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Spatial and Temporal Evolution of Wetlands in the Yellow River Delta Based on optical flow algorithm

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2025年第1期21卷 102-113页

作者： Liu, Chao Mu, Ruolan Wang, Chuanlong Yuan, Xiuhe Miao, Sheng Qingdao Univ Technol Sch Environm & Municipal Engn Qingdao Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China

The Yellow River Delta (YRD) wetlands are the largest coastal wetlands in China, and it serves to control soil erosion, nourish the climate and protect biodiversity. At present, due to climate change and human activities, the wetlands of the YRD are facing ecological and environmental problems such as species invasion, vegetation degradation, and biodiversity reduction. In order to study the evolution of wetland landscape types more intuitively, this paper proposed a semantic segmentation network based on the encoder and decoder structure of ResNet-18. Then, the wetland landscapes in the YRD were classified into five types by combining the Landsat series of remote sensing images. In addition, this paper used the optical flow algorithm to visualize the identification results, which can represent the evolution pattern of wetland landscape types in different years. The results of this paper have an important significance for the subsequent development planning and protection in the YRD.

关键词： Identification of Wetland Landscape Types optical flow algorithm ResNet Spatial and Temporal Evolution

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Using optical flow algorithm based on dynamic illumination mode to examine defects on highly reflective turbine blade surface

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IET IMAGE PROCESSING 2022年第14期16卷 3988-4010页

作者： Shang, Xuan Song, Wenyan Chen, Zhen Zhang, Congxuan Northwestern Polytech Univ Sch Power & Energy Xian Shaanxi Peoples R China Nanchang Hangkong Univ Sch Measuring & Opt Engn Nanchang 330063 Jiangxi Peoples R China

Notion of optical flow literally refers to the displacements of intensity patterns. In that sense, extracting interested information from 2D scene is analogy to modulation/demodulation in random signal processing. To address the limitations presented in computer vision based on static image, we propose a novel metal component defect detection method, specified as the instance of turbine blade surface detection, using optical flow *** start the specified pattern recognition in 2D presentation, we modulate the brightness constancy assumption equation as illumination varying model, by sampling the second image with function whose frequency was chosen according to the Nyquist sampling theorem, and a sinusoidal factor was introduced as an additive factor. This tunable channel based on 2D image transfers intensity features into optical modes. Then, we implement optical flow estimation on two sequential images. Experimental results reveal grayscale space shows completness in representing the optical modes of turbine blade with various kinds of surface characteristics. By modifying the index of information content, we propose quantitative index to evaluate the performance of our method. Evaluation reveals optical flow algorithm is qualified to examine defects on highly reflective turbine blade, and our method extends the application of optical flow.

关键词： blades brightness constancy assumption equation optical flow algorithm image sequences mechanical engineering computing static image illumination varying model feature extraction Nyquist sampling theorem sequential images pattern recognition turbines metal component defect detection method random signal processing image motion analysis computer vision optical flow estimation highly reflective turbine blade surface turbine blade surface detection inspection dynamic illumination mode optical modes

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Improved method on image stitching based on optical flow algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT 2020年 12卷 1-17页

作者： Lee, Hyunchul Lee, Sungmin Choi, Okkyung AUROS Technol 15-23 Dongtansandan 6 Gil Hwaseong Si Gyeonggi Do South Korea Ajou Univ Grad Sch Dept Knowledge Informat Engn Suwon South Korea Myongji Univ Bangmok Coll Gen Educ 116 Myongji Ro Yongin 17058 South Korea

With the rapid development of technologies based on virtual reality, image stitching is widely used in various fields such as broadcasting, games, education, and architecture. Image stitching is a method for connecting multiple images to produce a high-resolution image and a wide field of view image. It is common for most of the stitching methods to find and match the feature in the image. However, these stitching methods have the disadvantage that they cannot create a perfect 360-degree panoramic image because the depth of the projected area varies depending on the position and direction between adjacent cameras. Therefore, we propose an advanced stitching method to improve the deviation due to the difference in the depth of each area using the pixel value of the input image after the feature-based stitching. After the feature-based stitching method has been performed, the pixel values of overlapping areas in the image are calculated as an optical flow algorithm, then finely distorted, and then corrected to ensure that the image overlaps correctly. Through experiments, it was confirmed that the problem that was deviated from the feature-based stitching was solved. Besides, as a result of performance evaluation, it was proved that the proposed stitching method using an optical flow algorithm is capable of real-time and fast service.

关键词： Virtual Reality image stitching feature-based image stitching optical flow algorithm Farneback algorithm motion vector

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Real-time Sound Detection and Regeneration Based on optical flow algorithm of Laser Speckle Images 28

Real-time Sound Detection and Regeneration Based on Optical ...

