Relevant bibliographies by topics / Homography constraint

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Homography constraint'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Homography constraint"

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Jiang, Hai, Haipeng Li, Yuhang Lu, Songchen Han, and Shuaicheng Liu. "Semi-supervised Deep Large-Baseline Homography Estimation with Progressive Equivalence Constraint." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1024–32. http://dx.doi.org/10.1609/aaai.v37i1.25183.

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Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.
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Chen, Shengpeng, Wenyi Yang, Wei Wang, Jianting Mai, Jian Liang, and Xiaohu Zhang. "Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network." Sensors 24, no. 6 (2024): 1828. http://dx.doi.org/10.3390/s24061828.

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Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.
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Seo, Dong-Wook, Hyun-Uk Chae, Byeong-Woo Kim, Won-Ho Choi, and Kang-Hyun Jo. "Human Tracking based on Multiple View Homography." JUCS - Journal of Universal Computer Science 15, no. (13) (2009): 2463–84. https://doi.org/10.3217/jucs-015-13-2463.

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We propose a method for detection and tracking for objects under multiple cameras system. To track objects, one need to establish correspondence objects among multiple views. We apply the principal axis of objects and the homography constraint to match objects across multiple cameras. The principal axis belongs to the silhouette of objects that is extracted by the background subtraction. We use the multiple background model to the background subtraction. In an image sequence, many changes happen with respect to pixel intensity. This cannot be characterized by the single background model so that is necessary to use the multiple background model. Also, we use the median background model reducing some noises. The silhouette is detected by difference with background models and current image which includes moving objects. For calculating homography, we use landmarks on the ground plane in 3D space. The homography means the relation between two correspondence between two coinciding points from different views. The intersection of principal axes and ground plane in 3D space are the same point shown in each view. The intersection occurs when a principal axis in an image crosses to the transformed ground plane from another image. We construct the correspondence which means the relationship between intersection in current image and transformed intersection from the other image by homography constraint. Those correspondences confirm within a short distance measuring in the top viewed plane. Thus, we track a person by these corresponding points on the ground plane.
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Zhu, Haijiang, Xin Wen, Fan Zhang, Xuejing Wang, and Guanghui Wang. "Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement." IEEE Access 6 (2018): 28680–90. http://dx.doi.org/10.1109/access.2018.2837639.

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Liu, Junyuan, Ao Liang, Enbo Zhao, Mingqi Pang, and Daijun Zhang. "Homography Matrix-Based Local Motion Consistent Matching for Remote Sensing Images." Remote Sensing 15, no. 13 (2023): 3379. http://dx.doi.org/10.3390/rs15133379.

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Feature matching is a fundamental task in the field of image processing, aimed at ensuring correct correspondence between two sets of features. Putative matches constructed based on the similarity of descriptors always contain a large number of false matches. To eliminate these false matches, we propose a remote sensing image feature matching method called LMC (local motion consistency), where local motion consistency refers to the property that adjacent correct matches have the same motion. The core idea of LMC is to find neighborhoods with correct motion trends and retain matches with the same motion. To achieve this, we design a local geometric constraint using a homography matrix to represent local motion consistency. This constraint has projective invariance and is applicable to various types of transformations. To avoid outliers affecting the search for neighborhoods with correct motion, we introduce a resampling method to construct neighborhoods. Moreover, we design a jump-out mechanism to exit the loop without searching all possible cases, thereby reducing runtime. LMC can process over 1000 putative matches within 100 ms. Experimental evaluations on diverse image datasets, including SUIRD, RS, and DTU, demonstrate that LMC achieves a higher F-score and superior overall matching performance compared to state-of-the-art methods.
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Wu, Ruo, Kun Wang, and Jiquan Ma. "Feature Points Matching Algorithm based on Homography Constraint and Gray Scale Truncation Number." Journal of Physics: Conference Series 1229 (May 2019): 012049. http://dx.doi.org/10.1088/1742-6596/1229/1/012049.

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Yue, Yi, Tong Fang, Wen Li, et al. "Hierarchical Edge-Preserving Dense Matching by Exploiting Reliably Matched Line Segments." Remote Sensing 15, no. 17 (2023): 4311. http://dx.doi.org/10.3390/rs15174311.

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Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.
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Chuang, Hsiu-Min, Tytus Wojtara, Niklas Bergström, and Akio Namiki. "Velocity Estimation for UAVs by Using High-Speed Vision." Journal of Robotics and Mechatronics 30, no. 3 (2018): 363–72. http://dx.doi.org/10.20965/jrm.2018.p0363.

