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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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Cao, Chenguang. "Research on a Visual Servoing Control Method Based on Perspective Transformation under Spatial Constraint." Machines 10, no. 11 (2022): 1090. http://dx.doi.org/10.3390/machines10111090.
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Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method has difficulty performing the task. The present work describes space constraint challenges in a visual servoing system by introducing an assembly node and then presents a two-stage visual servoing control approach based on perspective transformation. A virtual image plane is constructed using a calibration-derived homography matrix. The assembly node, as well as other objects, are projected into the plane after that. Second, the controller drives the robotic arm by tracking the projections in the virtual image plane and adjusting the position and attitude of the workpiece accordingly. Three simple image features are combined into a composite image feature, and an active disturbance rejection controller (ADRC) is established to improve the robotic arm’s motion sensitivity. Real-time simulations and experiments employing a robotic vision system with an eye-to-hand configuration are used to validate the effectiveness of the presented method. The results show that the robotic arm can move the workpiece to the desired position without using coordinates.
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Ya-Nan Li, Ya-Nan Li, Zhen-Feng Zhang Ya-Nan Li, Yi-Fan Chen Zhen-Feng Zhang, and Chu-Hua Huang Yi-Fan Chen. "Feature Fusion Method for Low-Illumination Images." 電腦學刊 33, no. 6 (2022): 167–80. http://dx.doi.org/10.53106/199115992022123306014.
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<p>Aiming at the problem of inaccurate feature extraction of low illumination images, a method is proposed that fuses Scale Invariant Feature Transform into SuperPoint. Firstly, the low illumination image is light-enhanced. Secondly, SuperPoint and SIFT features are fused at feature map level, changing the deep neural network weight by labeling the prob output of the network with the SIFT of input image as the maximum of the current prob at pixel-level. Finally, the loss function is constructed based on homography transformation, its principle between image pairs is used to realize the constraint on network parameters. The training and evaluation are conducted on ExDark dataset, tests and comparisons are conducted on multiple indicators of SOTA on HPatches common dataset. The experimental results show that our method improves the precision and recall than SuperPoint, and performs well in multiple evaluation indicators.</p> <p>&nbsp;</p>
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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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Li, Xia, Bin Zhang, Hongying Zhang, Ronghua Xu, and Yalei Bai. "Research on solving heading attitude of airdrop cargo platform based on line features." International Journal of Advanced Robotic Systems 19, no. 3 (2022): 172988062210816. http://dx.doi.org/10.1177/17298806221081643.
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The present study envisages the development of an improved line features method to accurately estimate the attitude of the airdrop cargo platform during airdrop landing. Therefore, this article uses the geometric characteristics of the line features to improve the traditional line features extraction and removes the locally dense line features in the image, which greatly reduces the number of line features in the image. Then, the improved random sample consensus is used to remove the mismatching of line features, which improves the real-time performance of the algorithm and the accuracy of the attitude angle, and makes up for the problem of difficult extraction of point features or low matching accuracy in the airdrop environment. Finally, a constraint equation is established for the line features that are successfully matched, and using homography to obtain attitude of the airdrop cargo platform. This article also meets the requirements of accurate calculation attitude of airdrop cargo platform. The experiment shows the significance and feasibility of the airdrop cargo platform heading and attitude calculation technology based on the line feature, and it has a good application prospect.
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Zhang, Xingguo, Xinyu Shi, Xiaoyue Luo, Yinping Sun, and Yingdi Zhou. "Real-Time Web Map Construction Based on Multiple Cameras and GIS." ISPRS International Journal of Geo-Information 10, no. 12 (2021): 803. http://dx.doi.org/10.3390/ijgi10120803.
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Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers.
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Yan, Yuheng, Yiqiu Liang, Zihan Zhou, Bin Jiang, and Jian Xiao. "FastQR: Fast Pose Estimation of Objects Based on Multiple QR Codes and Monocular Vision in Mobile Embedded Devices." Wireless Communications and Mobile Computing 2021 (September 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/9481190.
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In recent years, the pose estimation of objects has become a research hotspot. This technique can effectively estimate the pose changes of objects in space and is widely used in many mobile devices, such as AR/VR. At present, mainstream technologies can achieve high-precision pose estimation, but the problem of that of multiple irregular objects in mobile and embedded devices under limited resource conditions is still challenging. In this paper, we propose a FastQR algorithm that can estimate the pose of multiple irregular objects on Renesas by utilizing homography method to solve the transformation matrix of a single QR code and then establish the spatial constraint relationship between multiple QR codes to estimate the posture of irregular objects. Our algorithm obtained a competitive result in simulation and verification on the RZ/A2M development board of Renesas. Moreover, the verification results show that our method can estimate the spatial pose of the multiobject accurately and robustly in distributed embedded devices. The average frame rate calculated on the RZ/A2M can reach 28 fps, which is at least 37 times faster than that of other pose estimation methods.
