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Journal articles on the topic 'Homography and homography decomposition'

Author: Grafiati

Published: 3 February 2023

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1

Wu, Chunfu, Guodong Li, Qingshun Tang, and Fengyu Zhou. "Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition." Journal of Control Science and Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/739894.

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For the monocular camera-based mobile robot system, an adaptive hybrid visual servo regulation algorithm which is based on a fast homography decomposition method is proposed to drive the mobile robot to its desired position and orientation, even when object’s imaging depth and camera’s position extrinsic parameters are unknown. Firstly, the homography’s particular properties caused by mobile robot’s 2-DOF motion are taken into account to induce a fast homography decomposition method. Secondly, the homography matrix and the extracted orientation error, incorporated with the desired view’s single feature point, are utilized to form an error vector and its open-loop error function. Finally, Lyapunov-based techniques are exploited to construct an adaptive regulation control law, followed by the experimental verification. The experimental results show that the proposed fast homography decomposition method is not only simple and efficient, but also highly precise. Meanwhile, the designed control law can well enable mobile robot position and orientation regulation despite the lack of depth information and camera’s position extrinsic parameters.

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Zhan, Xinrui, Yueran Liu, Jianke Zhu, and Yang Li. "Homography Decomposition Networks for Planar Object Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3234–42. http://dx.doi.org/10.1609/aaai.v36i3.20232.

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Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM. Although the previous planar trackers work well in most scenarios, it is still a challenging task due to the rapid motion and large transformation between two consecutive frames. The essential reason behind this problem is that the condition number of such a non-linear system changes unstably when the searching range of the homography parameter space becomes larger. To this end, we propose a novel Homography Decomposition Networks~(HDN) approach that drastically reduces and stabilizes the condition number by decomposing the homography transformation into two groups. Specifically, a similarity transformation estimator is designed to predict the first group robustly by a deep convolution equivariant network. By taking advantage of the scale and rotation estimation with high confidence, a residual transformation is estimated by a simple regression model. Furthermore, the proposed end-to-end network is trained in a semi-supervised fashion. Extensive experiments show that our proposed approach outperforms the state-of-the-art planar tracking methods at a large margin on the challenging POT, UCSB and POIC datasets. Codes and models are available at https://github.com/zhanxinrui/HDN.

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Zong, Xiao Ping, Yue Xia Li, Pei Guang Wang, and Wei Dong Liu. "Simulation Design of Visual Servo Based on Homography Matrix Decomposition." Applied Mechanics and Materials 241-244 (December 2012): 1855–58. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1855.

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A visual servo simulation model for puma560 robot based on homography matrix decomposition was designed. Utilizing the Robotics Toolbox and Machine Vision Toolbox, the simulation model of the visual servo system was built using the sub-systems in Matlab Simulink. Finally, the experimental results show the visual servo system can enable the robot to reach the desired position quickly and accurately.

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Zhang, Lei, Zhengjun Zhai, Lang He, Pengcheng Wen, and Wensheng Niu. "Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments." Sensors 19, no. 2 (January 20, 2019): 408. http://dx.doi.org/10.3390/s19020408.

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This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments.

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Chitrakaran, V. K., A. Behal, D. M. Dawson, and I. D. Walker. "Setpoint regulation of continuum robots using a fixed camera." Robotica 25, no. 5 (September 2007): 581–86. http://dx.doi.org/10.1017/s0263574707003475.

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SUMMARYIn this paper, we investigate the problem of measuring the shape of a continuum robot manipulator using visual information from a fixed camera. Specifically, we capture the motion of a set of fictitious planes, each formed by four or more feature points, defined at various strategic locations along the body of the robot. Then, utilizing expressions for the robot forward kinematics as well as the decomposition of a homography relating a reference image of the robot to the actual robot image, we obtain the three-dimensional shape information continuously. We then use this information to demonstrate the development of a kinematic controller to regulate the manipulator end-effector to a constant desired position and orientation.

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6

Tsironis, V., A. Tranou, A. Vythoulkas, A. Psalta, E. Petsa, and G. Karras. "AUTOMATIC RECTIFICATION OF BUILDING FAÇADES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (February 23, 2017): 645–50. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w3-645-2017.

