Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for...Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.展开更多
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. ...This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. It not only has higher speed but also can extract the edge better. Finally, an example of 2D image is given to demonstrate the usefulness and advantages of the algorithm.展开更多
To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed b...To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.展开更多
In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detec...In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.展开更多
Wavelet transform is an ideal way for edge detection because of its multi-scale property, localization both in time and frequency domain, sensitivity to the abrupt change of signals, and so on. An improved algorithm f...Wavelet transform is an ideal way for edge detection because of its multi-scale property, localization both in time and frequency domain, sensitivity to the abrupt change of signals, and so on. An improved algorithm for image edge detection based on Lifting Scheme is proposed in this paper. The simulation results show that our improved method can better reflect edge information of images.展开更多
This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selec...This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.展开更多
Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this wo...Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.展开更多
The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is cruci...The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.This work demonstrates an intelligent,lightweight,and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing.The miniaturized system comprises a binocular meta-lens,a 532 nm filter,and a CMOS sensor.For disparity computation,we propose a stereo-matching neural network with a novel H-Module.The H-Module incorporates an attention mechanism into the Siamese network.The symmetric architecture,with cross-pixel interaction and cross-view interaction,enables a more comprehensive analysis of contextual information in stereo images.Based on spatial intensity discontinuity,the edge enhancement eliminates illposed regions in the image where ambiguous depth predictions may occur due to a lack of texture.With the assistance of deep learning,our edge-enhanced system provides prompt responses in less than 0.15 seconds.This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling,machine vision,autonomous driving,and robotics development.展开更多
Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their us...Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.展开更多
This research presents an algorithm for face detection based on color images using three main components： skin color characteristics, hair color characteristics, and a decision structure which converts the obtained i...This research presents an algorithm for face detection based on color images using three main components： skin color characteristics, hair color characteristics, and a decision structure which converts the obtained information from skin and hair regions to labels for identifying the object dependencies and rejecting many of the incorrect decisions. Here we use face color characteristics that have a good resistance against the face rotations and expressions. This algorithm is also capable of being combined with other methods of face recognition in each stage to improve the detection.展开更多
Given image sequences of closely packed particles, the underlying aim is to estimate diameters without explicit segmentation. In a way, this is similar to the task of counting objects without directly counting them. S...Given image sequences of closely packed particles, the underlying aim is to estimate diameters without explicit segmentation. In a way, this is similar to the task of counting objects without directly counting them. Such calculations may, for example, be useful）Cast estimation of particle size in different application areas. The topic is that of estimating average size （=average diameter） of packed particles, from formulas involving edge density, and the edges from moment-based thresholding are used. An average shape factor is involved in the calculations, obtained for some frames from crude partial segmentation. Measurement results from about 80frames have been analyzed.展开更多
An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle...An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparendy faster than Hough transform.展开更多
In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to ...In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.展开更多
An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. Af...An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.展开更多
Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly fav...Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.展开更多
As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part i...As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part is studied and a precise algorithm of calculating the deviation angle of four types microphones is proposed,based on the feature extraction and visual detection.Pretreatment is performed to achieve the real-time microphone image.Canny edge detection and typical feature extraction are used to distinguish the four types of microphones,categorizing them as type M1 and type M2.And Hough transformation is used to extract the image features of microphone.Therefore,the deviation angle between the posture of microphone and the ideal posture in 2Dplane can be achieved.Depending on the angle,the system drives the motor to adjust posture of the microphone.The final purpose is to realize the high efficiency welding of four different types of microphones.展开更多
This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closin...This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closing operation based on Sobel Edge Detection Operation and the (μ-kσ) thresholding technique to detect obstacles to soften the various lighting and ground floor effects. Both the morphology method and thresholding technique are computationally simple. The processing speed of the algorithm is fast enough to avoid some active obstacles. In addition, this approach takes into account the history obstacle effects on the current state. Fuzzy logic is used to control the behaviors of AMR as it navigates in the environment. All behaviors run concurrently and generate motor response solely based on vision perception. A priority based on subsumption coordinator selects the most appropriate response to direct the AMR away from obstacles. Validation of the proposed approach is done on a Pioneer 1 mobile robot.展开更多
In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. Thi...In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. This realized the rapid extraction of the anatologic shape features in across compression and make possible the wood species could be characterized quantitatively.展开更多
基金financially supported by the National Council for Scientific and Technological Development(CNPq,Brazil),Swedish-Brazilian Research and Innovation Centre(CISB),and Saab AB under Grant No.CNPq:200053/2022-1the National Council for Scientific and Technological Development(CNPq,Brazil)under Grants No.CNPq:312924/2017-8 and No.CNPq:314660/2020-8.
文摘Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
文摘This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. It not only has higher speed but also can extract the edge better. Finally, an example of 2D image is given to demonstrate the usefulness and advantages of the algorithm.