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Li, Litao, Jiayang Cao, Shaodong Wei, Yonghua Jiang, and Xin Shen. "Improved On-Orbit MTF Measurement Method Based on Point Source Arrays." Remote Sensing 15, no. 16 (2023): 4028. http://dx.doi.org/10.3390/rs15164028.
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The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing satellites (e.g., Pleiades) using point sources. However, current point source methods mainly use 2D Gaussian functions to fit the discrete digital number (DN) of the point source on the image to extract the center of the point source and fit the PSF after encrypting multiple point sources; thus, noise robustness is poor and measurement accuracy varies widely. In this study, we developed a noise-resistant on-orbit MTF detection method based on the object space constraint among point source arrays. Utilizing object space constraint relationships among points in a point source array, a homography transformation model was established, enabling accurate extraction of sub-pixel coordinates for each point source response. Subsequently, aligning the luminosity distribution of all point sources concerning a reference point source, the encrypted PSF was obtained and then fitted to obtain the MTF. To validate the method, Gaofen-2 (GF-2) satellite images were used to conduct an in-orbit imaging experiment on the point source array of the Chinese Zhongwei remote sensing satellite calibration site. Compared with the Gaussian model methods, the proposed method yielded more accurate peak positions for each point source. Standard deviations of peak position constant ratios in along- and cross-track directions improved by 2.8 and 4.8 times, respectively. The root-mean-square error (RMSE) of the collinearity test results increased by 92%, and the noise resistance of the MTF curve improved by two times. Dynamic MTF values at the Nyquist frequency for the GF-2 panchromatic band in along- and cross-track directions were 0.0476 and 0.0705, respectively, and MTF values in different directions were well distinguished.
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Dai, Jun, Chunfeng Zhang, Songlin Liu, Xiangyang Hao, Zongbin Ren, and Yunzhu Lv. "GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments." Applied Sciences 14, no. 1 (2024): 455. http://dx.doi.org/10.3390/app14010455.
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Autonomous navigation and localization are the foundations of unmanned intelligent systems, therefore, continuous, stable, and reliable position services in unknown environments are especially important for autonomous navigation and localization. Aiming at the problem where GNSS cannot continuously localize in complex environments due to weak signals, poor penetration ability, and susceptibility to interference and that visual navigation and localization are only relative, this paper proposes a GNSS-aided visual dynamic localization method that can provide global localization services in unknown environments. Taking the three frames of images and their corresponding GNSS coordinates as the constraint data, the GNSS coordinate system and world coordinate system transformation matrix are obtained through horn coordinate transformation, and the relative positions of the subsequent image sequences in the world coordinate system are obtained through epipolar geometry constraints, homography matrix transformations, and 2D–3D position and orientation solving, which ultimately yields the global position data of unmanned carriers in GNSS coordinate systems when GNSS is temporarily unavailable. Both the dataset validation and measured data validation showed that the GNSS initial-assisted positioning algorithm could be applied to situations where intermittent GNSS signals exist, and it can provide global positioning coordinates with high positioning accuracy in a short period of time; however, the algorithm would drift when used for a long period of time. We further compared the errors of the GNSS initial-assisted positioning and GNSS continuous-assisted positioning systems, and the results showed that the accuracy of the GNSS continuous-assisted positioning system was two to three times better than that of the GNSS initial-assisted positioning system, which proved that the GNSS continuous-assisted positioning algorithm could maintain positioning accuracy for a long time and it had good reliability and applicability in unknown environments.
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Li, Yunwu, Xiaojuan Wang, and Dexiong Liu. "3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision." Journal of Sensors 2019 (March 3, 2019): 1–16. http://dx.doi.org/10.1155/2019/6832109.
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This paper proposes a 3D autonomous navigation line extraction method for field roads in hilly regions based on a low-cost binocular vision system. Accurate guide path detection of field roads is a prerequisite for the automatic driving of agricultural machines. First, considering the lack of lane lines, blurred boundaries, and complex surroundings of field roads in hilly regions, a modified image processing method was established to strengthen shadow identification and information fusion to better distinguish the road area from its surroundings. Second, based on nonobvious shape characteristics and small differences in the gray values of the field roads inside the image, the centroid points of the road area as its statistical feature was extracted and smoothed and then used as the geometric primitives of stereo matching. Finally, an epipolar constraint and a homography matrix were applied for accurate matching and 3D reconstruction to obtain the autonomous navigation line of the field roads. Experiments on the automatic driving of a carrier on field roads showed that on straight roads, multicurvature complex roads and undulating roads, the mean deviations between the actual midline of the road and the automatically traveled trajectory were 0.031 m, 0.069 m, and 0.105 m, respectively, with maximum deviations of 0.133, 0.195 m, and 0.216 m, respectively. These test results demonstrate that the proposed method is feasible for road identification and 3D navigation line acquisition.
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Goswami, Mayank. "A.I. Pipeline for Accurate Retinal Layer Segmentation Using OCT 3D Images." Photonics 10, no. 3 (2023): 275. http://dx.doi.org/10.3390/photonics10030275.
