Relevant bibliographies by topics / Homography and homography decomposition

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Academic literature on the topic 'Homography and homography decomposition'

Author: Grafiati
Published: 3 February 2023

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Journal articles on the topic "Homography and homography decomposition"

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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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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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Dissertations / Theses on the topic "Homography and homography decomposition"

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Manerikar, Ninad. "Fusion de capteurs visuels-inertiels et estimation d'état pour la navigation des véhicules autonomes." Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4111.

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L’estimation précise de l’état du système est un problème fondamental pour la navigation des véhicules autonomes. Ceci est particulièrement important lorsque le véhicule navigue dans des environnements encombrés ou à proximité d’obstacles, afin d’effectuer la localisation, l’évitement d’obstacles, la cartographie de l’environnement, etc. Bien que plusieurs algorithmes aient été proposés dans le passé pour ce problème d’estimation d’état, ils impliquent généralement un seul capteur ou plusieurs du même type. Afin de pouvoir exploiter les propriétés de multiples capteurs dotés de caractéristiques différentes (tels que Camera, IMU, Lidar, etc.), les chercheurs de la communauté de vision et de contrôle ont mis au point des modèles mathématiques qui produisent des estimations locales précises (position, orientation, vitesse, etc.). En m’inspirant de cela, ma thèse se concentre sur le développement d’observateurs non-linéaires pour l’estimation d’état en exploitant les algorithmes classiques de type Riccati en mettant l’accent sur la fusion de capteurs visuels-inertiels. Dans le cadre de cette thèse, nous utilisons une suite de capteurs à faible coût composée d’une caméra monoculaire et d’une centrale inertielle. Dans le cadre de la vision monoculaire, nous faisons l’hypothèse que la cible est pratiquement plate. Bien que cette hypothèse soit restrictive, les solutions proposées sont pertinentes pour de nombreuses applications dans les domaines de la robotique aérienne, terrestre et sous-marine. Dans ce contexte, deux nouveaux observateurs non linéaires sont proposés, le premier pour l’estimation de l’homographie et le deuxième pour l’estimation de l’attitude partielle, de la vitesse linéaire et de la profondeur. Dans la deuxième partie de la thèse, deux nouveaux observateurs déterministes de Riccati sont proposés pour traiter le problème classique de décomposition d’homographie au lieu de le résoudre image par image comme les approches algébriques traditionnelles. Tous ces travaux sont publiés dans des conférences internationales de haut-niveau. Tous les observateurs proposés ci-dessus font partie de la bibliothèque HomographyLab dont je suis l’un des principaux contributeurs. Cette bibliothèque a été évaluée au niveau TRL 7 (Technology Readiness Level) et est protégée par l’APP (Agence pour la Protection des Programmes) qui sert de brique principale pour diverses applications telles que l’estimation de vitesse et de flux optique, et la stabilisation basée sur l’homographie visuelle
Accurate state estimation is a fundamental problem for the navigation of Autonomous vehicles. This is particularly important when the vehicle is navigating through cluttered environments or it has to navigate in close proximity to its physical surroundings in order to perform localization, obstacle avoidance, environmental mapping etc. Although several algorithms were proposed in the past for this problem of state estimtation, they were usually applied to a single sensor or a specific sensor suite. To this end, researchers in the computer vision and control community came up with a visual-inertial framework (Camera + Imu) that exploit the combined properties of this sensor suite to produce precise local estimates (position, orientation, velocity etc). Taking inspiration from this, my thesis focuses on developing nonlinear observers for State Estimation by exploiting the classical Riccati design framework with a particular emphasis on visual-inertial sensor fusion. In the context of this thesis, we use a suite of low-cost sensors consisting of a monocular camera and an IMU. Throughout the thesis, the assumption on the planarity of the visual target has been considered. In the present thesis, two research topics have been considered. Firstly, an extensive study for the existing techniques for homography estimation has been carried out after which a novel nonlinear observer on the SL(3) group has been proposed with application to optical flow estimation. The novelty lies in the linearization approach undertaken to linearize a nonlinear observer on SL(3), thus making it more simplistic and suitable for practical implementation. Then, another novel observer based on deterministic Ricatti observer has been proposed for the problem of partial attitude, linear velocity and depth estimation for planar targets. The proposed approach does not rely on the strong assumption that the IMU provides the measurements of the vehicle’s linear acceleration in the body-fixed frame. Again experimental validations have been carried out to show the performance of the observer. An extension to this observer has been further proposed to filter the noisy optical flow estimates obtained from the extraction of continuous homography. Secondly, two novel observers for tackling the classical problem of homography decomposition have been proposed. The key contribution here lies in the design of two deterministic Riccati observers for addressing the homography decomposition problem instead of solving it on a frame-by-frame basis like traditional algebraic approaches. The performance and robustness of the observers have been validated over simulations and practical experiments. All the observers proposed above are part of the Homography-Lab library that has been evaluated at the TRL 7 (Technology Readiness Level) and is protected by the French APP (Agency for the Protection of Programs) which serves as the main brick for various applications like velocity, optical flow estimation and visual homography based stabilization
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DiMascio, Michelle Augustine. "Convolutional Neural Network Optimization for Homography Estimation." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544214038882564.