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28th Wireless and optical Communications Conference (WOCC)

作者： Wu, Nan Haruyama, Shinichiro Keio Univ Grad Sch Syst Design & Management Yokohama Kanagawa Japan

ISBN: (纸本)9781728106601

Sound detection with optical means is an appealing research topic. Laser speckle is one effective method to detect the sound due to its sensitivity to tiny motion. The traditional laser-speckle sound recovery methods require a high-speed camera to record fast-moving speckle images and then recover the original sound signals from the motion information of the captured speckle images. In this manuscript, a laser microphone system is proposed to detect and regenerate the sound signal in real time. In the proposed system, only a small part of the imaging sensor is used to ensure a high sampling rate with a common industrial camera and reduce the computation time consumption. Meanwhile, optical flow algorithm is employed to obtain the motion information of captured speckle images and regenerate the sound. These two points allow us to capture images from the camera and regenerate sound in real time without storing any data into the computer, which greatly increases the speed of the system and achieves a microphone-like functions. Experiments are conducted to show that the proposed system can detect and regenerate the sound signal in real-time with a high quality.

关键词： Laser Microphone Sound Recovery Laser Speckle optical flow algorithm

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Determination of the Three-Dimensional Rate of Cancer Cell Rotation in an optically-Induced Electrokinetics Chip Using an optical flow algorithm

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MICROMACHINES 2018年第3期9卷 118-118页

作者： Zhao, Yuliang Jia, Dayu Sha, Xiaopeng Zhang, Guanglie Li, Wen Jung Northeastren Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn Kowloon 999077 Hong Kong Peoples R China Shenzhen Acad Robot Shenzhen 518000 Peoples R China

Our group has reported that Melan-A cells and lymphocytes undergo self-rotation in a homogeneous AC electric field, and found that the rotation velocity of these cells is a key indicator to characterize their physical properties. However, the determination of the rotation properties of a cell by human eyes is both gruesome and time consuming, and not always accurate. In this paper, a method is presented to more accurately determine the 3D cell rotation velocity and axis from a 2D image sequence captured by a single camera. Using the optical flow method, we obtained the 2D motion field data from the image sequence and back-project it onto a 3D sphere model, and then the rotation axis and velocity of the cell were calculated. After testing the algorithm on animated image sequences, experiments were also performed on image sequences of real rotating cells. All of these results indicate that this method is accurate, practical, and useful. Furthermore, the method presented there can also be used to determine the 3D rotation velocity of other types of spherical objects that are commonly used in microfluidic applications, such as beads and microparticles.

关键词： cell self-rotation single cell rotation optically induced electrokinetics optical flow algorithm

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Moving Object Tracking Method Based on Improved Lucas-Kanade Sparse optical flow algorithm

Moving Object Tracking Method Based on Improved Lucas-Kanade...

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International Smart Cities Conference (ISC2)

作者： Li Dan Jiang Dai-hong Bao Rong Sun Jin-ping Zhao Wen-jing Wang Chao Xuzhou Inst Technol Machine Vis Applicat Engn & Technol Res Ctr Xuzho Key Lab Intelligent Ind Control Technol Jiangsu P Dept Informat & Elect Engn Xuzhou Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538625248

Improved the traditional L-K algorithm by lifting the wavelet multi-resolution algorithm, and the tracking speed of system was greatly enhanced while combined with SURF matching algorithm. On the basis of detecting feature points, reduced the probability of the exterior points. Tracking local feature points by multi resolution wavelet Pyramid optical flow algorithm solved the problems of object deformation, high speed, fog and haze, uneven illumination, partial occlusion in complex environment. The new method can improve the anti noise ability and improve the efficiency and accuracy of the algorithm. In addition, an adaptive template updating strategy is proposed to avoid tracking failures due to long time tracking errors.

关键词： optical flow algorithm feature extraction multi-resolution target tracking

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optical flow-based velocimetry algorithm for schlieren images of flame plume

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Journal of Tsinghua University (Science and Technology) 2025年第6期65卷 1153-1160页

作者： Shenlin YANG Lei ZHAO Heqing WANG Manhou LI School of Civil Engineering Hefei University of Technology Hefei 230009 China Anhui International Joint Research Center on Hydrogen Safety Hefei 230009 China