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An image data set from a multi-spectral animal imaging system was used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical and A.I.-based algorithms, separately and in combination, were tested for each task to determine their compatibility with data from the combined animal imaging system. The hybridization of A.I. with optical flow followed by homography transformation was shown to be effective (correlation value > 0.7) for registration. Resnet50 backbone was shown to be more effective than the famous U-net model for shadow region detection with a loss value of 0.9. A simple-to-implement analytical equation was shown to be effective for brightness manipulation with a 1% increment in mean pixel values and a 77% decrease in the number of zeros. The proposed equation allows the formulation of a constraint optimization problem using a controlling factor α for the minimization of the number of zeros, the standard deviation of the pixel values, and maximizing the mean pixel value. For layer segmentation, the standard U-net model was used. The A.I.-Pipeline consists of CNN, optical flow, RCNN, a pixel manipulation model, and U-net models in sequence. The thickness estimation process had a 6% error compared with manually annotated standard data.
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He, Haiqing, Jing Yu, Penggen Cheng, et al. "Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping." Remote Sensing 14, no. 1 (2021): 50. http://dx.doi.org/10.3390/rs14010050.
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Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.
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Zhang, Qing, and Wei Xiang. "Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking." Applied Sciences 13, no. 9 (2023): 5359. http://dx.doi.org/10.3390/app13095359.
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Object tracking requires heterogeneous images that are well registered in advance, with cross-modal image registration used to transform images of the same scene generated by different sensors into the same coordinate system. Infrared and visible light sensors are the most widely used in environmental perception; however, misaligned pixel coordinates in cross-modal images remain a challenge in practical applications of the object tracking task. Traditional feature-based approaches can only be applied in single-mode scenarios, and cannot be well extended to cross-modal scenarios. Recent deep learning technology employs neural networks with large parameter scales for prediction of feature points for image registration. However, supervised learning methods require numerous manually aligned images for model training, leading to the scalability and adaptivity problems. The Unsupervised Deep Homography Network (UDHN) applies Mean Absolute Error (MAE) metrics for cost function computation without labelled images; however, it is currently inapplicable for cross-modal image registration. In this paper, we propose aligning infrared and visible images using a rasterized parameter prediction algorithm with similarity measurement evaluation. Specifically, we use Cost Volume (CV) to predict registration parameters from coarse-grained to fine-grained layers with a raster constraint for multimodal feature fusion. In addition, motivated by the utilization of mutual information in contrastive learning, we apply a cross-modal similarity measurement algorithm for semi-supervised image registration. Our proposed method achieves state-of-the-art performance on the MS-COCO and FLIR datasets.
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Zhu, Changwei, Chujie Wu, Yanzhou Li, Shanshan Hu, and Haibo Gong. "Spatial Location of Sugarcane Node for Binocular Vision-Based Harvesting Robots Based on Improved YOLOv4." Applied Sciences 12, no. 6 (2022): 3088. http://dx.doi.org/10.3390/app12063088.
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Spatial location of sugarcane nodes using robots in agricultural conditions is a challenge in modern precision agriculture owing to the complex form of the sugarcane node when wrapped with leaves and the high computational demand. To solve these problems, a new binocular location method based on the improved YOLOv4 was proposed in this paper. First, the YOLOv4 deep learning algorithm was improved by the Channel Pruning Technology in network slimming, so as to ensure the high recognition accuracy of the deep learning algorithm and to facilitate transplantation to embedded chips. Secondly, the SIFT feature points were optimised by the RANSAC algorithm and epipolar constraint, which greatly reduced the mismatching problem caused by the similarity between stem nodes and sugarcane leaves. Finally, by using the optimised matching point to solve the homography transformation matrix, the space location of the sugarcane nodes was for the first time applied to the embedded chip in the complex field environment. The experimental results showed that the improved YOLOv4 algorithm reduced the model size, parameters and FLOPs by about 89.1%, while the average precision (AP) of stem node identification only dropped by 0.1% (from 94.5% to 94.4%). Compared with other deep learning algorithms, the improved YOLOv4 algorithm also has great advantages. Specifically, the improved algorithm was 1.3% and 0.3% higher than SSD and YOLOv3 in average precision (AP). In terms of parameters, FLOPs and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD and 1/10 of YOLOv3. At the same time, the average locational error of the stem node in the Z direction was only 1.88 mm, which totally meets the demand of sugarcane harvesting robots in the next stage.
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TANG, CHENG-YUAN, HONG-LONG CHOU, YI-LEH WU, and YAN-HUNG DING. "ROBUST FUNDAMENTAL MATRIX ESTIMATION USING COPLANAR CONSTRAINTS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 04 (2008): 783–805. http://dx.doi.org/10.1142/s0218001408006429.