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Focusing mainly on the case of (near-)planar building façades, a methodology for their automatic projective rectification is described and evaluated. It relies on a suitably configured, calibrated stereo pair of an object expected to contain a minimum of vertical and/or horizontal lines for the purposes of levelling. The SURF operator has been used for extracting and matching interest points. The coplanar points have been separated with two alternative methods. First, the fundamental matrix of the stereo pair, computed using robust estimation, allows estimating the relative orientation of the calibrated pair; initial parameter values, if needed, may be estimated via the essential matrix. Intersection of valid points creates a 3D point set in model space, to which a plane is robustly fitted. Second, all initial point matches are directly used for robustly estimating the inter-image homography of the pair, thus directly selecting all image matches referring to coplanar points; initial values for the relative orientation parameters, if needed, may be estimated from a decomposition of the inter-image homography. Finally, all intersected coplanar model points yield the object-to-image homography to allow image rectification. The in-plane rotation required to finalize the transformation is found by assuming that rectified images contain sufficient straight linear segments to form a dominant pair of orthogonal directions which correspond to horizontality/verticality in 3D space. In our implementation, image edges from Canny detector are used in linear Hough Transform (HT) resulting in a 2D array (ρ, θ) with values equal to the sum of pixels belonging to the particular line. Quantization parameter values aim at absorbing possible slight deviations from collinearity due to thinning or uncorrected lens distortions. By first imposing a threshold expressing the minimum acceptable number of edge-characterized pixels, the resulting HT is accumulated along the ρ-dimension to give a single vector, whose values represent the number of lines of the particular direction. Since here the dominant pair of orthogonal directions has to be found, all vector values are added with their π/2-shifted counterpart. This function is then convolved with a 1D Gaussian function; the optimal angle of in-plane rotation is at the maximum value of the result. The described approach has been successfully evaluated with several building façades of varying morphology by assessing remaining line convergence (projectivity), skewness and deviations from horizontality/verticality. Mean estimated deviation from a metric result was 0°.2. Open questions are also discussed.

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Fink, Geoff, Hui Xie, Alan F. Lynch, and Martin Jagersand. "Dynamic Visual Servoing for a Quadrotor Using a Virtual Camera." Unmanned Systems 05, no. 01 (January 2017): 1–17. http://dx.doi.org/10.1142/s2301385017500017.

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This paper presents a dynamic image-based visual servoing (IBVS) control law for a quadrotor unmanned aerial vehicle (UAV) equipped with a single fixed on-board camera. The motion control problem is to regulate the relative position and yaw of the vehicle to a moving planar target located within the camera’s field of view. The control law is termed dynamic as it’s based on the dynamics of the vehicle. To simplify the kinematics and dynamics, the control law relies on the notion of a virtual camera and image moments as visual features. The convergence of the closed-loop is proven to be globally asymptotically stable for a horizontal target. In the case of nonhorizontal targets, we modify the control using a homography decomposition. Experimental and simulation results demonstrate the control law’s performance.

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Michaelsen, E. "STITCHING LARGE MAPS FROM VIDEOS TAKEN BY A CAMERA MOVING CLOSE OVER A PLANE USING HOMOGRAPHY DECOMPOSITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-3/W22 (April 26, 2013): 125–29. http://dx.doi.org/10.5194/isprsarchives-xxxviii-3-w22-125-2011.

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Yan, Zhaocheng, Shuai Teng, Wenjun Luo, David Bassir, and Gongfa Chen. "Bridge Modal Parameter Identification from UAV Measurement Based on Empirical Mode Decomposition and Fourier Transform." Applied Sciences 12, no. 17 (August 30, 2022): 8689. http://dx.doi.org/10.3390/app12178689.

