Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
To address the issue of increased electromagnetic vibration in permanent magnet(PM) motors for electric vehicles under flux-weakening speed extension operation, this paper proposes a low-vibration design method for PM...To address the issue of increased electromagnetic vibration in permanent magnet(PM) motors for electric vehicles under flux-weakening speed extension operation, this paper proposes a low-vibration design method for PM motors from the perspective of saliency ratio. First, by establishing a theoretical model for vibration analysis under flux-weakening operation, it reveals the vibration mechanism whereby high-order armature magnetomotive force(ARM-MMF) harmonics under fluxweakening operation leads to enhanced radial electromagnetic force(REF). Second, an in-depth investigation into the intrinsic relationship between saliency ratio and ARM-MMF is conducted. This study proposes a novel methodology to design key ARM-MMF harmonics that induce significant electromagnetic vibration from the perspective of the saliency ratio. An innovative rotor topology featuring elliptical-arc composite flux barriers is developed to implement this approach. Furthermore, and synergistic optimization is performed with the saliency ratio and torque as optimization objectives to balance vibration suppression and torque output performance. Finally, a prototype is manufactured and tested. Both theoretical and experimental analyses validated the effectiveness of the motor and the proposed design method.展开更多
In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal...In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.展开更多
Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of int...Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.展开更多
Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noti...Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.展开更多
Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is prop...Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.展开更多
Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due ...Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.展开更多
The detection range of underwater laser imaging technology achieves 4—6 times of detection range of conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea explora...The detection range of underwater laser imaging technology achieves 4—6 times of detection range of conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea exploration and robotic works. However, the special features in underwater laser images, such as speckle noise and non-uniform illumination, bring great difficulty for image segmentation. In this paper, a novel saliency motivated pulse coupled neural network(SM-PCNN) is proposed for underwater laser image segmentation. The pixel saliency is used as external stimulus of neurons. For improvement of convergence speed to optimal segmentation, a gradient descent method based on maximum two-dimensional Renyi entropy criterion is utilized to determine the dynamic threshold. On the basis of region contrast in each iteration step, the real object regions are effectively distinguished,and the robustness against speckle noise and non-uniform illumination is improved by region selection. The proposed method is compared with four other state-of-the-art methods which are watershed, fuzzy C-means, meanshift and normalized cut methods. Experimental results demonstrate the superiority of our proposed method to allow more accurate segmentation and higher robustness.展开更多
Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment.Most computational visual attention models are designed with inspirations from mammalian vi...Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment.Most computational visual attention models are designed with inspirations from mammalian visual systems.However,electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time.Therefore,the visual system of the avian species,especially the nuclei related to the visual attention mechanism,are investigated in this paper.Afterwards,a hierarchical visual attention model is proposed for saliency detection.The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy.The"winner-takeall"network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy.Comparison results verify that the proposed model,which can define the focus of attention accurately,outperforms several state-of-the-art models.This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways.The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.展开更多
Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual（SR） and phase spectrum of the Fourier transform（PFT） models are simple and fast ...Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual（SR） and phase spectrum of the Fourier transform（PFT） models are simple and fast saliency detection approaches based on two-dimensional Fourier transform without the prior knowledge. For seismic data, the geological structure of the underground rock formation changes more obviously in the time direction. Therefore, one-dimensional Fourier transform is more suitable for seismic saliency detection. Fractional Fourier transform（FrFT） is an improved algorithm for Fourier transform, therefore we propose the seismic SR and PFT models in one-dimensional FrF T domain to obtain more detailed saliency maps. These two models use the amplitude and phase information in FrFT domain to construct the corresponding saliency maps in spatial domain. By means of these two models, several saliency maps at different fractional orders can be obtained for seismic attribute analysis. These saliency maps can characterize the detailed features and highlight the object areas, which is more conducive to determine the location of reservoirs. The performance of the proposed method is assessed on both simulated and real seismic data. The results indicate that our method is effective and convenient for seismic attribute extraction with good noise immunity.展开更多
The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to ide...The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.展开更多
Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high ...Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81%correct rate and 0.78%false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.展开更多