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Due to degeneracy problems (e.g. corresponding point pairs are located on the same plane), it is difficult to estimate the fundamental matrix robustly and reliably using estimators even if without outliers in corresponding point pairs. In order to overcome this problem, two novel and fast methods to test the coplanarity of the corresponding point pairs are proposed. The first method is to use plane fitting. Without rectification, we formulate a plane equation to check the coplanarity of the selected corresponding point pairs. The second method employs the concept of a homography. In several experimental designs, experiments were used to test our proposed methods. According to our results, the performance of our two proposed methods is better than that of the traditional method. Furthermore, the method using homography is better than that using plane fitting. Finally, we apply our results to 3D reconstruction.
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Wang, Chen, Xiang Wang, Xiao Bai, Yun Liu, and Jun Zhou. "Self-Supervised deep homography estimation with invertibility constraints." Pattern Recognition Letters 128 (December 2019): 355–60. http://dx.doi.org/10.1016/j.patrec.2019.09.021.
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Mohammed, H. M., and N. El-Sheimy. "FEATURE MATCHING ENHANCEMENT OF UAV IMAGES USING GEOMETRIC CONSTRAINTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 307–14. http://dx.doi.org/10.5194/isprs-archives-xlii-1-307-2018.
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<p><strong>Abstract.</strong> Preliminary matching of image features is based on the distance between their descriptors. Matches are further filtered using RANSAC, or a similar method that fits the matches to a model; usually the fundamental matrix and rejects matches not belonging to that model. There are a few issues with this scheme. First, mismatches are no longer considered after RANSAC rejection. Second, RANSAC might fail to detect an accurate model if the number of outliers is significant. Third, a fundamental matrix model could be degenerate even if the matches are all inliers. To address these issues, a new method is proposed that relies on the prior knowledge of the images’ geometry, which can be obtained from the orientation sensors or a set of initial matches. Using a set of initial matches, a fundamental matrix and a global homography can be estimated. These two entities are then used with a detect-and-match strategy to gain more accurate matches. Features are detected in one image, then the locations of their correspondences in the other image are predicted using the epipolar constraints and the global homography. The feature correspondences are then corrected with template matching. Since global homography is only valid with a plane-to-plane mapping, discrepancy vectors are introduced to represent an alternative to local homographies. The method was tested on Unmanned Aerial Vehicle (UAV) images, where the images are usually taken successively, and differences in scale and orientation are not an issue. The method promises to find a well-distributed set of matches over the scene structure, especially with scenes of multiple depths. Furthermore; the number of outliers is reduced, encouraging to use a least square adjustment instead of RANSAC, to fit a non-degenerate model.</p>
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Zhang, Yueyuan, Arpan Ghosh, Yechan An, Kyeongjin Joo, SangMin Kim, and Taeyong Kuc. "Geometry-Constrained Learning-Based Visual Servoing with Projective Homography-Derived Error Vector." Sensors 25, no. 8 (2025): 2514. https://doi.org/10.3390/s25082514.
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We propose a novel geometry-constrained learning-based method for camera-in-hand visual servoing systems that eliminates the need for camera intrinsic parameters, depth information, and the robot’s kinematic model. Our method uses a cerebellar model articulation controller (CMAC) to execute online Jacobian estimation within the control framework. Specifically, we introduce a fixed-dimension, uniform-magnitude error function based on the projective homography matrix. The fixed-dimension error function ensures a constant Jacobian size regardless of the number of feature points, thereby reducing computational complexity. By not relying on individual feature points, the approach maintains robustness even when some features are occluded. The uniform magnitude of the error vector elements simplifies neural network input normalization, thereby enhancing online training efficiency. Furthermore, we incorporate geometric constraints between feature points (such as collinearity preservation) into the network update process, ensuring that model predictions conform to the fundamental principles of projective geometry and eliminating physically impossible control outputs. Experimental and simulation results demonstrate that our approach achieves superior robustness and faster learning rates compared to other model-free image-based visual servoing methods.
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Zhang, Qiaomei, Baoquan Li, and Fuyun Sun. "Visual Servo Tracking Control and Scene Depth Identification of Mobile Robots with Velocity Saturation Constraints." Mathematics 13, no. 5 (2025): 790. https://doi.org/10.3390/math13050790.
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Velocity saturation constraints are a significant issue for wheeled mobile robots (WMRs) when designing kinematics-based control laws. To handle the problem of velocity saturation constraints, a novel monocular visual servoing controller is developed for WMRs to solve tracking problems and enable unknown depth estimation. By analyzing the kinematic model of the robot system and employing the homography decomposition technique, measurable signals are obtained to develop a visual tracking error model for non-holonomic mobile robots. To ensure that the velocity commands are consistently constrained within the allowed limits, a saturation function is employed in the designed visual servoing control law. Furthermore, an adaptive updating law is designed to estimate the unknown depth information. The boundedness of the velocity commands is analyzed to evaluate the saturation performance of the developed visual servoing controller. With the aid of Lyapunov techniques and Barbalat’s lemma, the stability of this scheme is demonstrated. The simulation and experiment verify the performance of the proposed method.