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This paper proposes two approaches, Empirical Mode Decomposition (EMD) and Fourier Transform (FT), to correct the vibration signals measured by an Unmanned Aerial Vehicle (UAV), which overcomes the difficulty of selection of reference points used in other correction methods, such as homography transformation and three-dimensional reconstruction. In the method of this paper, a UAV is used to collect the video of a vibrated bridge, and the displacement signal of the bridge is obtained from the video by Kanade–Lucas–Tomasi (KLT) optical flow method, which contains false displacement caused by the ego-motion of the UAV during the measurement. The false displacement can be effectively eliminated by EMD and FT to obtain the real displacement signal. Finally, the displacement signal is processed by the Operational Modal Analysis (OMA) technique to obtain the bridge modal parameters. The performance of correcting vibration signals and extracting bridge modal parameters from the vibration signals based on EMD, FT, and Differential Filtering (DF) are compared by taking the fixed camera measurement as a reference (the accuracy of measuring bridge vibration with fixed cameras has been verified) in this paper, and it is demonstrated that EMD has better reliability in processing signal measured by UAVs, which is mainly due to the absence of random factors and too much noise in the signal processing process of EMD.

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Goh, J. N., S. K. Phang, and W. J. Chew. "Real-time and automatic map stitching through aerial images from UAV." Journal of Physics: Conference Series 2120, no. 1 (December 1, 2021): 012025. http://dx.doi.org/10.1088/1742-6596/2120/1/012025.

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Abstract Real-time aerial map stitching through aerial images had been done through many different methods. One of the popular methods was a features-based algorithm to detect features and to match the features of two and more images to produce a map. There are several feature-based methods such as ORB, SIFT, SURF, KAZE, AKAZE and BRISK. These methods detect features and compute homography matrix from matched features to stitch images. The aim for this project is to further optimize the existing image stitching algorithm such that it will be possible to run in real-time as the UAV capture images while airborne. First, we propose to use a matrix multiplication method to replace a singular value decomposition method in the RANSAC algorithm. Next, we propose to change the workflow to detect the image features to increase the map stitching rate. The proposed algorithm was implemented and tested with an online aerial image dataset which contain 100 images with the resolution of 640 × 480. We have successfully achieved the result of 1.45 Hz update rate compared to original image stitching algorithm that runs at 0.69 Hz. The improvement shown in our proposed improved algorithm are more than two folds in terms of computational resources. The method introduced in this paper was successful speed up the process time for the program to process map stitching.

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Zhang, Gengxin, Danyang Qin, Jiaqiang Yang, Mengying Yan, Huapeng Tang, Haoze Bie, and Lin Ma. "UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic." Remote Sensing 14, no. 23 (November 27, 2022): 6013. http://dx.doi.org/10.3390/rs14236013.

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UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

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Zhou, Qiang, and Xin Li. "STN-Homography: Direct Estimation of Homography Parameters for Image Pairs." Applied Sciences 9, no. 23 (November 29, 2019): 5187. http://dx.doi.org/10.3390/app9235187.

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Estimating a 2D homography from a pair of images is a fundamental task in computer vision. Contrary to most convolutional neural network-based homography estimation methods that use alternative four-point homography parameterization schemes, in this study, we directly estimate the 3 × 3 homography matrix value. We show that after coordinate normalization, the magnitude difference and variance of the elements of the normalized 3 × 3 homography matrix is very small. Accordingly, we present STN-Homography, a neural network based on spatial transformer network (STN), to directly estimate the normalized homography matrix of an image pair. To decrease the homography estimation error, we propose hierarchical STN-Homography and sequence STN-homography models in which the sequence STN-Homography can be trained in an end-to-end manner. The effectiveness of the proposed methods is demonstrated based on experiments on the Microsoft common objects in context (MSCOCO) dataset, and it is shown that they significantly outperform the current state-of-the-art. The average processing time of the three-stage hierarchical STN-Homography and the three-stage sequence STN-Homography models on a GPU are 17.85 ms and 13.85 ms, respectively. Both models satisfy the real-time processing requirements of most potential applications.

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Li, Zheng Guo, Bo Wang, Yong Sheng Zhang, Xiao Chong Tong, Wei Can Meng, and Quan Fu Bao. "A Fast Orthoimage Mosaic Method for Application of Grid." Applied Mechanics and Materials 151 (January 2012): 685–89. http://dx.doi.org/10.4028/www.scientific.net/amm.151.685.