基金Supported by the CRSRI Open Research Program(CKWV2013225/KY)the Open Project Foundation of Key Laboratory of the Yellow River Sediment of Ministry of Water Resource(2014006)+2 种基金the Open Project Foundation of Key Lab of Port,Waterway and Sedimentation Engineering of the Ministry of Transportthe State Key Lab of Urban Water Resource and Environment(HIT)(ES201409)the Priority Academic Program Development of Jiangsu Higher Education Institution
文摘To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.
文摘In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.
文摘Wavelet transform is an ideal way for edge detection because of its multi-scale property, localization both in time and frequency domain, sensitivity to the abrupt change of signals, and so on. An improved algorithm for image edge detection based on Lifting Scheme is proposed in this paper. The simulation results show that our improved method can better reflect edge information of images.
基金supported in part by the Science and Technology Development Fund,Macao SAR FDCT/085/2018/A2the Guangdong Basic and Applied Basic Research Foundation(2019A1515111185)。
文摘This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.
基金supported by the University of Tunis El Manar and National Engineering School of Tunis
文摘Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.
基金supports from the Research Grants Council of the Hong Kong Special Administrative Region,China[Project No.C5031-22GCityU11310522+3 种基金CityU11300123]the Department of Science and Technology of Guangdong Province[Project No.2020B1515120073]City University of Hong Kong[Project No.9610628]JST CREST(Grant No.JPMJCR1904).
文摘The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.This work demonstrates an intelligent,lightweight,and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing.The miniaturized system comprises a binocular meta-lens,a 532 nm filter,and a CMOS sensor.For disparity computation,we propose a stereo-matching neural network with a novel H-Module.The H-Module incorporates an attention mechanism into the Siamese network.The symmetric architecture,with cross-pixel interaction and cross-view interaction,enables a more comprehensive analysis of contextual information in stereo images.Based on spatial intensity discontinuity,the edge enhancement eliminates illposed regions in the image where ambiguous depth predictions may occur due to a lack of texture.With the assistance of deep learning,our edge-enhanced system provides prompt responses in less than 0.15 seconds.This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling,machine vision,autonomous driving,and robotics development.
基金financially supported by the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCOthe National Research Foundation (NRF) grants (RS-2024-00462912, RS-2024-00416272, RS-2024-00337012, RS-2024-00408446) funded by the Ministry of Science and ICT (MSIT) of the Korean government+2 种基金the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean governmentthe Soseon Science fellowship funded by Community Chest of Koreathe NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government。
文摘Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.
文摘This research presents an algorithm for face detection based on color images using three main components： skin color characteristics, hair color characteristics, and a decision structure which converts the obtained information from skin and hair regions to labels for identifying the object dependencies and rejecting many of the incorrect decisions. Here we use face color characteristics that have a good resistance against the face rotations and expressions. This algorithm is also capable of being combined with other methods of face recognition in each stage to improve the detection.
文摘Given image sequences of closely packed particles, the underlying aim is to estimate diameters without explicit segmentation. In a way, this is similar to the task of counting objects without directly counting them. Such calculations may, for example, be useful）Cast estimation of particle size in different application areas. The topic is that of estimating average size （=average diameter） of packed particles, from formulas involving edge density, and the edges from moment-based thresholding are used. An average shape factor is involved in the calculations, obtained for some frames from crude partial segmentation. Measurement results from about 80frames have been analyzed.
文摘An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparendy faster than Hough transform.
基金Foundation project： This paper was supported by National Natural Science Foundation of China （No. 30371126）.
文摘In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.
文摘An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61332003 and 61303068)the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3024)
文摘Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.
基金supported by the Project of Youth Fund of the National Natural Science Foundation (No. 61203208)the National Natural Science Foundation of China(No.61327802)the Specialized Research Fund for the Doctoral Program of Higher Education (No.2013320111 0009)
文摘As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part is studied and a precise algorithm of calculating the deviation angle of four types microphones is proposed,based on the feature extraction and visual detection.Pretreatment is performed to achieve the real-time microphone image.Canny edge detection and typical feature extraction are used to distinguish the four types of microphones,categorizing them as type M1 and type M2.And Hough transformation is used to extract the image features of microphone.Therefore,the deviation angle between the posture of microphone and the ideal posture in 2Dplane can be achieved.Depending on the angle,the system drives the motor to adjust posture of the microphone.The final purpose is to realize the high efficiency welding of four different types of microphones.
基金TheNationalNaturalSienceFoundationofChina (No .6 2 385 2 )
文摘This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closing operation based on Sobel Edge Detection Operation and the (μ-kσ) thresholding technique to detect obstacles to soften the various lighting and ground floor effects. Both the morphology method and thresholding technique are computationally simple. The processing speed of the algorithm is fast enough to avoid some active obstacles. In addition, this approach takes into account the history obstacle effects on the current state. Fuzzy logic is used to control the behaviors of AMR as it navigates in the environment. All behaviors run concurrently and generate motor response solely based on vision perception. A priority based on subsumption coordinator selects the most appropriate response to direct the AMR away from obstacles. Validation of the proposed approach is done on a Pioneer 1 mobile robot.
文摘In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. This realized the rapid extraction of the anatologic shape features in across compression and make possible the wood species could be characterized quantitatively.
