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基于计算机视觉的油菜叶面积计算方法研究 被引量:12
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作者 李锦卫 管鹤卿 廖桂平 《农业网络信息》 2010年第12期15-18,23,共5页
介绍了利用计算机视觉技术测量油菜叶面积的原理和方法,并以纸重法为标准对比分析了传统直尺法与此方法在油菜三种类型叶片叶面积测量中的优劣性。结果表明,计算机视觉法在精度上明显高于直尺法,尤其在无柄叶测量中此方法的测量平均相... 介绍了利用计算机视觉技术测量油菜叶面积的原理和方法,并以纸重法为标准对比分析了传统直尺法与此方法在油菜三种类型叶片叶面积测量中的优劣性。结果表明,计算机视觉法在精度上明显高于直尺法,尤其在无柄叶测量中此方法的测量平均相对误差为3.09%,低于直尺法的10.49%,进一步根据标准测量值对直尺法测量无柄叶时的系数进行校正,发现系数选择0.75更为合适。 展开更多
关键词 油菜 叶面积测量 计算机视觉法 纸重 直尺
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NEW CORNER DETECTION ALGORITHM BASED ON MULTI-FEATURE SYNTHESIS 被引量:3
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作者 邱卫国 昂海松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2004年第3期174-178,共5页
Corner detection is a chief step in computer vision. A new corner detection algorithm in planar curves is proposed. Firstly, from the human perception, two key characteristics are given as an amendment of the traditio... Corner detection is a chief step in computer vision. A new corner detection algorithm in planar curves is proposed. Firstly, from the human perception, two key characteristics are given as an amendment of the traditional corner properties. Based on the two properties, the concept of the fuzzy set is introduced into a detection. Secondly, the extracted-formulae of three groups including the features of the corner subject degree are derived. Through synthesizing the features of three groups, the judgments of the corner detection, location, and optimization are obtained. Finally, by using the algorithm the detection results of several examples and feature curves for some interested parts, as well as the detection results for the test images history in references are given. Results show that the algorithm is easily realized after adopting the fuzzy set, and the detection effect is very ideal. 展开更多
关键词 image feature corner detection fuzzy infe-rence subject degree
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Computer Vision Method in Human Motion Detection
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作者 傅莉 方帅 徐心和 《Defence Technology(防务技术)》 SCIE EI CAS 2007年第1期54-58,共5页
Human motion detection based on computer vision is a frontier research topic and is causing an increasing attention in the field of computer vision research. The wavelet transform is used to sharpen the ambiguous edge... Human motion detection based on computer vision is a frontier research topic and is causing an increasing attention in the field of computer vision research. The wavelet transform is used to sharpen the ambiguous edges in human motion image. The shadow’s effect to the image processing is also removed. The edge extraction can be successfully realized. This is an effective method for the research of human motion analysis system. 展开更多
关键词 计算机视觉法 人体运动检测 图象重建 小波变换
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Facial expression feature extraction method based on improved LBP 被引量:4
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作者 WANG Si-ming LIANG Yun-hua 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第4期342-347,共6页
Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global featur... Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global features extracted.To solve these problems,a facial expression feature extraction method is proposed based on improved LBP.Firstly,LBP is converted into double local binary pattern(DLBP).Then by combining Taylor expansion(TE)with DLBP,DLBP-TE algorithm is obtained.Finally,the DLBP-TE algorithm combined with extreme learning machine(ELM)is applied in seven kinds of ficial expression images and the corresponding experiments are carried out in Japanese adult female facial expression(JAFFE)database.The results show that the proposed method can significantly improve facial expression recognition rate. 展开更多
关键词 facial expression feature extraction DLBP-TE algorithm computer vision extrem learning machine(ELM)
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Semantic image annotation based on GMM and random walk model 被引量:1
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作者 田东平 《High Technology Letters》 EI CAS 2017年第2期221-228,共8页
Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk... Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation. 展开更多
关键词 semantic image annotation Gaussian mixture model GMM) random walk rival penalized expectation maximization (RPEM) image retrieval
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A practical iterative two-view metric reconstruction with uncalibrated cameras 被引量:1
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作者 ZHOU Yong-jun KOU Xin-jian 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第10期1614-1623,共10页