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Traditional orthoimage mosaic methods do not perform well in computational speed and geometric precision. This paper proposed a fast orthoimage mosaic method for the application of grid. First of all, down-sample the original images and extracts feature, and adopt SANSAC to estimate the relative initial homography; second, refine homography matrix by Levenberg-Marquardt method and use the sparse bundle adjustment method to estimate the precise homography matrix; Third, passed the homography matrix to the original level of image by the homography relationship of the down-sampling and original image. Finally, synthesize the mosaic image. Our experiments showed that the method combined the down-sampling homography relationship of original image with sparse bundle adjustment organically, which effectively improved the speed and obtained geometric seamless mosaic.

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Miller, Paul, Batel Liran-Hazan, and Vered Vaknin. "Morphological Decomposition in Reading Hebrew Homographs." Journal of Psycholinguistic Research 45, no. 3 (May 3, 2015): 717–38. http://dx.doi.org/10.1007/s10936-015-9364-4.

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Sheridan, Sonia Landy. "Homography: Visualizing Time." Leonardo 23, no. 2/3 (1990): 233. http://dx.doi.org/10.2307/1578612.

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Coghetto, Roland. "Homography in ℝℙ." Formalized Mathematics 24, no. 4 (December 1, 2016): 239–51. http://dx.doi.org/10.1515/forma-2016-0020.

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Summary The real projective plane has been formalized in Isabelle/HOL by Timothy Makarios [13] and in Coq by Nicolas Magaud, Julien Narboux and Pascal Schreck [12]. Some definitions on the real projective spaces were introduced early in the Mizar Mathematical Library by Wojciech Leonczuk [9], Krzysztof Prazmowski [10] and by Wojciech Skaba [18]. In this article, we check with the Mizar system [4], some properties on the determinants and the Grassmann-Plücker relation in rank 3 [2], [1], [7], [16], [17]. Then we show that the projective space induced (in the sense defined in [9]) by ℝ3 is a projective plane (in the sense defined in [10]). Finally, in the real projective plane, we define the homography induced by a 3-by-3 invertible matrix and we show that the images of 3 collinear points are themselves collinear.

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Nguyen, Ty, Steven W. Chen, Shreyas S. Shivakumar, Camillo Jose Taylor, and Vijay Kumar. "Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model." IEEE Robotics and Automation Letters 3, no. 3 (July 2018): 2346–53. http://dx.doi.org/10.1109/lra.2018.2809549.

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Mou, Wei, Han Wang, and Gerald Seet. "Robust Homography Estimation Based on Nonlinear Least Squares Optimization." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/897050.

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The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.

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de Jesus, Kelly, Karla de Jesus, Pedro Figueiredo, João Paulo Vilas-Boas, Ricardo Jorge Fernandes, and Leandro José Machado. "Reconstruction Accuracy Assessment of Surface and Underwater 3D Motion Analysis: A New Approach." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/269264.

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This study assessed accuracy of surface and underwater 3D reconstruction of a calibration volume with and without homography. A calibration volume (6000 × 2000 × 2500 mm) with 236 markers (64 above and 88 underwater control points—with 8 common points at water surface—and 92 validation points) was positioned on a 25 m swimming pool and recorded with two surface and four underwater cameras. Planar homography estimation for each calibration plane was computed to perform image rectification. Direct linear transformation algorithm for 3D reconstruction was applied, using 1600000 different combinations of 32 and 44 points out of the 64 and 88 control points for surface and underwater markers (resp.). Root Mean Square (RMS) error with homography of control and validations points was lower than without it for surface and underwater cameras (P≤0.03). With homography, RMS errors of control and validation points were similar between surface and underwater cameras (P≥0.47). Without homography, RMS error of control points was greater for underwater than surface cameras (P≤0.04) and the opposite was observed for validation points (P≤0.04). It is recommended that future studies using 3D reconstruction should include homography to improve swimming movement analysis accuracy.

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Luo, Yinhui, Xingyi Wang, Yuezhou Wu, and Chang Shu. "Detail-Aware Deep Homography Estimation for Infrared and Visible Image." Electronics 11, no. 24 (December 14, 2022): 4185. http://dx.doi.org/10.3390/electronics11244185.

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Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset.