Pests detecting is an important research subject in grain storage field.In the past decades,many edge detection methods have been applied to the edge detection of stored grain pests.Although some of them can realize t...Pests detecting is an important research subject in grain storage field.In the past decades,many edge detection methods have been applied to the edge detection of stored grain pests.Although some of them can realize the stored grain pests detecting,precision and robustness are not good enough.Spectral residual(SR)saliency edge detection defines the logarithmic spectrumof image as novelty part of the image information.The remaining spectrumis converted to the airspace to obtain edge detection results.SR algorithm is completely based on frequency domain processing.It not only can effectively simplify the target detection algorithm,but also can improve the effectiveness of target recognition.The experimental results show that the edge results of stored grain pests detected by SR method are effective and stable.展开更多
Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms hav...Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.展开更多
In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based...In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of work- pieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency （DM-TBS） is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method.展开更多
In order to better represent infrared target features under different environments, a saliency detection method based on region covariance and global feature is proposed. Firstly, the region covariance features on dif...In order to better represent infrared target features under different environments, a saliency detection method based on region covariance and global feature is proposed. Firstly, the region covariance features on different scale spaces and different image regions are extracted and transformed into sigma features,then combined with central position feature, the local salient map is generated. Next, a global salient map is generated by gray contrast and density estimation. Finally, the saliency detection result of infrared images is obtained by fusing the local and global salient maps. The experimental results show that the salient map of the proposed method has complete target features and obvious edges,and the proposed method is better than the state of art method both qualitatively and quantitatively.展开更多
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
基金supported in part by the Modern Agriculture Major Core Technology Innovation Project of Jiangsu Province under Grant CX(22)1005in part by the National Natural Science Foundation of China under Grant 52177046in part by the Project of Innovation of Postgraduate of Jiangsu Province under Grant KYCX25_4228。
文摘To address the issue of increased electromagnetic vibration in permanent magnet(PM) motors for electric vehicles under flux-weakening speed extension operation, this paper proposes a low-vibration design method for PM motors from the perspective of saliency ratio. First, by establishing a theoretical model for vibration analysis under flux-weakening operation, it reveals the vibration mechanism whereby high-order armature magnetomotive force(ARM-MMF) harmonics under fluxweakening operation leads to enhanced radial electromagnetic force(REF). Second, an in-depth investigation into the intrinsic relationship between saliency ratio and ARM-MMF is conducted. This study proposes a novel methodology to design key ARM-MMF harmonics that induce significant electromagnetic vibration from the perspective of the saliency ratio. An innovative rotor topology featuring elliptical-arc composite flux barriers is developed to implement this approach. Furthermore, and synergistic optimization is performed with the saliency ratio and torque as optimization objectives to balance vibration suppression and torque output performance. Finally, a prototype is manufactured and tested. Both theoretical and experimental analyses validated the effectiveness of the motor and the proposed design method.
基金co-supported by the National Natural Science Foundation of China (Nos.61005028,61175032,and 61101222)
文摘In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.
基金This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401+1 种基金in part,by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant Numbers SJCX21_0363in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.
基金supported by NSFC Joint Fund with Guangdong under Key Project(U1201258)National Natural Science foundation of China(61402261+3 种基金6130308861572286)the scientific research foundation of Shandong Province of Outstanding Young Scientist Award(BS2013DX048)Shandong Ji’nan Science and Technology Development Project(201202015)
文摘Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.
基金supported by the National Natural Science Foundation of China(61210012)
文摘Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.
基金the National High Technology Research and Development Program(863)of China(No.2011AA09A106)the National Natural Science Foundation of China(No.51009040)and the Fundamental Research Funds for Central Universities of China(No.HEUCF140113)
文摘The detection range of underwater laser imaging technology achieves 4—6 times of detection range of conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea exploration and robotic works. However, the special features in underwater laser images, such as speckle noise and non-uniform illumination, bring great difficulty for image segmentation. In this paper, a novel saliency motivated pulse coupled neural network(SM-PCNN) is proposed for underwater laser image segmentation. The pixel saliency is used as external stimulus of neurons. For improvement of convergence speed to optimal segmentation, a gradient descent method based on maximum two-dimensional Renyi entropy criterion is utilized to determine the dynamic threshold. On the basis of region contrast in each iteration step, the real object regions are effectively distinguished,and the robustness against speckle noise and non-uniform illumination is improved by region selection. The proposed method is compared with four other state-of-the-art methods which are watershed, fuzzy C-means, meanshift and normalized cut methods. Experimental results demonstrate the superiority of our proposed method to allow more accurate segmentation and higher robustness.