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Chojnacki, Wojciech, Zygmunt L. Szpak, Michael J. Brooks, and Anton van den Hengel. "Enforcing consistency constraints in uncalibrated multiple homography estimation using latent variables." Machine Vision and Applications 26, no. 2-3 (2015): 401–22. http://dx.doi.org/10.1007/s00138-015-0660-7.
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Altarriba, Jeanette, and Jennifer L. Gianico. "Lexical Ambiguity Resolution Across Languages: A Theoretical and Empirical Review." Experimental Psychology 50, no. 3 (2003): 159–70. http://dx.doi.org/10.1026//1617-3169.50.3.159.
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Abstract. Words that involve completely different meanings across languages but possess significant overlap in form are referred to as homographic noncognates or interlexical homographs (e.g., red is a color word in English but means “net” in Spanish). An important question in the investigation of the processing of these words is whether or not both meaning and form are integral to their representation leading to language-specific processing of these items. In contrast, some theories have been put forth indicating that the processing of these words is nonselective with regards to language. Simply stated, when one of these words is encountered, all of the relevant meanings are accessed regardless of the specific demands of the task and the base language that is being used. In the present, critical review, evidence purported to favor each view is presented along with a discussion of the methodological and analytic constraints that moderate the reported findings. The data lead to the conclusion that there is a time course involved in the activation of multiple meanings such that a primary or dominant meaning (sometimes biased by frequency) is typically accessed more readily, followed by the opposite language meaning. These results indicated that studies should focus on manipulating the timing intervals between the presentation of these words and subsequent responses that are required by a particular task.
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Zhu, Yuan, Hao An, Huaide Wang, Ruidong Xu, Mingzhi Wu, and Ke Lu. "RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization." Sensors 24, no. 2 (2024): 536. http://dx.doi.org/10.3390/s24020536.
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Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.
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Bar-On, Amalia, Tal Oron, and Orna Peleg. "Semantic and syntactic constraints in resolving homography: a developmental study in Hebrew." Reading and Writing 34, no. 8 (2021): 2103–26. http://dx.doi.org/10.1007/s11145-021-10129-6.
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Lin, Bin, Xiangpeng Xu, Zhihua Shen, Xia Yang, Lijun Zhong, and Xiaohu Zhang. "A Registration Algorithm for Astronomical Images Based on Geometric Constraints and Homography." Remote Sensing 15, no. 7 (2023): 1921. http://dx.doi.org/10.3390/rs15071921.
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The registration of astronomical images, is one of the key technologies to improve the detection accuracy of small and weak targets during astronomical image-based observation. The error of registration has a great influence on the trace association of targets. However, most of the existing methods for point-matching and image transformation lack pertinence for this actual scene. In this study, we propose a registration algorithm based on geometric constraints and homography, for astronomical images. First, the position changes in stars in an image caused by the motion of the platform where the camera had been stationed, were studied, to choose a more targeted registration model, which is based on homography transformation. Next, each image was divided into regions, and the enclosed stable stars were used for the construction of triangles, to reduce the errors from unevenly distributed points and the number of triangles. Then, the triangles in the same region of two images were matched by the geometric constraints of side lengths and a new cumulative confidence matrix. Finally, a strategy of two-stage estimation was applied, to eliminate the influence of false pairs and realize accurate registration. The proposed method was then tested on sequences of real optical images under different imaging conditions and confirmed to have outstanding performance in the dispersion rate of points, the accuracy of matching, and the error of registration, as compared to baseline methods. The mean pixel errors after registration for different sequences are all less than 0.5 when the approximate rotation angle per image is from 0.58 ×10−2 to 5.89 ×10−2.
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Guan, Banglei, Qifeng Yu, and Friedrich Fraundorfer. "Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints." Computer Vision and Image Understanding 170 (May 2018): 79–91. http://dx.doi.org/10.1016/j.cviu.2018.03.001.
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Ghavam, Sepehr, Matthew Post, Mohamed A. Naiel, Mark Lamm, and Paul Fieguth. "Constraints for Time-Multiplexed Structured Light with a Hand-held Camera." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–3. http://dx.doi.org/10.15353/jcvis.v6i1.3562.
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 Multi-frame structured light in projector-camera systems affords high-density and non-contact methods of 3D surface reconstruction. However, they have strict setup constraints which can become expensive and time-consuming. Here, we investigate the conditions under which a projective homography can be used to compensate for small perturbations in pose caused by a hand-held camera. We synthesize data using a pinhole camera model and use it to determine the average 2D reprojection error per point correspondence. This error map is grouped into regions with specified upper-bounds to classify which regions produce sufficiently minimal error to be considered feasible for a structured-light projector-camera system with a hand-held camera. Empirical results demonstrate that a sub-pixel reprojection accuracy is achievable with a feasible geometric constraints
 
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López-Nicolás, Gonzalo, Nicholas R. Gans, Sourabh Bhattacharya, Carlos Sagüés, Josechu J. Guerrero, and Seth Hutchinson. "Homography-Based Control Scheme for Mobile Robots With Nonholonomic and Field-of-View Constraints." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 4 (2010): 1115–27. http://dx.doi.org/10.1109/tsmcb.2009.2034977.
