The development and deployment of diverse resistance sources in new wheat cultivars underpin the durable control of stripe rust.In the present study,two loci for adult plant resistance(APR),QYr SM155.1 and QYr SM155.2...The development and deployment of diverse resistance sources in new wheat cultivars underpin the durable control of stripe rust.In the present study,two loci for adult plant resistance(APR),QYr SM155.1 and QYr SM155.2,were identified in the Chinese wheat breeding line Shaanmai 155.QYr SM155.1 was mapped to a 3.0-c M interval between the single-nucleotide polymorphism(SNP)markers AX-109583610 and AX-110907562 on chromosome arm 2 BL.QYr SM155.2 was mapped to a 2.1-c M interval flanked by the SNP markers AX-110378556 and AX-86173526 on chromosome arm 7 AS.A genome-wide association study was used to identify markers associated with APR in a panel of 411 spring wheat lines.Thirteen and 11 SNPs were significantly associated with QYr SM155.1 and QYr SM155.2,respectively,corresponding to physical intervals of 653.75–655.52 Mb on 2 BL and 81.63–83.93 Mb on7 AS.To characterize the haplotype variation and the distribution of these QTL,haplotype analysis was performed based on these SNPs in an independent panel of 1101 worldwide wheat accessions.Three major haplotypes(2 B_h1,2 B_h2,and 2 B_h3)for QYr SM155.1 and four major haplotypes(7 A_h1,7 A_h2,7 A_h3,and 7 A_h4)for QYr SM155.2 were identified.Accessions individually harboring QYr SM155.1_h1 and QYr SM155.2_h1 haplotypes and their combination displayed resistance.Additional assays of 1306 current Chinese cultivars and breeding lines using markers flanking QYr SM155.1 and QYr SM155.2 indicated that the resistance haplotypes of the two QTL were present in respectively 1.45%and 14.16%of lines.Increasing resistance haplotype frequencies at these two loci using marker-assisted selection should benefit wheat production in China.展开更多
Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio...Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.展开更多
In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on...In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on human visual saliency model in H.264/AVC. Firstly, we modifie Itti's saliency model. Secondly, target bits of each frame are allocated through the correlation of saliency region between the current and previous frame, and the complexity of each MB is modified through the saliency value and its Mean Absolute Difference (MAD) value. Lastly, the algorithm was implemented in JVT JM12.2. Simulation results show that, comparing with traditional rate control algorithm, the proposed one can reduce the coding bit rate and improve the reconstructed video subjective quality, especially for visual saliency region. It is very suitable for wireless video transmission.展开更多
A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2...A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.展开更多
Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,i...Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,its shape may be changed and part of the information may be lost.Therefore,we propose a method for constructing salience adaptive morphological structuring elements based on minimum spanning tree(MST).First,the gradient image of the input image is calculated,the edge image is obtained by non-maximum suppression(NMS)of the gradient image,and then chamfer distance transformation is performed on the edge image to obtain a salience map(SM).Second,the radius of structuring element is determined by calculating the maximum and minimum values of SM and then the minimum spanning tree is calculated on the SM.Finally,the radius is used to construct a structuring element whose shape and size adaptively change with the local features of the input image.In addition,the basic morphological operators such as erosion,dilation,opening and closing are redefined using the adaptive structuring elements and then compared with the classical morphological operators.The simulation results show that the proposed method can make full use of the local features of the image and has better processing results in image structure preservation and image filtering.展开更多
Flower Image Classification is a Fine-Grained Classification problem.The main difficulty of Fine-Grained Classification is the large inter-class similarity and the inner-class difference.In this paper,we propose a new...Flower Image Classification is a Fine-Grained Classification problem.The main difficulty of Fine-Grained Classification is the large inter-class similarity and the inner-class difference.In this paper,we propose a new algorithm based on Saliency Map and PCANet to overcome the difficulty.This algorithm mainly consists of two parts:flower region selection,flower feature learning.In first part,we combine saliency map with gray-scale map to select flower region.In second part,we use the flower region as input to train the PCANet which is a simple deep learning network for learning flower feature automatically,then a 102-way softmax layer that follow the PCANet achieve classification.Our approach achieves 84.12%accuracy on Oxford 17 Flowers dataset.The results show that a combination of Saliency Map and simple deep learning network PCANet can applies to flower image classification problem.展开更多
DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map genera...DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map generation algorithm based on gradient,SMGG)。使用高层卷积层生成的特征图梯度信息计算不同特征图的重要性,在模型的结构和内部参数已知的情况下,从模型最后一层入手,通过对特征图梯度的计算,生成不同特征图相对于显著性图的权重;对特征重要性进行正向和负向分类,利用有正向影响的权值将特征图中捕获的特征进行加权,构成当前决策的正向解释;利用对其他类别有负向影响的权值将特征图中捕获的特征进行加权,构成当前决策的反向解释。二者共同生成决策的显著性图,得出智能体决策行为的依据,实验证明了该方法的有效性。展开更多
Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occ...Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.展开更多
基金financially the National Natural Science Foundation of China(31871611 and31971890)the National Science Foundation for Young Scientistsin China(31901494 and 31901869)+1 种基金International Cooperation and Exchange of the National Natural Science Foundation of China(31961143019)the Integrated Extension Project of Agricultural Science and Technology Innovation in Shaanxi Province(NYKJ-2021-YL(XN)15)。
