅

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Adaptive Rate Block Compressive Sensing Based on Algorithmic Reconstruction Error

Adaptive Rate Block Compressive Sensing Based on Algorithmic...

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Chonghuan Song Jianhua Chen School of Information Science and Engineering Yunnan University Kunming Yunnan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Compressive sensing (CS) for a full-sized image requires a large sensing matrix, consuming significant storage space and computational resources. Block-based CS addresses this but allocates the same number of samples to each block, ignoring the sparsity differences among various blocks. Additionally, the original image's sparsity is unknown in practical systems. Therefore, it is necessary to accurately estimate the sparsity from some initial sampling information. This paper proposes an adaptive rate block CS model based on the difference of the results of two reconstruction algorithms. The image is initially sampled at the sampling end. Then a high-accuracy algorithm and a low-accuracy algorithm are used to reconstruct initial sampled signals respectively. The sparsity of each image block is estimated by the difference between the two initial reconstructed signals, allowing the appropriate allocation of the number of samples for a block. The main computations of this scheme are concentrated at the reconstruction end, which can effectively save resources at the sampling end. Experimental results show that the proposed scheme can effectively improve reconstruction quality while reducing the sampling rate.

关键词： Surveillance Measurement uncertainty Signal processing algorithms Reconstruction algorithms Signal processing Sensors Resource management image reconstruction Compressed sensing Videos

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Classification and detection of skin diseases based on CNN-powered image segmentation 3

Classification and detection of skin diseases based on CNN-p...

引用

3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Vasudeva Reddy, T. Anirudh Reddy, R. Bhanu Teja, C.S. Sai Charan Reddy, N. Chandra Sekhar Rao, N. Hima B V Raju Institute of Technology Department of ECE Telangana India

ISBN: (纸本)9798350338607

Skin diseases have become more prevalent in recent years, posing a significant burden on healthcare systems globally. These conditions can range from noncancerous problems like acne, eczema, and vitiligo to more severe diseases such as melanoma, basal and squamous cell carcinoma. Skin ailments not only have an impact on physical health but can also affect the patient's psychological well-being, especially when the face is disfigured or damaged. Early detection and diagnosis of dermatological conditions can significantly improve survival rates as their severity can worsen over time. This article covers various methods used to identify and diagnose skin diseases, including natural diagnostic techniques and image pre-processing mechanisms and classification algorithms used globally. By understanding these techniques, healthcare professionals can detect skin diseases accurately, leading to better patient outcomes. Advancements in technology and research in this field continue to progress, providing hope to those who suffer from skin diseases. © 2023 IEEE.

关键词： Python

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A Novel Deep Learning Approach for Heliostat Dirt Segmentation and Severity Assessment

A Novel Deep Learning Approach for Heliostat Dirt Segmentati...

引用

IEEE International conference on Signal processing, Communications and Computing (ICSPCC)

作者： Wei Zhao Zhongtao Pan Tao Sheng Junfeng Zhang Yiming Xue Luoxiao Yang Department of New Energy Project Powerchina Northwest Engineering Corporation Limited Xi'an China Qinghai Branch Powerchina Renewable energy Corporation Limited Qinghai China Department of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Photothermal power generation is a promising technique for converting solar radiation into electricity with high efficiency and stability. However, the performance and maintenance of photothermal power plants depend on the cleanliness and reflectivity of the heliostats. This paper introduces an innovative approach to addressing the challenges of dirt detection and segmentation on heliostats. Leveraging the capabilities of deep learning, we propose the Multi-Scale Heliostat Dirt Segmentation and Classification (MSHDSC) framework, integrating a novel multi-scale feature fusion module (MSFFM) with an enhanced DeepLabV3+ network. This framework effectively segments small dirt areas on heliostat images, facilitating precise cleaning strategies. A unique aspect of our work is the introduction of an unsupervised clustering algorithm post-segmentation, which categorizes dirt based on color and texture, assigning a severity score to each category. This categorization assists in determining the cleaning complexity and prioritizing maintenance efforts. Experimental results show that our method outperforms several state-of-the-art image segmentation models in terms of accuracy and efficiency and provides useful information for targeted and prioritized cleaning of heliostats by robots or drones.

关键词： Deep learning image segmentation Accuracy Signal processing algorithms Clustering algorithms Cleaning Stability analysis Maintenance Power generation Surface treatment

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Deep Learning-Based Depth Map Defect Removal for Industrial Applications 10

Deep Learning-Based Depth Map Defect Removal for Industrial ...

引用

conference on Optical Metrology and Inspection for Industrial Applications X

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Khamidullin, I. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510667877;9781510667884

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

关键词： depth map defect detection image reconstruction manufacturing deep learning U-Net

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Recognition and Analysis of Glass Subsurface Defects by Integrating Machine Vision and image processing Technology

Recognition and Analysis of Glass Subsurface Defects by Inte...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Chen Lin xiong Ling Guangming Luo Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies.

关键词： Visualization image recognition Machine vision Metals Glass Optical imaging Classification algorithms Manufacturing Surface treatment Optical devices

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Indoor Visible Light Positioning Method Based on Sparse Fingerprints Using Extended Min-Max and WKNN algorithms

Indoor Visible Light Positioning Method Based on Sparse Fing...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Xunuo Li Zhonghua Liang Zhenyu Wang Hui Li School of Information Engineering Chang’an University Xi’an China

Indoor visible light positioning (VLP) systems based on received signal strength (RSS) fingerprint can provide high-precision indoor positioning with low complexity. However, laborious offline field measurements are required to collect RSS fingerprints, and the subsequent fingerprint matching process also needs a large computation overhead. In order to address the above problems, in this paper, we propose an indoor VLP method based on sparse fingerprints using extended Min-Max (E-Min-Max) and weighted k-nearest neighbor (WKNN) algorithms. In the proposed VLP method, bicubic interpolation is used during the offline phase to directly estimate the RSS values of virtual reference nodes. In the online phase, a coarse estimate of the position of target node (TN) is firstly obtained by using the E-Min-Max algorithm. Then, based on the coarse estimate of the TN’s position, the WKNN algorithm is used to determine the TN’s accurate position. Simulation results show that compared to the traditional WKNN algorithm, the proposed VLP method has much higher fingerprint matching efficiency while ensuring high positioning accuracy.