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Zeng, Rui. "Homography estimation: From geometry to deep learning." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134132/1/Rui_Zeng_Thesis.pdf.

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Homography is an important area of computer vision for scene understanding and plays a key role in extracting relationships across different viewpoints of a scene. This thesis focuses on studying homography transformations between images from both geometric and deep learning perspectives. We have developed an accurate and effective homography estimation system for sports scenes analysis an efficient and novel 3D perspective feature to improve 3D object recognition especially for the vehicle recognition.
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Turk, Matthew Robert. "A homography-based multiple-camera person-tracking algorithm /." Online version of thesis, 2008. http://hdl.handle.net/1850/7853.

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Graham, Athelia. "The Effects of Homography on Computer-generated High Frequency Word Lists." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2692.pdf.

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Fristedt, Hampus. "Homography Estimation using Deep Learning for Registering All-22 Football Video Frames." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583.

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Homography estimation is a fundamental task in many computer vision applications, but many techniques for estimation rely on complicated feature extraction pipelines. We extend research in direct homography estimation (i.e. without explicit feature extraction) by implementing a convolutional network capable of estimating homographies. Previous work in deep learning based homography estimation calculates homographies between pairs of images, whereas our network takes single image input and registers it to a reference view where no image data is available. The application of the work is registering frames from American football video to a top-down view of the field. Our model manages to register frames in a test set with an average corner error equivalent to less than 2 yards.
Homografiuppskattning är ett förkrav för många problem inom datorseende, men många tekniker för att uppskatta homografier bygger på komplicerade processer för att extrahera särdrag mellan bilderna. Vi bygger på tidigare forskning inom direkt homografiuppskattning (alltså, utan att explicit extrahera särdrag) genom att  implementera ett Convolutional Neural Network (CNN) kapabelt av att direkt uppskatta homografier. Arbetet tillämpas för att registrera bilder från video av amerikansk fotball till en referensvy av fotbollsplanen. Vår modell registrerar bildramer från ett testset till referensvyn med ett snittfel i bildens hörn ekvivalent med knappt 2 yards.
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Ähdel, Victor. "On the effect of architecture on deep learning based features for homography estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233194.

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Keypoint detection and description is the first step of homography and essential matrix estimation, which in turn is used in Visual Odometry and Visual SLAM. This work explores the effect (in terms of speed and accuracy) of using different deep learning architectures for such keypoints. The fully convolutional networks — with heads for both the detector and descriptor — are trained through an existing self-supervised method, where correspondences are obtained through known randomly sampled homographies. A new strategy for choosing negative correspondences for the descriptor loss is presented, which enables more flexibility in the architecture design. The new strategy turns out to be essential as it enables networks that outperform the learnt baseline at no cost in inference time. Varying the model size leads to a trade-off in speed and accuracy, and while all models outperform ORB in homography estimation, only the larger models approach SIFT’s performance; performing about 1-7% worse. Training for longer and with additional types of data might give the push needed to outperform SIFT. While the smallest models are 3× faster and use 50× fewer parameters than the learnt baseline, they still require 3× as much time as SIFT while performing about 10-30% worse. However, there is still room for improvement through optimization methods that go beyond architecture modification, e.g. quantization, which might make the method faster than SIFT.
Nyckelpunkts-detektion och deskriptor-skapande är det första steget av homografi och essentiell matris estimering, vilket i sin tur används inom Visuell Odometri och Visuell SLAM. Det här arbetet utforskar effekten (i form av snabbhet och exakthet) av användandet av olika djupinlärnings-arkitekturer för sådana nyckelpunkter. De hel-faltade nätverken – med huvuden för både detektorn och deskriptorn – tränas genom en existerande själv-handledd metod, där korrespondenser fås genom kända slumpmässigt valda homografier. En ny strategi för valet av negativa korrespondenser för deskriptorns träning presenteras, vilket möjliggör mer flexibilitet i designen av arkitektur. Den nya strategin visar sig vara väsentlig då den möjliggör nätverk som presterar bättre än den lärda baslinjen utan någon kostnad i inferenstid. Varieringen av modellstorleken leder till en kompromiss mellan snabbhet och exakthet, och medan alla modellerna presterar bättre än ORB i homografi-estimering, så är det endast de större modellerna som närmar sig SIFTs prestanda; där de presterar 1-7% sämre. Att träna längre och med ytterligare typer av data kanske ger tillräcklig förbättring för att prestera bättre än SIFT. Även fast de minsta modellerna är 3× snabbare och använder 50× färre parametrar än den lärda baslinjen, så kräver de fortfarande 3× så mycket tid som SIFT medan de presterar runt 10-30% sämre. Men det finns fortfarande utrymme för förbättring genom optimeringsmetoder som övergränsar ändringar av arkitekturen, som till exempel kvantisering, vilket skulle kunna göra metoden snabbare än SIFT.
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Kio, Onoise Gerald. "Distortion correction for non-planar deformable projection displays through homography shaping and projected image warping." Thesis, University of Central Lancashire, 2016. http://clok.uclan.ac.uk/16569/.