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Ondrašovič, Milan, and Peter Tarábek. "Homography Ranking Based on Multiple Groups of Point Correspondences." Sensors 21, no. 17 (August 26, 2021): 5752. http://dx.doi.org/10.3390/s21175752.

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Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.

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Ying, Jie, Yan Bin Song, and Lin Li Lu. "Research on Panorama Generation Based on Homography Estimation." Applied Mechanics and Materials 423-426 (September 2013): 2518–21. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2518.

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An improved panorama generation method based on homography estimation was proposed. Corners detection were realized using FAST algorithm. After corner detection, the related corners were matched and homography estimation was established. Using random sample consensus related estimation, we got accurate interior points to estimate parameters. Image fusion was realized using hybrid filter operator. Experimental results showed that the panorama generation method based on homography estimation is effective and robust.

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Zhao, Yue, and Xiao Hua Hu. "Affine Reconstruction Based on Parallel Plane and Infinity Point." Advanced Materials Research 255-260 (May 2011): 2272–75. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2272.

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Affine reconstruction is to restore the affine shape of the object. Generally, there are two ways of achieving, one is to determine the plane at infinity, another is to determine the plane homography. Using the homography which had determined the plane at infinity achieve affine reconstruction. In this paper, firstly give out the homography of infinity plane and the algorithm of affine reconstruction, then proved: if the scene contains a set of parallel planes and a infinity point, the homography of infinity plane can be obtained and affine reconstruction can be linearly got in the scene. Computer simulation and real experiments show that the linear affine reconstruction algorithm is correct, and the approach has a good precision.

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Finlayson, GrahamD, Han Gong, and RobertB Fisher. "Color Homography Color Correction." Color and Imaging Conference 2016, no. 1 (November 7, 2016): 310–14. http://dx.doi.org/10.2352/issn.2169-2629.2017.32.310.

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Liu, Shaoguo, Haibo Wang, Yiyi Wei, and Chunhong Pan. "BB-Homography: Joint Binary Features and Bipartite Graph Matching for Homography Estimation." IEEE Transactions on Circuits and Systems for Video Technology 25, no. 2 (February 2015): 239–50. http://dx.doi.org/10.1109/tcsvt.2014.2339591.

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Shen, Yan, Yuxing Dai, and Zhiliang Zhu. "Efficient line matching with homography." Measurement Science and Technology 29, no. 3 (February 14, 2018): 035001. http://dx.doi.org/10.1088/1361-6501/aaa210.

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Finlayson, Graham, Han Gong, and Robert B. Fisher. "Color Homography: Theory and Applications." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 1 (January 1, 2019): 20–33. http://dx.doi.org/10.1109/tpami.2017.2760833.

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Kaliszan, Jerzy. "Verbal-substantival homography in Russian." Scripta Neophilologica Posnaniensia, no. 13 (August 17, 2018): 17–22. http://dx.doi.org/10.14746/snp.2013.13.02.

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Rajnik, Eugeniusz. "Rzeczowniki homograficzne w języku duńskim − analiza morfologiczna i semantyczna." Studia Rossica Posnaniensia, no. 43 (November 26, 2018): 269–78. http://dx.doi.org/10.14746/strp.2018.43.20.

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The article is a study of nouns with the same spelling but different grammatical gender (graphic homonymy, i.e. homography) in Modern Danish. The aim of the article is to analyze the grammatical and lexical homography of both neuter and non-neuter Danish nouns, which have been divided into two groups. The nouns in the first group occur in both grammatical genders with no difference in meaning (grammatical homographs), while the second group is comprised of nouns that do have different meanings based on their grammatical gender (lexical homographs). The author has also identified differences based on word stress (stress homography), different pronunciation and differences connected with the presence of stød or lack thereof, which usually cause a difference in meaning.

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Seibt, Simon, Bartosz Von Rymon Lipinski, and Marc Erich Latoschik. "Dense Feature Matching Based on Homographic Decomposition." IEEE Access 10 (2022): 21236–49. http://dx.doi.org/10.1109/access.2022.3152539.

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Ge, Dong Yuan, Xi Fan Yao, Wen Jiang Xiang, and Yuan Liu. "Solving Intrinsic Parameters of Camera Calibrated from Controlled Motion Sequences and Homography." Applied Mechanics and Materials 121-126 (October 2011): 4716–20. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4716.