基金supported by Natural Science Foundation of China(61425008,61333004,61273054)
文摘Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment.Most computational visual attention models are designed with inspirations from mammalian visual systems.However,electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time.Therefore,the visual system of the avian species,especially the nuclei related to the visual attention mechanism,are investigated in this paper.Afterwards,a hierarchical visual attention model is proposed for saliency detection.The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy.The"winner-takeall"network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy.Comparison results verify that the proposed model,which can define the focus of attention accurately,outperforms several state-of-the-art models.This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways.The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.
基金supported by the National Natural Science Foundation of China (Nos.61571096,61775030,41274127,41301460,and 40874066)
文摘Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual（SR） and phase spectrum of the Fourier transform（PFT） models are simple and fast saliency detection approaches based on two-dimensional Fourier transform without the prior knowledge. For seismic data, the geological structure of the underground rock formation changes more obviously in the time direction. Therefore, one-dimensional Fourier transform is more suitable for seismic saliency detection. Fractional Fourier transform（FrFT） is an improved algorithm for Fourier transform, therefore we propose the seismic SR and PFT models in one-dimensional FrF T domain to obtain more detailed saliency maps. These two models use the amplitude and phase information in FrFT domain to construct the corresponding saliency maps in spatial domain. By means of these two models, several saliency maps at different fractional orders can be obtained for seismic attribute analysis. These saliency maps can characterize the detailed features and highlight the object areas, which is more conducive to determine the location of reservoirs. The performance of the proposed method is assessed on both simulated and real seismic data. The results indicate that our method is effective and convenient for seismic attribute extraction with good noise immunity.
基金Supported by the National Natural Science Foundation of China （50706006） and the Science and Technology Development Program of Jilin Province （20040513）.
文摘The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.
基金Supported by National Natural Science Foundation of China(Grant Nos.U1564201,61573171,61403172,51305167)China Postdoctoral Science Foundation(Grant Nos.2015T80511,2014M561592)+3 种基金Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20140555)Six Talent Peaks Project of Jiangsu Province,China(Grant Nos.2015-JXQC-012,2014-DZXX-040)Jiangsu Postdoctoral Science Foundation,China(Grant No.1402097C)Jiangsu University Scientific Research Foundation for Senior Professionals,China(Grant No.14JDG028)
文摘Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81%correct rate and 0.78%false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.
基金financially supported by National Natural Science Foundation of China(No.61871176)Key Scientific and Technological Project of Science and Technology Department of Henan Province(No.172102210030,182102110099)+2 种基金Key Scientific Research Project Program of Universities of Henan Province(No.18B520025)Open Fund of Key Laboratory of Grain Information Processing and Control(No.KFJJ-2018-102)supported by Collaborative Innovation Center of Grain Storage and Security of Henan Province
文摘Pests detecting is an important research subject in grain storage field.In the past decades,many edge detection methods have been applied to the edge detection of stored grain pests.Although some of them can realize the stored grain pests detecting,precision and robustness are not good enough.Spectral residual(SR)saliency edge detection defines the logarithmic spectrumof image as novelty part of the image information.The remaining spectrumis converted to the airspace to obtain edge detection results.SR algorithm is completely based on frequency domain processing.It not only can effectively simplify the target detection algorithm,but also can improve the effectiveness of target recognition.The experimental results show that the edge results of stored grain pests detected by SR method are effective and stable.
文摘Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.
基金supported by National Natural Science Foundation of China(Nos.61379097,91748131,61771471,U1613213 and 61627808)National Key Research and Development Plan of China(No.2017YFB1300202)Youth Innovation Promotion Association Chinese Academy of Sciences(CAS)(No.2015112)
文摘In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of work- pieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency （DM-TBS） is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(61303192)the China Postdoctoral Science Foundation(2015M5726942016T90979)
文摘In order to better represent infrared target features under different environments, a saliency detection method based on region covariance and global feature is proposed. Firstly, the region covariance features on different scale spaces and different image regions are extracted and transformed into sigma features,then combined with central position feature, the local salient map is generated. Next, a global salient map is generated by gray contrast and density estimation. Finally, the saliency detection result of infrared images is obtained by fusing the local and global salient maps. The experimental results show that the salient map of the proposed method has complete target features and obvious edges,and the proposed method is better than the state of art method both qualitatively and quantitatively.