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Video projectors have advanced from being tools for only delivering presentations on flat or planar surfaces to tools for delivering media content in such applications as augmented reality, simulated sports practice and invisible displays. With the use of non-planar surfaces for projection comes geometric and radiometric distortions. This work dwells on correcting geometric distortions occurring when images or video frames are projected onto static and deformable non-planar display surfaces. The distortion-correction process involves (i) detecting feature points from the camera images and creating a desired shape of the undistorted view through a 2D homography, (ii) transforming the feature points on the camera images to control points on the projected images, (iii) calculating Radial Basis Function (RBF) warping coefficients from the control points, and warping the projected image to obtain an undistorted image of the projection on the projection surface. Several novel aspects of this work have emerged and include (i) developing a theoretical framework that explains the cause of distortion and provides a general warping pattern to be applied to the projection, (ii) carrying out the distortion-correction process without the use of a distortion-measuring calibration image or structured light pattern, (iii) carrying out the distortioncorrection process on a projection display that deforms with time with a single uncalibrated projector and uncalibrated camera, and (iv) performing an optimisation of the distortioncorrection processes to operate in real-time. The geometric distortion correction process designed in this work has been tested for both static projection systems in which the components remain fixed in position, and dynamic projection systems in which the positions of components or shape of the display change with time. The results of these tests show that the geometric distortion-correction technique developed in this work improves the observed image geometry by as much as 31% based on normalised correlation measure. The optimisation of the distortion-correction process resulted in a 98% improvement of its speed of operation thereby demonstrating the applicability of the proposed approach to real projection systems with deformable projection displays.
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Bazargani, Hamid. "Real-Time Recognition of Planar Targets on Mobile Devices. A Framework for Fast and Robust Homography Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31698.

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The present thesis is concerned with the problem of robust pose estimation for planar targets in the context of real-time mobile vision. As a consequence of this research, individual developments made in isolation by earlier researchers are here considered together. Several adaptations to the existing algorithms are undertaken yielding a unified framework for robust pose estimation. This framework is specifically designed to meet the growing demand for fast and robust estimation on power-constrained platforms. For robust recognition of targets at very low computational costs, we employ feature based methods which are based on local binary descriptors allowing fast feature matching at run-time. The matching set is then fed to a robust parameter estimation algorithm in order to obtain a reliable homography. On the basis of our experimental results, it can be concluded that reliable homography estimates can be obtained using a device-friendly implementation of the Gaussian Elimination algorithm. We also show in this thesis that our simplified approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. The author's attention is focused not only on developing fast algorithms for the recognition framework but also on the optimized implementation of such algorithms. Any other recognition framework would similarly benefit from our optimized implementation.
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Xu, Jun [Verfasser]. "A Textile Fabric Protruding Fibers Measurement System Based on Camera Vision and Variable Homography / Jun Xu." Düren : Shaker, 2019. http://d-nb.info/119052600X/34.

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Books on the topic "Homography and homography decomposition"

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Eves, Howard, and Roland Deaux. Introduction to the Geometry of Complex Numbers. Dover Publications, Incorporated, 2013.

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Introduction to the geometry of complex numbers. Mineola, N.Y., USA: Dover, 2008.