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A camera calibration based on controlled motion sequences and homography is adopted. First condition of unique solution for constrained equation set is proofed. Then two sets three orthogonal motions are carried out for camera in manipulator, thus 6 homographic matrixes of plane are obtained at 7 view points, and thus the intrinsic parameters of camera are achieved. The experiments on real images validate our technology based on homography.

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Zhou, Qiang, and Xin Li. "Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots." Applied Sciences 9, no. 14 (July 20, 2019): 2908. http://dx.doi.org/10.3390/app9142908.

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When locating wall-climbing robots with vision-based methods, locating and controlling the wall-climbing robot in the pixel coordinate of the wall map is an effective alternative that eliminates the need to calibrate the internal and external parameters of the camera. The estimation accuracy of the homography matrix between the camera image and the wall map directly impacts the pixel positioning accuracy of the wall-climbing robot in the wall map. In this study, we focused on the homography estimation between the camera image and wall map. We proposed HomographyFpnNet and obtained a smaller homography estimation error for a center-aligned image pair compared with the state of the art. The proposed hierarchical HomographyFpnNet for a non-center-aligned image pair significantly outperforms the method based on artificially designed features + Random Sample Consensus. The experiments conducted with a trained three-stage hierarchical HomographyFpnNet model on wall images of climbing robots also achieved small mean corner pixel error and proved its potential for estimating the homography between the wall map and camera images. The three-stage hierarchical HomographyFpnNet model has an average processing time of 10.8 ms on a GPU. The real-time processing speed satisfies the requirements of wall-climbing robots.

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Kaliszan, Jerzy. "Rosyjska homografia gramatyczna." Folia Scandinavica Posnaniensia 20, no. 1 (December 1, 2016): 133–40. http://dx.doi.org/10.1515/fsp-2016-0031.

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Abstract The article is devoted to the study of Russian grammatical homography (i.e. the relationship between paradigmatic forms with the same spelling but different pronunciation caused by different word stress) among nouns, adjectives and verbs. This type of homography may be illustrated by such pairs of inflectional forms of the same lexeme as руки - руки, большую - большую, смотрите - смотрите etc. The author’s aim is an attempt to describe all kinds of grammatical homographs existing in contemporary Russian.

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Kaliszan, Jerzy. "Грамматическая субстантивная омография в русском языке." Studia Rossica Posnaniensia, no. 38 (January 1, 2013): 119–23. http://dx.doi.org/10.14746/strp.2013.38.9.

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This article is devoted to characterizing one of the main areas of contemporary Russian stress homography (i.e. the relationship between words that are spelled in the same way but pronounced differently as a result of different word stress). The author’s aim is to attempt to describe all kinds of grammatical (paradigmatical) substantival homography that manifests itself in correlations between inflectional forms of the same lexeme such as учителя – учителя, письма – письма, (по) снегу – (на) снегу, etc.

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Imran Sainan, Khairul, Mirwansah Mohamad, Zulkifli Mohamed3, Saiful Bahari Saari, Muhd Faiz Mat, and Nurul Syuhadah Khusaini. "Athletes Tracking using Homography Method: a Preliminary Study." International Journal of Engineering & Technology 7, no. 4.27 (November 30, 2018): 6. http://dx.doi.org/10.14419/ijet.v7i4.27.22427.

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Particle tracking has been used widely to track a single particle motion or trajectory in a medium. One of the applications of the particle tracking is in sports analysis. The tracking method can be divided into, the wearable device based system and the image based system. The wearable device based system utilizing global (GPS) and local (LPM) positioning system to track player movement. The image based system use video image processing to track the player movement and recorded is frames basis. However, the image processing method in a football match requires correction as it is normally recorded from the side of the field. Thus, to solve this problem a set of mathematical solution is needed to convert the image coordinate system (pixels) to the actual coordinate system (meters). The most commonly used is the homography method. The technique requires at least 4 reference points to transform the image coordinate into the actual coordinate system. In this project, a futsal game was recorded. The image coordinate of the player were marked in each frame with respect to the time. The image coordinate data were converted into the actual coordinate using homography matrix. Comparisons were made between the homography technique method and open-source available image warp processing method for validation. Based on the result, the homography coordinate transformation system produce a good agreement with actual player activity on the field.

