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检索条件"主题词=Detection and tracking algorithms"

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Phase defect inspection of 130 nm node phase shift masks using a simultaneous transmitted and reflected light pattern inspection algorithm

Phase defect inspection of 130 nm node phase shift masks usi...

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17th European Conference on Mask Technology for Integrated Circuits and Microcomponents

作者： Zurbrick, L Emery, D Rudzinski, M Wihl, M Prudhomme, M Crell, C Griesinger, UA Vorwerk, M Hennig, M KLA Tencor Corp San Jose CA USA

ISBN: (纸本)0819440396

Phase shifting mask technology will be necessary to produce integrated circuits at the 130 nm node using KrF wavelength steppers. in order to successfully accomplish this goal, it is necessary to detect and repair phase shifting defects that may occur in the manufacture of these reticles. An inspection algorithm has been developed to improve the phase shift defect detection rate of an UV reticle inspection system and is based upon the simultaneous use of the transmitted and reflected light signals. This paper describes the phase defect sensitivity improvement over transmitted light only pattern inspection results and simultaneous transmitted and reflected light based contamination inspection results.

关键词： Inspection Phase shifts Defect detection Reticles Algorithm development Photomasks detection and tracking algorithms Defect inspection Contamination Signal detection

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Research on target detection algorithm for foggy weather based on YOLO-FOG 7

Research on target detection algorithm for foggy weather bas...

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7th International Conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2024

作者： Gu, Fang Zhu, Kai School of Mechanical Engineering Jiangsu University of Technology Jiangsu Changzhou213001 China School of Automobile and Traffic Engineering Jiangsu University of Technology Jiangsu Changzhou213001 China

ISBN: (纸本)9781510681866

Images acquired under foggy conditions have blurred lighting and low resolution and contrast, while the feature extraction of traditional target detection designs does not take into account the fog obscuring the target, affecting the recognition rate of targets such as vehicles and pedestrians. Based on this, this paper proposes YOLO-FOG. Based on Anchor-Free, the TSCODE module is inserted to further separate the feature encoding of the two tasks;MobileViT is introduced into the pyramid structure of the feature graph to construct the long-distance dependencies of the features and integrate the features. When evaluated using the RTTS dataset, the mAP values of Bicycle, Bus, Car, Motorbike, and Person with their five types of targets can reach 82.19%, 81.36%, 89.47%, 75.89%, and 84.01%, respectively, and meanwhile the simultaneous detection speed up to 65.52 fps, which proves that this paper's method combines both accuracy and speed, and has good application prospects. © 2024 SPIE.

关键词： Target detection detection and tracking algorithms Fiber optic gyroscopes Data modeling Feature extraction Classification systems Head Performance modeling Image classification Transformers

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Robot-state detection for visual navigation using a neural network approach

Robot-state detection for visual navigation using a neural n...

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Conference on Intelligent Robots and Computer Vision XVIII - algorithms, Techniques, and Active Vision

作者： D'Orazio, T Circirelli, G Distante, A CNR Ist Elaboraz Segnali & Immagini I-70126 Bari Italy

ISBN: (纸本)0819434302

In this paper we describe how a robot detects its state with respect to a goal using only visual information and consequently how it learns to reach that goal navigating in a free environment. The state detection is carried out using a feed-forward neural network, with several multiple input and output units, trained using the quickprop method that is an optimized variant of the back-propagation algorithm. The color images captured by the onboard camera of the robot, are color coded and pre-processed to construct a robust set of inputs to the net, taking account of the trade-off between the dimension of the input set and the lost of information in the image. The simple goal-reaching behavior, finally, is learned using a reinforcement learning algorithm with which the robot associates a proper action to each detected state. To speed up this learning phase an initial state-action mapping is learned in simulation. Starting from this basic knowledge, the real robot will continue to learn the optimal actions for reaching the goal since new unexplored situations can occur in the real environment. The results obtained experimenting this approach on the real robot Nomad200 are described in the paper.

关键词： Neural networks Image processing Information visualization Visualization detection and tracking algorithms Evolutionary algorithms Associative arrays Onboard cameras Algorithm development Environmental sensing

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High-speed and high-maneuvering target detection method based on TRT and special GRFT 2

High-speed and high-maneuvering target detection method base...