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All-sky Auroral image Super-Resolution via Spatial-temporal Nonlocal Regularized Low-rank

All-sky Auroral Image Super-Resolution via Spatial-temporal ...

引用

Natural Language processing (ICNLP), International conference on

作者： Mingyue Li Qian Wang Fan Hu Rui Yang School of Telecommunication and Information Engineering Xi’an University of Posts & Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Aurora phenomenon is a projection of solar-terrestrial interaction onto the polar upper atmosphere. Systematically observing the auroral morphology and its evolution helps humans study solar activity and space weather. To enhance more details of auroral morphology and motion, which is essential for cross-site collaborative studies and identification of small-scale structures, we employ a super-resolution (SR) approach to improve the resolution of all-sky auroral images. Considering that similar auroral structures and motion patterns may appear in both neighboring frames and the current image, we introduce spatio-temporal continuity into the image super-resolution reconstruction algorithm and explore the nonlocal similarity between neighboring frames to obtain complete and real high-resolution auroral structures. An adaptive all-sky field mapping strategy is proposed to change its neighborhood relationship to obtain a low-rank relationship in all-sky field. A low-rank based super-resolution approach is applied to global images containing spatial-temporal continuity. We experimentally show that the proposed method improves the details of reconstructed high-resolution images and outperforms other methods for ASI auroral images.

关键词： Ion radiation effects Superresolution Morphology Collaboration Atmosphere Reconstruction algorithms Magnetosphere image reconstruction Testing Meteorology

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Underwater image enhancement and detection based on convolutional DCP and YOLOv5 41

Underwater image enhancement and detection based on convolut...

引用

第41届中国控制会议

作者： Liu Guodong Feng Lihui Lu Jihua Yan Lei School of Optics and Photonics Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing Institute of Technology Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Underwater image restoration is conducive to better underwater resource detection and information effective ***,the light in the complex water body diffusely reflective and the selection absorption of different band light results in blurring and color distortion of underwater ***,we propose a convolutional Dark Channel Prior(DCP)underwater image recovery algorithm to enhance underwater *** can do the pre-processing work for the subsequent YOLOv5 object *** enhancement algorithm first performs Commission International Eclairage Lab(CIELAB)equalization of underwater images for color distortion ***,the underwater image formation parameters are estimated by the minimum convolution region ***,Contrast Limited Adaptive Histogram Equalization(CLAHE) is performed to obtain an enhanced underwater ***,the enhanced underwater image is input to the YOLOv5 model for *** results show that the proposed method outperforms state-of-art algorithms in terms of image recovery effect,evaluation quality and detection accuracy.

关键词： image classification color equalization peak signal-to-noise ratio underwater color image quality evaluation

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A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip

A Compact Real-Time Thermal Imaging System Based on Heteroge...

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IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Hyun Woo Oh Cheol-Ho Choi Jeong Woo Cha Hyunmin Choi Jung-Ho Shin Joon Hwan Han Infra Technology R&D Center Hanwha Systems Seongnam Republic of Korea

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640×480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280×1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Single instruction multiple data Noise Imaging systems architecture Organizations Real-time systems System-on-chip VLIW image enhancement Videos

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Study on the Iterative Estimation and Compensation Algorithm for IQ Imbalance in Coherent Optical Communication systems

Study on the Iterative Estimation and Compensation Algorithm...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Changjun Huang Fengtao He Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Coherent optical communication technology is a key technology for building high-speed optical fibre communication networks, while the amplitude and phase imbalance between the in-phase and quadrature (IQ) signals at the receiving end will lead to the degradation of the received signal quality and affect the performance of the communication system. In this thesis, a narrowband zero-intermediate frequency (ZIF) receiver IQ imbalance estimation and compensation algorithm is proposed with the research objective of designing IQ imbalance estimation and compensation algorithms for the 16 quadrature amplitude modulation (16QAM) signal in coherent optical communication systems that can be implemented in hardware circuits. The algorithm is based on the Gram-Schmidt orthogonalization procedure (GSOP) for approximate estimation of the IQ imbalance parameters and improves the estimation accuracy of the IQ imbalance parameters by means of iterative optimization. The IQ imbalance signal is effectively compensated by the inverse matrix of the IQ mismatch parameter matrix. Based on the estimation error of the IQ imbalance parameters, the bit error rate (BER) of the signal transmission at different signal to noise ratio (SNR), and the simulation results of the 16QAM constellation diagram before and after compensation, it is concluded that the iterative optimized algorithm based on GSOP reduces the complexity of hardware implementation and improves the efficiency and accuracy of the IQ imbalance parameters estimation effectively. The findings of this study hold practical engineering significance for estimating and calibrating IQ imbalance in coherent optical communication systems using high-order modulated signals through digital signal processing techniques.

关键词： Accuracy Circuits Bit error rate Estimation Signal processing algorithms Approximation algorithms Optical fiber communication Iterative algorithms Hardware Signal to noise ratio

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