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Zhao, Qiang, Yike Ma, Chen Zhu, Chunfeng Yao, Bailan Feng, and Feng Dai. "Image stitching via deep homography estimation." Neurocomputing 450 (August 2021): 219–29. http://dx.doi.org/10.1016/j.neucom.2021.03.099.

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Lee, Ki-Yong, and Joon-Woong Lee. "Ground Plane Detection Using Homography Matrix." Journal of Institute of Control, Robotics and Systems 17, no. 10 (October 1, 2011): 983–88. http://dx.doi.org/10.5302/j.icros.2011.17.10.983.

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Yoo, Hojin, and Kyungkoo Jun. "Deep Homography for License Plate Detection." Information 11, no. 4 (April 17, 2020): 221. http://dx.doi.org/10.3390/info11040221.

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The orientation of plate images in license plate recognition is one of the factors that influence its accuracy. In particular, tilted plate images are harder to detect and recognize characters with than aligned ones. To this end, the rectification of plates in a preprocessing step is essential to improve their performance. We propose deep models to estimate four-corner coordinates of tilted plates. Since the predicted corners can then be used to rectify plate images, they can help improve plate recognition in plate recognition. The main contributions of this work are a set of open-structured hybrid networks to predict corner positions and a novel loss function that combines pixel-wise differences with position-wise errors, producing performance improvements. Regarding experiments using proprietary plate images, one of the proposed modes produces a 3.1% improvement over the established warping method.

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Lee, Seohyung, Daeho Lee, and Jin Hyuk Choi. "Multiple Images Stitching Using Homography Similarity." Journal of Korean Institute of Information Technology 13, no. 6 (June 30, 2015): 129. http://dx.doi.org/10.14801/jkiit.2015.13.6.129.

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Huang, Yifei, Changbo Wang, and Chenhui Li. "Translucent Image Recoloring through Homography Estimation." Computer Graphics Forum 37, no. 7 (October 2018): 421–32. http://dx.doi.org/10.1111/cgf.13579.

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Zhu, HaiJiang, Ping Yang, and ShiGang Li. "Estimating fisheye camera parameters from homography." Science China Information Sciences 55, no. 9 (June 17, 2011): 2119–27. http://dx.doi.org/10.1007/s11432-011-4277-9.

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Osuna-Enciso, Valentín, Erik Cuevas, Diego Oliva, Virgilio Zúñiga, Marco Pérez-Cisneros, and Daniel Zaldívar. "A Multiobjective Approach to Homography Estimation." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3629174.

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In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm.

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Li, Nan, Yifang Xu, and Chao Wang. "Quasi-Homography Warps in Image Stitching." IEEE Transactions on Multimedia 20, no. 6 (June 2018): 1365–75. http://dx.doi.org/10.1109/tmm.2017.2771566.

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Diyazitdinova, Alfiya A. "Improving noise immunity of homography superposition for television signals." Physics of Wave Processes and Radio Systems 24, no. 1 (May 6, 2021): 58–66. http://dx.doi.org/10.18469/1810-3189.2021.24.1.58-66.

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The algorithm of homography superposition of the television signals was shown in this article. The algorithm allows improving noise immunity for homography parameters of television signal with textural fragments. The core of the estimation procedure of homography parameters for image superposition is the matching of the feature points. The feature points are local extremes of the pixel brightness of the image. The matching is defined by the maximum correlation coefficient between a region of the image of a neighbor area of the feature point. The log-polar system of the images provides an invariant correlation of scale and rotate are proportional offsets along the axis in this coordinate system. The noise immunity is provided due to the developing procedure of removing feature points in the textural region. This procedure leads to decreasing probability of error matching of the feature points. The numerical modeling shows high noise immunity of the developed procedure in comparing the current researches.

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Wang, Yuanwei, Mei Yu, Gangyi Jiang, Zhiyong Pan, and Jiqiang Lin. "Image Registration Algorithm Based on Convolutional Neural Network and Local Homography Transformation." Applied Sciences 10, no. 3 (January 21, 2020): 732. http://dx.doi.org/10.3390/app10030732.