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2nd International Conference on Optics and Communication Technology, ICOCT 2022

作者： Wang, Yang Ma, Qiankuo Zhang, Xiaokuan Xu, Jiahua Air and Missile Defense College Air Force Engineering University Xi’an China

ISBN: (纸本)9781510660533

For High-speed and high-maneuvering target detection, a detection method based on TRT and special GRFT is proposed in this paper. Firstly, the range migration and Doppler migration caused by target velocity and jerk are corrected by TRT. Secondly, estimation of target acceleration can be realized by special GRFT. Then, the estimated acceleration is used to construct a compensation function to correct the range migration and Doppler migration caused by acceleration. Finally, we can achieve coherent accumulation Simulation results demonstrate the effectiveness of the proposed method. © 2022 SPIE.

关键词： Doppler effect Target detection detection and tracking algorithms Radar Computer simulations Fourier transforms

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Multiscale scheme for image magnification

Multiscale scheme for image magnification

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Conference on Image and Video Communications and Processing 2000

作者： Nicolier, F Laligant, O Truchetet, F IUT Creusot F-71200 Le Creusot France

ISBN: (纸本)0819435929

Using the wavelet transform (WT), a given signal is decomposed into a succession of embedded approximations and detail coefficients. The observation of the details shows that similarities can be noticed across scales, in particular for the transitions (edges in an image). A wavelet-based magnification that both increases the resolution of an image and adds high-frequency information is proposed in this paper. From a non-subsampled WT, the zero-crossings of the details coefficients provide a consistent representation. From these coefficients, a prediction of high-frequency coefficients is possible via the computation of local Liptschitz exponents but needs an interpolation due to the constancy of the number of details coefficients. The proposed magnification is based on the decimated Mallat's algorithm. As this transformation is not shift-invariant, the local laws cannot be computed. The prediction is realized via the learning of representative edge signatures. A multiscale database is therefore constructed from the edge's zero-crossings. The magnification quality is evaluated by application on synthetic and noisy images.

关键词： Wavelets Reconstruction algorithms Databases Image quality Wavelet transforms detection and tracking algorithms Fast wavelet transforms Image processing Saturn Image resolution

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Stereo Matching by optimization using weak environmental constraints

Stereo Matching by optimization using weak environmental con...

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Conference on Intelligent Robots and Computer Vision XV: algorithms, Techniques, Active Vision, and Materials Handling

作者： Branca, A Stella, E Attolico, G Distante, A Istituto Elaborazione Segnali ed Immagini/CNR (Italy)

ISBN: (纸本)0819423068

Oue goal is to mar ch primitives of a pair of images, thereby solving the correspondence problem, in order to estimate depths of 3D scene points from the relative distance (disparity) between matched features. We propose a feature-based approach to solve the correspondence problem by minimizing an appropriate energy function where constraints on radiometric similarity and projective geometric invariance of coplanar points are defined. The method can be seen as a correlation based approach which take into account the projective invariance of coplanar points in computing the optimal matches.

关键词： detection and tracking algorithms Feature extraction 3D image processing Error analysis Direct methods Image segmentation Object recognition Optimization (mathematics) 3D vision Machine vision

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Field weed classification based on modified LBP and local sparse representation

Field weed classification based on modified LBP and local sp...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Yang, Huan College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Accurately identifying field weeds is crucial for selecting appropriate agricultural machinery and herbicides. This paper focuses on nine common weed species during the seedling stage in natural backgrounds of field vegetables, proposing a novel weed foreground segmentation method based on image processing and a new weed classification model using sparse representation classification (K_SRC). Initially, K-means clustering and threshold segmentation are applied to the a component of Lab color space to obtain binary weed images, followed by area filtering to remove interference. The binary weed image is then masked with the original image to extract clean weed images. Color features and mLBP texture features are extracted from segmented weed images to form a training sample set. KNN computes a local dictionary, and sparse coefficients are calculated using the OMP algorithm. Experimental results demonstrate a classification accuracy of 95.22% compared to four other algorithms. Additionally, this research provides new insights into the application of sparse representation in agriculture. © 2024 SPIE.

关键词： Image segmentation Education and training Feature extraction Image classification detection and tracking algorithms Binary data Image processing algorithms and systems Reconstruction algorithms Image processing

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Research on target detection algorithm in autonomous driving scenarios based on improved YOLOv5

Research on target detection algorithm in autonomous driving...