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Deaux, Roland. Introduction to the Geometry of Complex Numbers. Dover Publications, Incorporated, 2013.

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Book chapters on the topic "Homography and homography decomposition"

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Kanatani, Kenichi, Yasuyuki Sugaya, and Yasushi Kanazawa. "Homography Computation." In Guide to 3D Vision Computation, 81–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48493-8_6.

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Zhang, Beiwei, and Y. F. Li. "Homography-Based Dynamic Calibration." In Intelligent Systems, Control and Automation: Science and Engineering, 57–91. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2654-3_4.

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Zhang, Jirong, Chuan Wang, Shuaicheng Liu, Lanpeng Jia, Nianjin Ye, Jue Wang, Ji Zhou, and Jian Sun. "Content-Aware Unsupervised Deep Homography Estimation." In Computer Vision – ECCV 2020, 653–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58452-8_38.

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Xiang, Tian-Zhu, Gui-Song Xia, and Liangpei Zhang. "Image Stitching Using Smoothly Planar Homography." In Pattern Recognition and Computer Vision, 524–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03398-9_45.

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Bimbo, Alberto Del, Fernando Franco, and Federico Pernici. "Local Homography Estimation Using Keypoint Descriptors." In Lecture Notes in Electrical Engineering, 203–17. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3831-1_12.

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Sakamoto, Masatoshi, Yasuyuki Sugaya, and Kenichi Kanatani. "Homography Optimization for Consistent Circular Panorama Generation." In Advances in Image and Video Technology, 1195–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_121.

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Loan, Ton Thi Kim, Xuan-Qui Pham, Huu-Quoc Nguyen, Nguyen Dao Tan Tri, Ngo Quang Thai, and Eui-Nam Huh. "Homography-Based Motion Detection in Screen Content." In Advances in Computer Science and Ubiquitous Computing, 875–81. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_122.

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Nam, Siwook, Hanjoo Kim, and Jaihie Kim. "Trajectory Estimation Based on Globally Consistent Homography." In Computer Analysis of Images and Patterns, 214–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_27.

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Wang, Xiang, Chen Wang, Xiao Bai, Yun Liu, and Jun Zhou. "Deep Homography Estimation with Pairwise Invertibility Constraint." In Lecture Notes in Computer Science, 204–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97785-0_20.

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Yang, Xiaohang, Lingtong Kong, Ziyun Liang, and Jie Yang. "Homography Estimation Network Based on Dense Correspondence." In Communications in Computer and Information Science, 632–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92310-5_73.

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Conference papers on the topic "Homography and homography decomposition"

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Manerikar, Ninad, Minh-Duc Hua, Simone De Marco, and Tarek Hamel. "Riccati observer design for homography decomposition." In 2020 European Control Conference (ECC). IEEE, 2020. http://dx.doi.org/10.23919/ecc51009.2020.9143740.

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Ozuag, Ersin, and Sarp Erturk. "A homography matrix decomposition based video synchronization approach." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830661.

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Liu, He, Hiroyuki Hase, and Shogo Tokai. "Deeper Understanding on Solution Ambiguity by Homography Decomposition." In Robotics and Applications. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.740-004.

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Paliwal, Pinak, and Vikas Paliwal. "3D Scene Angles using UL Decomposition of Planar Homography." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00230.

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Zhang Xuebo, Fang Yongchun, Ma Bojun, Liu Xi, and Zhang Ming. "A fast homography decomposition technique for visual servo of mobile robots." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605543.

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Kukelova, Zuzana, Jan Heller, Martin Bujnak, and Tomas Pajdla. "Radial distortion homography." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298663.

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Liu, Shaoguo, Haibo Wang, Jixia Zhang, Franck Davoine, and Chunhong Pan. "Softferns for homography estimation." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116288.

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Cao, Si-Yuan, Jianxin Hu, Zehua Sheng, and Hui-Liang Shen. "Iterative Deep Homography Estimation." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00192.

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Lingrand, D. "Particular Forms of Homography Matrices." In British Machine Vision Conference 2000. British Machine Vision Association, 2000. http://dx.doi.org/10.5244/c.14.60.

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Jain, Paresh Kumar. "Homography Estimation from Planar Contours." In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). IEEE, 2006. http://dx.doi.org/10.1109/3dpvt.2006.77.

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Reports on the topic "Homography and homography decomposition"

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Gonzales, Antonio, Cara Monical, and Tony Perkins. HSolo: Homography from a single affine aware correspondence. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1663258.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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Abstract:
The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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