This paper presents a practical iterative algorithm for two-view metric reconstruction without any prior knowledge about the scene and motion in a nonsingular geometry configuration. The principal point is assumed to ... This paper presents a practical iterative algorithm for two-view metric reconstruction without any prior knowledge about the scene and motion in a nonsingular geometry configuration. The principal point is assumed to locate at the image center with zero skew and the same aspect ratio, and the interior parameters are fixed, so the self-calibration becomes focal-length cali- bration. Existing focal length calibration methods are direct solutions of a quadric composed of fundamental matrix, which are sensitive to noise. A quaternion-based linear iterative Least-Square Method is proposed in this paper, and one-dimensional searching for optimal focal length in a constrained region instead of solving optimization problems with inequality constraints is applied to simplify the computation complexity, then unique rotational matrix and translate vector are recovered. Experiments with simulation data and real images are given to verify the algorithm. 展开更多
关键词 Close-range photogrammetry Computer vision General relative orientation Unit quaternion Metric reconstruction
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Analyzing Motion Patterns in Crowded Scenes via Automatic Tracklets Clustering 被引量:1
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作者 王冲 赵旭 +1 位作者 邹毅 刘允才 《China Communications》 SCIE CSCD 2013年第4期144-154,共11页
Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose... Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach. 展开更多
关键词 crowded scene analysis motionpattern tracklet automatic clustering
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Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network 被引量:2
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作者 Gaiping He 《International Journal of Technology Management》 2016年第9期53-55,共3页
Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis... Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image. 展开更多
关键词 High Resolution Satellite Image Classification Convolution Neural Network Clustering Algorithm.
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An improved RANSAC algorithm for 3D wheel alignment
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作者 CHENG Wei ZHU Zhifeng +3 位作者 YAO Yong WANG Bing ZHOU Fang TANG Dezhi 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第4期407-417,共11页
Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special... Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special circle center target board is designed to calibrate the camera,and then the registration of the homography matrix is optimized by using the improved RANSAC(Random sample consensus)algorithm combined with the designed special target board,and the parameters of the wheel alignment system are adjusted by using the space vector principle.Accurate measurements are made to obtain the parameters of the four-wheel alignment.Design a calibration comparison experiment between the traditional target board and the new type of target board,and conduct a comparative test with the existing four-wheel aligner of the depot.The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9%to 21%,while improving the calibration accuracy by about 10.6%to 17.8%.And through the real vehicle test,it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning.This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters. 展开更多
关键词 computer vision four-wheel alignment binocular calibration RANSAC algorithm homography matrix
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A new algorithm of brain volume contours segmentation
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作者 吴建明 施鹏飞 《Journal of Zhejiang University Science》 CSCD 2003年第3期294-299,共6页
This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a ... This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation. 展开更多
关键词 CT slices Contours segmentation Edge detector
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Unwrapping and stereo rectification for omnidirectional images 被引量:1
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作者 Jie LEI Xin DU +1 位作者 Yun-fang ZHU Ji-lin LIU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第8期1125-1139,共15页
Omnidirectional imaging sensors have been used in more and more applications when a very large field of view is required.In this paper,we investigate the unwrapping,epipolar geometry and stereo rectification issues fo... Omnidirectional imaging sensors have been used in more and more applications when a very large field of view is required.In this paper,we investigate the unwrapping,epipolar geometry and stereo rectification issues for omnidirectional vision when the particular mirror model and the camera parameters are unknown in priori.First,the omnidirectional camera is calibrated under the Taylor model,and the parameters related to this model are obtained.In order to make the classical computer vision algorithms of conventional perspective cameras applicable,the ring omnidirectional image is unwrapped into two kinds of panoramas:cylinder and cuboid.Then the epipolar geometry of arbitrary camera configuration is analyzed and the essential matrix is deduced with its properties being indicated for ring images.After that,a simple stereo rectification method based on the essential matrix and the conformal mapping is proposed.Simulations and real data experimental results illustrate that our methods are effective for the omnidirectional camera under the constraint of a single view point. 展开更多
关键词 Single point of view CALIBRATION Catadioptric image unwrapping Omnidirectional stereo vision Epipolar geometry Essential matrix Conformal mapping
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