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International Conference on Computer Vision, Robotics, and Automation Engineering, CRAE 2024

作者： Xiang, Zhao Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9781510682283

With the rapid evolution of science and technology, the imperative for autonomous driving has become ever more pressing. In the domain of self-driving technology, the precise and efficient identification of targets within dynamic driving environments remains a paramount challenge. This paper takes on these challenges directly by introducing a pioneering target detection methodology, building upon an enhanced iteration of YOLOv5. Initially, by substituting FasterNet for the original backbone network, the model leverages its partial convolution features to significantly enhance computational efficiency and inference speed. Additionally, the integration of the SimAM attention mechanism dynamically adjusts the significance of various regions within the feature map, empowering the model to prioritize essential image information and refine detection accuracy. To further bolster the model's accuracy in detecting smaller targets, a dedicated module for small target detection is introduced, meticulously extracting feature map details. Lastly, the incorporation of WIoU as the new loss function mitigates the influence of geometric factors, thereby bolstering the model's robustness. Experimental findings demonstrate that the enhanced algorithm elevates mAP and FPS by 2.9% and 2.6 frames per second, respectively, compared to the original model, effectively addressing the challenges of detection speed and accuracy within autonomous driving scenarios. © 2024 SPIE.

关键词： Autonomous driving Target detection detection and tracking algorithms Small targets Performance modeling Autoregressive models Feature extraction Neurons

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New quasistatic magnetic and electric field imaging arrays and algorithms for object detection, identification, and discrimination

New quasistatic magnetic and electric field imaging arrays a...

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detection and Remediation Technologies for Mines and Minelike Targets IV Conference

作者： Goldfine, N Schlicker, D Washabaugh, A Clark, D Zahn, M JENTEK Sensors Inc Watertown MA 02472 USA

ISBN: (纸本)0819431842

Unlike radar-based imaging technologies (i.e., ground penetrating radar, GPR) that use electromagnetic waves (which have electric and magnetic fields governed by the "wave equation" or full Maxwell's equation), quasistatic imaging technologies operate at lower frequencies where electric and magnetic fields are decoupled. Magnetoquasistatic (MQS) devices, such as metal detectors, that impose magnetic fields satisfy the diffusion equation in conducting media and Laplace's equation in air or poorly conducting soils. Electroquasistatic (EQS) devices satisfy Laplace's equation. In Laplacian or diffusion decay, the amplitude of the magnetic and electric fields decay exponentially with distance from the drive windings or electrode. For quasistatic sensors, objects are detected and imaged through perturbations to the applied magnetic or electric fields that change the mutual transimpedances or transadmittances at the sensor terminals, rather than through time delays of reflected electromagnetic waves as in GPR. Limitations of conventional MQS sensors (metal detectors) include (1) limited depth of sensitivity, (2) lack of accurate quantitative modeling which prevents quantitative image formation, and (3) inability to discriminate shape, depth, or material type. Conventional metal detectors can often detect all relevant metal objects but can generally not discriminate between landmines or unexploded ordnance and harmless clutter. Advances in inductive methods combining spatial and spectral information have began to offer discrimination for relatively large metal objects. However, no substantial progress has been made to differentiate low-metal mines ham small metal clutter. This paper introduces three navel imaging technologies that address these limitations (1) an MQS sensor called the Meandering Winding Magnetometer (MWMTM)-Array for metallic objects, (2) a new EQS-Array that can image both nonmetal and metal objects, and (3) a hybrid MQS/EQS method for low-metal mines. Alg

关键词： Sensors Magnetism Optical spheres Metals Land mines Magnetic sensors Image resolution detection and tracking algorithms Image sensors Patents

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Quadcopter following controller design and MATLAB/Simulink implementation 9

Quadcopter following controller design and MATLAB/Simulink i...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Cui, Zhiyuan Li, Yuan School of Electronic Information Engineering Shanghai Dianji University Shanghai201306 China

ISBN: (纸本)9781510672802

Target tracking in the field of quadcopters is a highly challenging and intricate research area that encompasses control algorithms, sensor data fusion, and dynamics modeling. It necessitates a comprehensive approach to ensure the quadcopter's motion trajectory is stable. This stability allows the quadcopter to carefully navigate along a predetermined route, ultimately facilitating effective target tracking. Consequently, this paper focuses on analyzing the motion principles and dynamics model of quadcopters. MATLAB is utilized to simulate the model. In particular, a cascade PID control algorithm is designed, based on the fixed height mode, to actively stabilize the quadcopter's movement. To confirm the efficacy of the proposed target following controller, a simulation model is constructed using Simulink. © 2024 SPIE.

关键词： Device simulation Control systems Design and modelling detection and tracking algorithms Modeling Motion models Simulink MATLAB Motion analysis Sensors

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