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In recent years, applications of high-speed visual systems have been well developed because of their high environmental recognition ability. These system help to improve the maneuverability of unmanned aerial vehicles (UAVs). Thus, we herein propose a high-speed visual unit for UAVs. The unit is lightweight and compact, consisting of a 500 Hz high-speed camera and a graphic processing unit. We also propose an improved UAV velocity estimation algorithm using optical flows and a continuous homography constraint. By using the high-frequency sampling rate of the high-speed vision unit, the estimation accuracy is improved. The operation of our high-speed visual unit is verified in the experiments.
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Chen, Yifu, Yuan Le, Lin Wu, et al. "Weak-Texture Seafloor and Land Image Matching Using Homography-Based Motion Statistics with Epipolar Geometry." Remote Sensing 16, no. 14 (2024): 2683. http://dx.doi.org/10.3390/rs16142683.

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The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and this results in matching-extraction failure. To address this issue, a novel method, homography-based motion statistics with an epipolar constraint (HMSEC), is proposed to improve the number, reliability, and robustness of matching points for weak-textured seafloor images. In the matching process of HMSEC, a large number of reliable matching points can be identified from the preliminary matching points based on the motion smoothness assumption and motion statistics. Homography and epipolar geometry are also used to estimate the scale and rotation influences of each matching point in image pairs. The results show that the matching-point numbers for the seafloor and land regions can be significantly improved. In this study, we evaluated this method for the areas of Zhaoshu Island, Ganquan Island, and Lingyang Reef and compared the results to those of the grid-based motion statistics (GMS) method. The increment of matching points reached 2672, 2767, and 1346, respectively. In addition, the seafloor matching points had a wider distribution and reached greater water depths of −11.66, −14.06, and −9.61 m. These results indicate that the proposed method could significantly improve the number and reliability of matching points for seafloor images.
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Jia, Songmin, Ke Wang, and Xiuzhi Li. "Mobile Robot Simultaneous Localization and Mapping Based on a Monocular Camera." Journal of Robotics 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7630340.

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This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) algorithm for mobile robot. In this proposed method, the tracking and mapping procedures are split into two separate tasks and performed in parallel threads. In the tracking thread, a ground feature-based pose estimation method is employed to initialize the algorithm for the constraint moving of the mobile robot. And an initial map is built by triangulating the matched features for further tracking procedure. In the mapping thread, an epipolar searching procedure is utilized for finding the matching features. A homography-based outlier rejection method is adopted for rejecting the mismatched features. The indoor experimental results demonstrate that the proposed algorithm has a great performance on map building and verify the feasibility and effectiveness of the proposed algorithm.
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Dissertations / Theses on the topic "Homography constraint"

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Tippetts, Beau J. "Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1418.

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A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.
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Szpak, Zygmunt Ladyslaw. "Constrained parameter estimation in multiple view geometry." Thesis, 2013. http://hdl.handle.net/2440/82702.

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Multiple view geometry is a branch of computer vision devoted entirely to the study of the relationship between images generated from a fixed three-dimensional scene. Thanks to the body of knowledge generated in this domain some of the most exciting developments in navigation have recently been realised. Google's release of Street-view maps is the most remarkable example. Currently there is a growing demand for new insight and knowledge originating from multiple view geometry, as two of the most popular technological companies, Google and Apple, embark on a mission to generate three-dimensional maps. The research conducted in this thesis makes a direct contribution to two specific problems that arise frequently in the context of multiple view geometry: homography estimation and ellipse fitting. A homography is used to establish a relationship between two images of a scene, whenever the scene consists of a flat surface. If the scene consists of several at surfaces, such as walls of buildings in urban environments, then multiple homographies are required to adequately represent the relationship between a pair of images. But when multiple homographies are required, computer vision practitioners typically estimate homographies separately. This thesis demonstrates that multiple homographies must not be estimated separately, because additional interhomography constraints need to be satisfied in order for a collection of homographies to accurately reflect the three-dimensional geometry of the scene. This thesis offers a comprehensive account of a variety of subtleties that arise in the estimation of multiple homographies, and presents detailed novel algorithms for fulfilling the estimation task. A central contribution is the development of a new framework for jointly estimating multiple homographies. The new framework leads to considerably more accurate homography estimates than previous approaches. The second major contribution of this thesis relates to another frequently encountered task in multiple view geometry: ellipse fitting. Recently many new cost functions promising unbiasedness, consistency or hyperaccuracy have been reported to improve the state-of-the-art in fitting ellipses to data. Unfortunately, the new cost functions have not been substantiated with thorough experimental comparisons. This thesis offers an extensive evaluation of both new and old ellipse fitting methods with the aid of comprehensive simulations. The findings suggest that there is not much difference between the newer and more established estimators. There is, however, a significant difference between the sole estimator that guarantees an ellipse fit, and other estimators which are prone to occasionally producing hyperbolas. The estimator that guarantees an ellipse fit is significantly less accurate. To remedy this undesirable discovery, a new ellipse estimator is proposed that shares a similar statistical accuracy to the unbiased, consistent or hyper-accurate estimators, but unlike all of these, still guarantees an ellipse fit.<br>Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013
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Book chapters on the topic "Homography constraint"

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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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97785-0_20.