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In order to overcome the poor robustness of traditional image registration algorithms in illuminating and solving the problem of low accuracy of a learning-based image homography matrix estimation algorithm, an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. Firstly, to ensure the diversity of samples, a sample and label generation method based on moving direct linear transformation (MDLT) is designed. The generated samples and labels can effectively reflect the local characteristics of images and are suitable for training the CNN model with which multiple pairs of local matching points between two images to be registered can be calculated. Then, the local homography matrices between the two images are estimated by using the MDLT and finally the image registration can be realized. The experimental results show that the proposed image registration algorithm achieves higher accuracy than other commonly used algorithms such as the SIFT, ORB, ECC, and APAP algorithms, as well as another two learning-based algorithms, and it has good robustness for different types of illumination imaging.

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Shi, Huaitao, Lei Guo, Shuai Tan, Gang Li, and Jie Sun. "Improved Parallax Image Stitching Algorithm Based on Feature Block." Symmetry 11, no. 3 (March 7, 2019): 348. http://dx.doi.org/10.3390/sym11030348.

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Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.

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Banga, Arina, Esther Hanssen, Robert Schreuder, and Anneke Neijt. "How subtle differences in orthography influence conceptual interpretation." Units of Language – Units of Writing 15, no. 2 (August 10, 2012): 185–208. http://dx.doi.org/10.1075/wll.15.2.04ban.

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The present study investigates linguistic relativity. The units of writing investigated are e and en, which are used to represent units of language in Dutch, Frisian, and Afrikaans. Dutch has homographic forms in the plural suffix -en and the linking element of noun-noun compounds en. Frisian does not have homography of this kind, while Afrikaans has a different homography. This raises the question whether second language learners of Dutch consistently interpret the linking en in Dutch noun-noun compounds as plural in the way that native speakers do. Plurality ratings for Dutch modifiers obtained from native Dutch speakers are compared with ratings from Frisian-Dutch bilinguals and Afrikaaners learning Dutch as L2. Significant differences relating to orthography are observed. We therefore argue that differing orthographic conventions in one’s native language (L1) can lead to different interpretations for the same everyday words written in Dutch (L2). Orthography thus provides an example of linguistic relativity. Keywords: linguistic relativity; second language learning; morphology; compounding; linking element; plurality; homography; Dutch; Frisian; Afrikaans

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Park, Keon-woo, Yoo-Jeong Shim, Myeong-jin Lee, and Heejune Ahn. "Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments." Sensors 20, no. 1 (December 22, 2019): 92. http://dx.doi.org/10.3390/s20010092.

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In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.

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LIU, CHENGUANG, HENGDA CHENG, and ARAVIND DASU. "SCALE ROBUST HEAD POSE ESTIMATION BASED ON RELATIVE HOMOGRAPHY TRANSFORMATION." New Mathematics and Natural Computation 10, no. 01 (March 2014): 69–90. http://dx.doi.org/10.1142/s1793005714500045.

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Head pose estimation has been widely studied in recent decades due to many significant applications. Different from most of the current methods which utilize face models to estimate head position, we develop a relative homography transformation based algorithm which is robust to the large scale change of the head. In the proposed method, salient Harris corners are detected on a face, and local binary pattern features are extracted around each of the corners. And then, relative homography transformation is calculated by using RANSAC optimization algorithm, which applies homography to a region of interest (ROI) on an image and calculates the transformation of a planar object moving in the scene relative to a virtual camera. By doing so, the face center initialized in the first frame will be tracked frame by frame. Meanwhile, a head shoulder model based Chamfer matching method is proposed to estimate the head centroid. With the face center and the detected head centroid, the head pose is estimated. The experiments show the effectiveness and robustness of the proposed algorithm.

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Seo, Sangwon, Soowoong Joeng, Yunsang Han, Jongsoo Choi, and Sangkeun Lee. "Efficient Homography Estimation for Panoramic Image Generation." Journal of the Institute of Electronics and Information Engineers 50, no. 8 (August 15, 2013): 215–24. http://dx.doi.org/10.5573/ieek.2013.50.8.215.

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