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Wang, W., A. Lou, and J. Wang. "THE RESEARCH OF LINE MATCHING ALGORITHM UNDER THE IMPROVED HOMOGRAPH MATRIX CONSTRAINT CONDITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B3 (July 31, 2012): 345–50. http://dx.doi.org/10.5194/isprsarchives-xxxix-b3-345-2012.
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Wang, Xiaolong, Lei Yu, Yingying Zhang, et al. "HomoMatcher: Achieving Dense Feature Matching with Semi-Dense Efficiency by Homography Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 7952–60. https://doi.org/10.1609/aaai.v39i8.32857.
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Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module. Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images. To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework. We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints. By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers. Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.
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Cui, Jiang, and Wang. "Unsupervised Moving Object Segmentation from Stationary or Moving Camera based on Multi-frame Homography Constraints." Sensors 19, no. 19 (2019): 4344. http://dx.doi.org/10.3390/s19194344.
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Moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the designed cumulative acknowledgment strategy. (2) In the second stage, a super-pixel-based Markov Random Fields model is used to refine the spatial accuracy of initial segmentation and obtain final pixel level labeling, which has integrated trajectory classification information, a dynamic appearance model, and spatial temporal cues. The proposed method overcomes the limitations of existing object segmentation algorithms and resolves the difference between stationary and moving cameras. The algorithm is tested on several challenging open datasets. Experiments show that the proposed method presents significant performance improvement over state-of-the-art techniques quantitatively and qualitatively.
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Li, Jun, Yufeng Chen, and Aiming Mu. "Research on Unsupervised Feature Point Prediction Algorithm for Multigrid Image Stitching." Symmetry 16, no. 8 (2024): 1064. http://dx.doi.org/10.3390/sym16081064.
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The conventional feature point-based image stitching algorithm exhibits inconsistencies in the quality of feature points across diverse scenes. This may result in the deterioration of the alignment effect or even the inability to align two images. To address this issue, this paper presents an unsupervised multigrid image alignment method that integrates the conventional feature point-based image alignment algorithm with deep learning techniques. The method postulates that the feature points are uniformly distributed in the image and employs a deep learning network to predict their displacements, thereby enhancing the robustness of the feature points. Furthermore, the precision of image alignment is enhanced through the parameterization of APAP (As-projective-as-possible image stitching with moving DLT) multigrid deformation. Ultimately, based on the symmetry exhibited by the homography matrix and its inverse matrix throughout the projection process, image chunking inverse warping is introduced to obtain the stitched images for the multigrid deep learning network. Additionally, the mesh shape-preserving loss is introduced to constrain the shape of the multigrid. The experimental results demonstrate that in the real-world UDIS-D dataset, the method achieves notable improvements in feature point matching and homography estimation tasks, and exhibits superior alignment performance on the traditional image stitching dataset.
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Jia, Bingxi, and Shan Liu. "Switched visual servo control of nonholonomic mobile robots with field-of-view constraints based on homography." Control Theory and Technology 13, no. 4 (2015): 311–20. http://dx.doi.org/10.1007/s11768-015-4068-8.
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Kweon, Hyeokjun, Hyeonseong Kim, Yoonsu Kang, Youngho Yoon, WooSeong Jeong, and Kuk-Jin Yoon. "Pixel-Wise Warping for Deep Image Stitching." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1196–204. http://dx.doi.org/10.1609/aaai.v37i1.25202.
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Existing image stitching approaches based on global or local homography estimation are not free from the parallax problem and suffer from undesired artifacts. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of a Pixel-wise Warping Module (PWM) and a Stitched Image Generating Module (SIGMo). For PWM, we obtain pixel-wise warp in a similar manner as estimating an optical flow (OF). In the stitching scenario, the input images usually include non-overlap (NOV) regions of which warp cannot be directly estimated, unlike the overlap (OV) regions. To help the PWM predict a reasonable warp on the NOV region, we impose two geometrical constraints: an epipolar loss and a line-preservation loss. With the obtained warp field, we relocate the pixels of the target image using forward warping. Finally, the SIGMo is trained by the proposed multi-branch training framework to generate a stitched image from a reference image and a warped target image. For training and evaluating the proposed framework, we build and publish a novel dataset including image pairs with corresponding pixel-wise ground truth warp and stitched result images. We show that the results of the proposed framework are quantitatively and qualitatively superior to those of the conventional methods.
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Tao, Ye, and Zhihao Ling. "Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study." Sensors 20, no. 12 (2020): 3420. http://dx.doi.org/10.3390/s20123420.