文摘The development and deployment of diverse resistance sources in new wheat cultivars underpin the durable control of stripe rust.In the present study,two loci for adult plant resistance(APR),QYr SM155.1 and QYr SM155.2,were identified in the Chinese wheat breeding line Shaanmai 155.QYr SM155.1 was mapped to a 3.0-c M interval between the single-nucleotide polymorphism(SNP)markers AX-109583610 and AX-110907562 on chromosome arm 2 BL.QYr SM155.2 was mapped to a 2.1-c M interval flanked by the SNP markers AX-110378556 and AX-86173526 on chromosome arm 7 AS.A genome-wide association study was used to identify markers associated with APR in a panel of 411 spring wheat lines.Thirteen and 11 SNPs were significantly associated with QYr SM155.1 and QYr SM155.2,respectively,corresponding to physical intervals of 653.75–655.52 Mb on 2 BL and 81.63–83.93 Mb on7 AS.To characterize the haplotype variation and the distribution of these QTL,haplotype analysis was performed based on these SNPs in an independent panel of 1101 worldwide wheat accessions.Three major haplotypes(2 B_h1,2 B_h2,and 2 B_h3)for QYr SM155.1 and four major haplotypes(7 A_h1,7 A_h2,7 A_h3,and 7 A_h4)for QYr SM155.2 were identified.Accessions individually harboring QYr SM155.1_h1 and QYr SM155.2_h1 haplotypes and their combination displayed resistance.Additional assays of 1306 current Chinese cultivars and breeding lines using markers flanking QYr SM155.1 and QYr SM155.2 indicated that the resistance haplotypes of the two QTL were present in respectively 1.45%and 14.16%of lines.Increasing resistance haplotype frequencies at these two loci using marker-assisted selection should benefit wheat production in China.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.62061024the Project of Gansu Province Science and Technology Department under Grant No.22ZD6GA055.
文摘Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.
基金supported by National Natural Science Foundation of China under Grant No.610700800973 Sub-Program Projects under Grant No.2009CB320906+3 种基金National Science and Technology of Major Special Projects under Grant No.2010ZX03004-003S&T Planning Project of Hubei Provincial Department of Education under Grant No. Q20112805H&SPlanning Project of Hubei Provincial Department of Education under Grant No.2011jyte142Science Foundation of HubeiProvincial under Grant No.2010CDB05103
文摘In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on human visual saliency model in H.264/AVC. Firstly, we modifie Itti's saliency model. Secondly, target bits of each frame are allocated through the correlation of saliency region between the current and previous frame, and the complexity of each MB is modified through the saliency value and its Mean Absolute Difference (MAD) value. Lastly, the algorithm was implemented in JVT JM12.2. Simulation results show that, comparing with traditional rate control algorithm, the proposed one can reduce the coding bit rate and improve the reconstructed video subjective quality, especially for visual saliency region. It is very suitable for wireless video transmission.
文摘A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.
基金National Natural Science Foundation of China(No.61761027)。
文摘Classical mathematical morphology operations use a fixed size and shape structuring element to process the whole image.Due to the diversity of image content and the complexity of target structure,for processed image,its shape may be changed and part of the information may be lost.Therefore,we propose a method for constructing salience adaptive morphological structuring elements based on minimum spanning tree(MST).First,the gradient image of the input image is calculated,the edge image is obtained by non-maximum suppression(NMS)of the gradient image,and then chamfer distance transformation is performed on the edge image to obtain a salience map(SM).Second,the radius of structuring element is determined by calculating the maximum and minimum values of SM and then the minimum spanning tree is calculated on the SM.Finally,the radius is used to construct a structuring element whose shape and size adaptively change with the local features of the input image.In addition,the basic morphological operators such as erosion,dilation,opening and closing are redefined using the adaptive structuring elements and then compared with the classical morphological operators.The simulation results show that the proposed method can make full use of the local features of the image and has better processing results in image structure preservation and image filtering.
文摘Flower Image Classification is a Fine-Grained Classification problem.The main difficulty of Fine-Grained Classification is the large inter-class similarity and the inner-class difference.In this paper,we propose a new algorithm based on Saliency Map and PCANet to overcome the difficulty.This algorithm mainly consists of two parts:flower region selection,flower feature learning.In first part,we combine saliency map with gray-scale map to select flower region.In second part,we use the flower region as input to train the PCANet which is a simple deep learning network for learning flower feature automatically,then a 102-way softmax layer that follow the PCANet achieve classification.Our approach achieves 84.12%accuracy on Oxford 17 Flowers dataset.The results show that a combination of Saliency Map and simple deep learning network PCANet can applies to flower image classification problem.
文摘DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map generation algorithm based on gradient,SMGG)。使用高层卷积层生成的特征图梯度信息计算不同特征图的重要性,在模型的结构和内部参数已知的情况下,从模型最后一层入手,通过对特征图梯度的计算,生成不同特征图相对于显著性图的权重;对特征重要性进行正向和负向分类,利用有正向影响的权值将特征图中捕获的特征进行加权,构成当前决策的正向解释;利用对其他类别有负向影响的权值将特征图中捕获的特征进行加权,构成当前决策的反向解释。二者共同生成决策的显著性图,得出智能体决策行为的依据,实验证明了该方法的有效性。
基金a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT)Republic of Korea.This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding program Grant Code(NU/RG/SERC/12/6).
文摘Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.