Objective flow velocity measurement methods for weak flame plumes face several challenges due to the complex dynamics involved. Flame plumes, driven by buoyancy forces, are inherently turbulent, with the plume motion accompanied by the entrainment of the surrounding air. The boundary between the plume and the environment continuously evolves in space, making it difficult to capture the plume's true flow characteristics. Past plume velocity measurement methods rely on intrusive methods, using tools such as Pitot tubes, smoke probes, and hot-wire anemometers. These methods disrupt the plume flow field and cannot accurately reflect the undisturbed temporal and spatial characteristics of the entire plume, thereby limiting their applicability for a detailed analysis of weak flame plumes. Methods To address these challenges, we employed the schlieren imaging technique to visualize flame plumes. This nonintrusive visualization technique allowed the capture of the flow field induced by buoyancy forces at a high resolution. Industrial cameras were used to record the ignition process and flame plume dynamics at varying heights and oil pan diameters. By analyzing the schlieren images, we aimed to overcome the limitations of traditional measurement methods. In this study, we derived a simplified two-dimensional Navier-Stokes equation to develop an optimized optical flow (OF) algorithm tailored for velocity measurements in flow fields. The proposed algorithm was applied to the schlieren images of flame plumes, showing significant improvements over conventional OF methods. Results The key advancements of the optimized algorithm are as follows. (1) Enhanced sensitivity and precision: The optimized algorithm produces smoother displacement fields and more uniform vorticity fields. This enables the detection of finer vortex structures that are often overlooked by conventional OF methods. By improving the resolution and accuracy of the calculated flow field, the algorithm provides a m

关键词： optical flow algorithm flame plume flow velocity measurement schlieren particle image velocimetry (PIV)

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A modified optical flow algorithm based on bilateral-filter and multi-resolution analysis for PIV image processing

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flow MEASUREMENT AND INSTRUMENTATION 2014年 38卷 121-130页

作者： Shi, Baocheng Wei, Jinjia Pang, Mingjun Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China Changzhou Univ Sch Mech Engn Changzhou 213016 Peoples R China

Particle Image Velocimetry (PIV) technology is an efficient and powerful testing method to investigate the characteristics of flow field. The topic of PIV post-processing techniques has roused researchers' wide concern for its great influence on the success of flow field measurement. The traditional correlation algorithms have their innate defects. In the present study, a modified optical flow algorithm is proposed to overcome these deficiencies based on bilateral-filter and multi-resolution analysis of Ply image processing. The algorithm is designed based on the principle of multilayer segments, in which the isotropic diffusion method is employed to calculate the low-resolution layer of the image and the nonlinear filtering method is used to process the high-resolution layer. This new algorithm can reduce image noise effectively and maintain the details of the image boundary. In addition, the design of nonlinear filter makes the optical flow equation simpler, and the optimal velocity mapping factor method needs less iteration and reduces the computational load. The algorithm is first tested on synthetic time-resolved channel flow images, and the computational results from the simulated particle images are found to be in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual up-channel flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： optical flow algorithm Post-processing PIV Multi-resolution analysis

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A Novel WasSAR Image Offset Information Estimation Method for 3-D Information Acquisition

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Li, Yishi Chen, Leping Song, Yongping Huang, Xiaotao An, Daoxiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

The wide angle staring synthetic aperture radar (WasSAR), as an emerging synthetic aperture radar (SAR) observation mode, enables long-duration, multiangular imaging of a scene, demonstrating remarkable advantages in 3-D information acquisition. A critical step in the process of 3-D information extraction lies in accurately determining the displacement information between subaperture images captured from adjacent azimuth angles. During the WasSAR imaging process, spatial targets exhibit positional discrepancies in imaging results obtained from different azimuth perspectives, making pixel-wise displacement estimation in SAR images highly challenging, especially in complex scenarios. In order to address this challenge, this study proposes an innovative displacement estimation method for WasSAR imagery tailored for 3-D information extraction. The proposed approach begins with brightness equalization preprocessing to harmonize the brightness distribution between two images, ensuring the accuracy of subsequent processing steps. This is followed by an initial estimation using block-based registration techniques based on the enhanced correlation coefficient (ECC). Finally, an improved optical flow algorithm is employed to achieve precise displacement estimation, significantly enhancing the accuracy and reliability of displacement information estimation between SAR images. A series of experiments were conducted using Ku-band mountain scene datasets acquired by the National University of Defense Technology (NUDT). The experimental results include a comprehensive comparative analysis with several existing displacement estimation methods, showcasing the superior performance of the proposed algorithm across multiple key performance metrics. The proposed algorithm's performance in 3-D information extraction applications was also evaluated, confirming its effectiveness and high practicality.

关键词： 3-D information acquisition enhanced correlation coefficient (ECC) algorithm offset estimation optical flow algorithm synthetic aperture radar (SAR) image processing wide angle staring synthetic aperture radar (WasSAR) 3-D information acquisition enhanced correlation coefficient (ECC) algorithm offset estimation optical flow algorithm synthetic aperture radar (SAR) image processing wide angle staring synthetic aperture radar (WasSAR)

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Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

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Computers, Materials & Continua 2023年第4期75卷 715-740页

作者： Tianliang Lu Yuxuan Bao Lanting Li People’s Public Security University of China Beijing100038China

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace *** address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and ***,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial ***,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are *** results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream *** iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.

关键词： Deepfake detection CapsNet optical flow algorithm temporal–spatial features

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