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The foreground segmentation method is a crucial first step for many video analysis methods such as action recognition and object tracking. In the past five years, convolutional neural network based foreground segmentation methods have made a great breakthrough. However, most of them pay more attention to stationary cameras and have constrained performance on the pan–tilt–zoom (PTZ) cameras. In this paper, an end-to-end deep features homography transformation and fusion network based foreground segmentation method (HTFnetSeg) is proposed for surveillance videos recorded by PTZ cameras. In the kernel of HTFnetSeg, there is the combination of an unsupervised semantic attention homography estimation network (SAHnet) for frames alignment and a spatial transformed deep features fusion network (STDFFnet) for segmentation. The semantic attention mask in SAHnet reinforces the network to focus on background alignment by reducing the noise that comes from the foreground. STDFFnet is designed to reuse the deep features extracted during the semantic attention mask generation step by aligning the features rather than only the frames, with a spatial transformation technique in order to reduce the algorithm complexity. Additionally, a conservative strategy is proposed for the motion map based post-processing step to further reduce the false positives that are brought by semantic noise. The experiments on both CDnet2014 and Lasiesta show that our method outperforms many state-of-the-art methods, quantitively and qualitatively.
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Ingle, Palash Yuvraj, and Young-Gab Kim. "Panoramic Video Synopsis on Constrained Devices for Security Surveillance." Systems 13, no. 2 (2025): 110. https://doi.org/10.3390/systems13020110.
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As the global demand for surveillance cameras increases, the digital footage data also explicitly increases. Analyzing and extracting meaningful content from footage is a resource-depleting and laborious effort. The traditional video synopsis technique is used for constructing a small video by relocating the object in the time and space domains. However, it is computationally expensive, and the obtained synopsis suffers from jitter artifacts; thus, it cannot be hosted on a resource-constrained device. In this research, we propose a panoramic video synopsis framework to address and solve the problems of the efficient analysis of objects for better governance and storage. The surveillance system has multiple cameras sharing a common homography, which the proposed method leverages. The proposed method constructs a panorama by solving the broad viewpoints with significant deviations, collisions, and overlapping among the images. We embed a synopsis framework on the end device to reduce storage, networking, and computational costs. A neural network-based model stitches multiple camera feeds to obtain a panoramic structure from which only tubes with abnormal behavior were extracted and relocated in the space and time domains to construct a shorter video. Comparatively, the proposed model achieved a superior accuracy matching rate of 98.7% when stitching the images. The feature enhancement model also achieves better peak signal-to-noise ratio values, facilitating smooth synopsis construction.
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Honda, Keisuke. "Homographic kanji, their ambiguity and the effectiveness of okurigana as a device for disambiguation." Written Language and Literacy 12, no. 2 (2009): 213–36. http://dx.doi.org/10.1075/wll.12.2.06hon.
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This paper has three goals. First, it addresses the problem of ambiguity induced by a large number of kanji graphemes that are used in the current Japanese writing system. Secondly, it summarises and examines claims that this ambiguity is reduced by an orthographic device called okurigana. Thirdly, it argues that the disambiguating function of okurigana is seriously restricted by several constraints. To support this argument, a survey of the Joyo Kanji Hyo (i.e., inventory of kanji graphemes and their readings recommended by the Japanese Cabinet for daily use) is presented. The results of this survey demonstrate that even though okurigana actually disambiguates a number of kanji graphemes, it fails to do so in many cases because of the constraints. This indicates that a significant amount of ambiguity remains unresolved in the kanji script.
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Chen, Yifu, Yuan Le, Zhong Xie, et al. "The Image Matching Algorithm Basing on Homographic Constraints for Marine Surveying, Mapping and 3D Reconstruction." Journal of Coastal Research 97, sp1 (2019): 184. http://dx.doi.org/10.2112/si97-026.1.
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Orii, Hideaki, Keitaro Tominaga, Hideaki Kawano, Hiroshi Maeda, and Norikazu Ikoma. "Extraction of the Region Feasible for Safe Ambulation from Head-Mounted Wearable Camera Images Employing Genetic Algorithm on Homography Constraints." Applied Mechanics and Materials 103 (September 2011): 633–40. http://dx.doi.org/10.4028/www.scientific.net/amm.103.633.
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The objects have potentially dangerousness for pedestrians, such as illegally-parked bicy-cles, power poles, stopping vehicles, etc., could be found in a street. Those objects are threats to thesafety of elderly and disabled people with impaired vision, because it is difficult for them to find thoseobjects. In this paper, to enhance the elderly and disabled people's mobility, we propose the methodto estimate the region which is feasible for people's mobility in life space. In this method, the imagesare obtained from a head-mounted wearable camera, and the suited region for walking is estimatedusing 3-dimensional analysis and genetic algorithm. The performance and the validity are shown byapplying the proposed method to a number of scenes in life space.
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Cha, Hyoungsuk. "Musical Characteristics and Functions of ‘Gaetakseong’, an Anchaebisori of Gyeongje Beompae." National Gugak Center 50 (October 31, 2024): 263–92. http://dx.doi.org/10.29028/jngc.2024.50.263.