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Monnin, David, Etienne Bieber, Gwenaél Schmitt, and Armin Schneider. "An Effective Rigidity Constraint for Improving RANSAC in Homography Estimation." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17691-3_19.

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Khan, Saad M., and Mubarak Shah. "A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744085_11.

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Chojnacki, Wojciech, and Zygmunt L. Szpak. "Full Explicit Consistency Constraints in Uncalibrated Multiple Homography Estimation." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_41.

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Guan, Banglei, Zhang Li, Qifeng Yu, Yang Shang, and Xiaolin Liu. "Structure from Motion Using Homography Constraints for Sequential Aerial Imagery." In 6th International Symposium of Space Optical Instruments and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56488-9_4.

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He, Qiang, and Chee-hung Henry Chu. "Planar Surface Detection in Image Pairs Using Homographic Constraints." In Advances in Visual Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919476_3.

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Conference papers on the topic "Homography constraint"

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Nakano, Gaku. "Algebraic Constraint for Preserving Convexity of Planar Homography." In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. http://dx.doi.org/10.1109/3dv53792.2021.00023.

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"ROBUST OCCLUSION HANDLING WITH MULTIPLE CAMERAS USING A HOMOGRAPHY CONSTRAINT." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001804605600565.

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Zhai, You, Guangjun Yu, Hongyun Wang, and Xiwei Guo. "Image matching for structured scenes based on ASIFT and homography constraint." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322914.

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Kwolek, Bogdan. "Multi Camera-Based Person Tracking Using Region Covariance and Homography Constraint." In 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2010. http://dx.doi.org/10.1109/avss.2010.20.

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Tong, Xiaomin, Tao Yang, Runping Xi, Dapei Shao, and Xiuwei Zhang. "A Novel Multi-planar Homography Constraint Algorithm for Robust Multi-people Location with Severe Occlusion." In 2009 International Conference on Image and Graphics (ICIG). IEEE, 2009. http://dx.doi.org/10.1109/icig.2009.90.

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Herbon, Christopher, Gabriel Schumann, Klaus-Dietz Tonnies, and Bernd Stock. "Detection and Segmentation of Quasi-Planar Surfaces Through Expectation Maximization Under a Planar Homography Constraint." In 2015 12th Conference on Computer and Robot Vision (CRV). IEEE, 2015. http://dx.doi.org/10.1109/crv.2015.19.

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Hirao, Yuto, and Hiroshi Kawasaki. "Simultaneous independent information display at multiple depths using multiple projectors and patterns created by epipolar constraint and homography transformation." In VRST '18: 24th ACM Symposium on Virtual Reality Software and Technology. ACM, 2018. http://dx.doi.org/10.1145/3281505.3281612.

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Jadhav, Akash. "Multi-View Human Tracking and 3D Localization in Retail." In 3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121214.

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In recent years, retail stores have seen traction in bringing online shopping experience to offline stores via autonomous checkouts. Autonomous checkouts is a computer vision-based technology that needs to understand three human elements within the store: who, where, and doing what. This paper addresses two of the three elements: who and where. It presents an approach to track and localize humans in a multi-view camera system. Traditional methods have limitations as they: (1) fail to overcome substantial occlusion of humans; (2) suffer a lengthy processing time; (3) require a planar homography constraint between camera frames; (4) suffer swapping of labels assigned to a human. The proposed method in this paper handles all the aforementioned limitations. The key idea is to use a hierarchical association model for tracking, which uses each human's clothing features, human pose orientation, and relative depth of joints, and runs at over 23fps.
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Yuping Shen, Nazim Ashraf, and Hassan Foroosh. "Action recognition based on homography constraints." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761439.

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Chojnacki, Wojciech, Zygmunt L. Szpak, Michael J. Brooks, and Anton van den Hengel. "Multiple Homography Estimation with Full Consistency Constraints." In 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2010. http://dx.doi.org/10.1109/dicta.2010.87.

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