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This study aims to elucidate the musical characteristics and functions of gaetakseong (開鐸聲), an anchaebisori of Gyeongje beompae (Buddhist chant). Since musical features of gaetakseong, as opposed to other subgenres of beompae, are observed, not constrained by musical piece and ‘sori (sound)’ or ‘seong (聲 sound)’, it is necessary to examine different types of beompae in order to clarify the identity of gaetakseong. Therefore, this study selects and analyzes a total of five songs: two hotsori pieces that are non-anchaebisori among beompae, and three pieces that represent the other three anchaebisori, yuchiseong (由致聲), chakeoseong (着語聲), and pyeongeseong (徧界聲). The common musical characteristics of the five pieces include a free rhythm with asymmetrical, irregular beats and a melodic mode of menari-tori finishing in ‘mi’ or ‘la’. In the case of text setting, there are four different patterns shown in these songs: ① melismatic (multiple notes per syllable), ② homographic+melismatic, ③ homographic (one note per syllable), and ④ homographic+melismatic+melismatic.’ In pyeongeseong, however, only two patterns ② and ③ are observed. In particular, the common melodic characteristics of the five pieces are found in the ③ homographic pattern; the melody is relatively monotonous because it is mainly composed of three notes 'mi', 'la', and 'do', centering on 'la', or it continuously repeats a single note. Therefore, it is found that the melody in the ③ homographic pattern has less activity and a narrow range compared to the ①, ②, and ④ patterns, whereas more dynamic melodic patterns as well as an expanded range, depending on the singer's capability, appear in yuchiseong. The distinctive musical features of the five pieces are first in the literary form, with “Bokcheongge” written in verse, and “Yeongsan Gaegye”, “Yuchi”, “Chakeo”, and “Su-ui-an-jwa” written in prose, each showing with a different writing style. Regarding the tempo, most start slowly, around 35-50, while “Huchakeo” typically starts faster than that, around 75-80. In addition, the range of hotsori was the minor 10th, which is more than an octave while yuchiseong shows the perfect 8th and chakeoseong and pyeongeseong show the minor 6th, which are less than an octave. Gaetakseong has been emphasized as a sound of “reading blurry and darkly” or “sweeping away” among scholars and practitioners of beompae. As a result, the partial characteristics of the gaetakseong were identified as the essence of the overall ‘seong.’ However, as discussed in this paper, the sound of “reading blurry and darkly” or “sweeping away” is limited just to the simple melody-type singing style of the homographic pattern, and gaetakseong reveals the characteristics of ‘seong’, showing that various singing styles other than the homographic pattern coexist. Therefore, it can be argued that gaetakseong is the ‘seong’ with various musical structures and forms. Moreover, it has been found that the “Yeongsangaegye”, which has very long lyrics, is implemented by selectively using two or three singing styles depending on the situation, demonstrating that the singing style of gaetakseong is very flexible and fluid. Since gaetakseong is woven with four singing styles alternating and intersecting, it can be said to be a very suitable method for effectively handling limited time without significantly damaging the characteristics of each genre of music unique to beompae. Therefore, in large-scale rituals, when similar genres of beompae are arranged without overlapping each other or it is judged difficult to finish the rituals within a set time, the musical function of gaetakseong, which can reduce the performance time compared to the original while sufficiently utilizing the unique characteristics of beompae, will stand out even more.
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Zhang, Shihua, Zhenjie Zhu, Zizhuo Li, Tao Lu, and Jiayi Ma. "Matching While Perceiving: Enhance Image Feature Matching with Applicable Semantic Amalgamation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10094–102. https://doi.org/10.1609/aaai.v39i10.33095.
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Image feature matching is a cardinal problem in computer vision, aiming to establish accurate correspondences between two-view images. Existing methods are constrained by the performance of feature extractors and struggle to capture local information affected by sparse texture or occlusions. Recognizing that human eyes consider not only similar local geometric features but also high-level semantic information of scene objects when matching images, this paper introduces SemaGlue. This novel algorithm perceives and incorporates semantic information into the matching process. In contrast to recent approaches that leverage semantic consistency to narrow the scope of matching areas, SemaGlue achieves semantic amalgamation with the designed Semantic-Aware Fusion (SAF) Block by injecting abundant semantic features from the pre-trained segmentation model. Moreover, the Cross-Domain Alignment (CDA) Block is proposed to address domain alignment issues, bridging the gaps between semantic and geometric domains to ensure applicable semantic amalgamation. Extensive experiments demonstrate that SemaGlue outperforms state-of-the-art methods across various applications such as homography estimation, relative pose estimation, and visual localization.
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He, Haiqing, Wenbo Xiong, Fuyang Zhou, Zile He, Tao Zhang, and Zhiyuan Sheng. "Topology-Aware Multi-View Street Scene Image Matching for Cross-Daylight Conditions Integrating Geometric Constraints and Semantic Consistency." ISPRS International Journal of Geo-Information 14, no. 6 (2025): 212. https://doi.org/10.3390/ijgi14060212.
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While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction.