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Today, the objective of applying shadow theory to architectural drawing is usually to obtain one (or several) bitmap images. This a priori innocuous fact leads to vector images being conspicuous by their absence when lighting factors come into play during the modelling and parametric design of architecture and its environment. The present article sets out a methodology to calculate shadows by placing vector drawing not only at the heart of the process but also of the obtained results. We found that in today's context of university excellence, it is essential to apply knowledge, control, as well as possible editing and postproduction of the effects generated by rendering process lighting calculations. Such interventions are also generally useful and effective in our profession. [ABSTRACT FROM AUTHOR]

The data in the cloud is protected by various mechanisms to ensure security aspects and user's privacy. But, deceptive attacks like phishing might obtain the user's data and use it for malicious purposes. In Spite of much technological advancement, phishing acts as the first step in a series of attacks. With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks. The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular domain names, using reserved characters for re directions and multi-chain phishing. Such phishing URLs can be stored as a part of the document and uploaded in the cloud, providing a nudge to hackers in cloud storage. The cloud servers are becoming the trusted tool for executing these attacks. The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client's end (browser). At the same time, the avalanche effect and immutability of block-chain proves to be a strong source of security. Considering these trends in technology, a block-chain based filtering implementation for preserving the integrity of user data stored in the cloud is proposed. The proposed Phish Block detects the homographic phishing URLs with accuracy of 91% which assures the security in cloud storage. [ABSTRACT FROM AUTHOR]

On July 28, 2021, the Device for the Autonomous Bootstrapping of Unified Sentience ("DABUS") became the first computer to be recognized as a patent inventor. Due to the advocacy of DABUS's inventor, Dr. Stephen Thaler, the world's definition of "inventor" has finally fractured - dividing patent regimes between recognition of machine inventorship and lack thereof. This division has sparked many scholarly conversations about inventorship contribution, but none have discussed the implications of a homographic inventorship. This Article addresses the implications of international homographic inventorship - where countries have different notions and rules concerning patent inventorship - and the consequences for failing to understand the divergences that could result in patent invalidation. This Article adds to the literature by addressing Thaler's tireless inventorship advocacy, highlighting that Thaler uses his position of privilege to argue for inventorship acknowledgement of his machine and simultaneously to relinquish his own inventorship recognition. To emphasize, there is no existing caselaw except the DABUS case where a potential inventor has argued for the acknowledgement of another inventor and simultaneously relinquished their own recognition - whether that unacknowledged inventor was human or not human. Thaler's advocacy amplifies the need for continued conversation regarding closing the patent inventorship gap for women and underrepresented minorities of color, who are too often tokenized and marginalized in STEM and in the patent process. By bringing the definition of inventor to the forefront, the DABUS case represents more than just a case of AI inventorship: it is a potential gateway to provide language and arguments to frame conversations about inventorship equity. In particular, the unique instance of Thaler's inventorship advocacy for his machine prompts questions about why inventors from privileged positions do not advocate for inventors from historically marginalized backgrounds. Based on a review of patent case law and sociology studies concerning power dynamics and communal recognition, this Article provides recommendations to address this issue and accelerate the stagnant process of achieving inventorship equity. [ABSTRACT FROM AUTHOR]

This article proposes yet another approach towards looking into causes for attention distribution of an operator of unmanned aerial vehicle. During examination, the operator is being tested at dedicated flight simulator while data are gathered and visualized through a mobile eye tracker. Two work stages are considered sequentially, i.e. building a geometric 2D transformation of region of interest (homography) within an image, and overlaying a dynamic heatmap as well. In the former stage, spontaneous movements of the operator's head, recorded by the video, are eliminated thus enabling the operator to use the mobile eye tracker instead of a desktop-based one. During the latter stage, the distribution of operator's attention over time is displayed. In order to implement the current research, a source code has been developed in C++ for some features readily available in OpenCV library to be used. In addition, data gathered after carrying out flight session are processed and discussed thoroughly. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are the most popular way to collect ground data today, thanks to their low cost and matchless convenience. However, UAVs are prone to unstable flight poses because they are so light in weight, which has resulted in a new challenge for UAV image stitching. In this paper, we propose a robust approach to stitch UAV images captured from approximately planar scenes without pose parameters assistance. The key idea of the proposed framework lies in an effective projection plane selection strategy, which is capable of resisting the perspective distortion from existing pose-perturbed images. To select a reasonable reference image as the projection plane, we first divide all the images into two groups (stable group and unstable group) according to their registration error under the affine model. Then, a specifically designed approach is used to define a weighted topological graph, which guarantees that the reference image is selected from the stable group while maintaining a global minimum accumulated registration error. Based on our cost topological graph, each unstable group image is locally attached to a stable group image via a homography. Finally, alignment parameters of all the stable group images are solved using affine model, after which global optimization is performed on the model of both groups. Comparing our results to those of the conventional approaches indicates that our proposed approach produced superior results in several challenging experiments involving both qualitative and quantitative evaluation. [ABSTRACT FROM AUTHOR]

Highlights • We propose a minimal closed-form solution for plane-induced homography. • We propose a minimal solution for plane motion and structure problem. • We derive a concise closed-form relation between two solutions of plane motion and structure. Abstract Plane motion and structure is a classical problem in photogrammetry and computer vision communities. While the methods based on the Singular Value Decomposition of the plane-induced homography matrix have been largely successful, they are subject to the special assumption about the bottom right entry or the determinant sign of the homography matrix. This paper proposes firstly an explicit closed-form solution for the homography using the normalized barycentric coordinates from four image point correspondences in general position. Then we use algebraic method to propose a minimal solution for the plane motion and structure problem. Further, we derive a concise closed-form relation between two solutions of the plane motion and structure. The performance and practicability of our method are validated by testing on synthetic and real data. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for assessing the motion stability of a video after stabilization. The assessment works in a non-reference manner that directly measures the intrinsic smoothness of the video motion path. Specifically, the motion path is cast as a curve embedded in the Lie group of homographies, and its smoothness is mathematically characterized by the intrinsic geodesic curvature. A bundle of paths are adopted to handle spatially variant motions through the frames. Then, we compute the weighted curvature for a holistic assessment on the motion stability. Other factors related to video stabilization, e.g., distortion and cropping, are also investigated as supplement. We collect 160 shaky video clips and their stabilized results for verification, and the experimental evidence shows the effectiveness of our algorithm in good correlation with human subjective judgements. [ABSTRACT FROM AUTHOR]

Today's smartphones make a broad variety of sensors (gyroscope, magnetometer, camera, accelerometer, GPS, etc.) readily available and easily accessible through different APIs, favouring the acquisition of data. An everyday usage is the measurement of physical parameters, like sound or acceleration. The advances in terms of level of integration and its application to embedded devices power consumption and wide adoption of system on chips and more recently multiprocessors system on chip mean that a new sort of applications can be addressed. Applications are backed with powerful computing devices depending on batteries. Using these resource-limited devices and their parallel power efficiently is a challenging task. To fully exploit the potential of these hardware devices, parallelism has to be carefully applied to the most resource demanding parts of the application. This paper proposes an efficient image composition method to analyze fruit surfaces using CCD cameras and smartphones. The image composition is done by capturing video from which redundant frames are disposed using a data-parallel local feature detector. Relevant frames are then stitched using direct methods. The proposal was tested in the case of calculating the damaged surface of cherries. [ABSTRACT FROM AUTHOR]

Highlights • Iterative transform simulation is proposed to reduce the computational complexity of image matching. • A geometric method to evaluate the homography matrix is proposed to filter out wrong matching results. • Superior performance in terms of speed while maintaining accuracy in matching oblique aerial images. Abstract This paper presents a fast and accurate method for matching oblique aerial image pairs. In order to achieve accurate matching results, we must consider viewpoint differences between the input images in addition to rotation and scaling. Existing methods that match aerial image pairs with viewpoint differences undergo heavy computation and have difficulty finding correspondences. In this paper, we propose a homography matrix evaluation method based on a geometric approach to increase the accuracy of image matching results. In addition, we achieve faster matching through an iterative transform simulation that reduces computational complexity. Experimental results show that the proposed method improves aerial image matching in terms of computational efficiency while achieving successful matching results. [ABSTRACT FROM AUTHOR]

Images of co-planar points in 3-dimensional space taken from different camera positions are a homography apart. Homographies are at the heart of geometric methods in computer vision and are used in geometric camera calibration, 3D reconstruction, stereo vision and image mosaicking among other tasks. In this paper we show the surprising result that homographies are the apposite tool for relating image colors of the same scene when the capture conditions—illumination color, shading and device—change. Three applications of color homographies are investigated. First, we show that color calibration is correctly formulated as a homography problem. Second, we compare the chromaticity distributions of an image of colorful objects to a database of object chromaticity distributions using homography matching. In the color transfer problem, the colors in one image are mapped so that the resulting image color style matches that of a target image. We show that natural image color transfer can be re-interpreted as a color homography mapping. Experiments demonstrate that solving the color homography problem leads to more accurate calibration, improved color-based object recognition, and we present a new direction for developing natural color transfer algorithms. [ABSTRACT FROM AUTHOR]

Babylen Tatarsky, the protagonist in Russian writer Viktor Pelevin’s novel Generation “P” (translated into English by Andrew Bromfield as Homo Zapiens), works to adapt American advertisements for the Russian market and witnesses how the reality of Russia’s tumultuous 1990s is replaced by a consumer-driven television simulation. Puns in the advertising slogans that Tatarsky translates, interspersed throughout the novel, are central to its plot. Some of these puns exhibit greater sophistication than others: in addition to utilizing homonymy, homophony, homography, paronymy, and polysemy, they involve transmesis, multilingualism, and intertextuality. This article compares how Pelevin’s translators (English, German, Polish, Spanish, and French) approached these difficult puns. The objective of this comparative analysis is to demonstrate how the intertext(s) evoked through wordplay may, on the one hand, impede translation, but, on the other, open avenues for creative solutions, by producing new traces and echoes of meaning that make the act of translation possible. The issues raised by the various translations point to a need to re-examine the roles and tasks of the translator and underscore the importance of keeping the (un)translatability debate open. Ultimately, this article aims to contribute to the ongoing reconceptualization of what literary translation is and, especially, what it does: with texts, readers, literatures, and, above all, with language. [ABSTRACT FROM AUTHOR]

Multi-view stereo (MVS) map based 3D range reconstruction is to generate 3D ranges by analyzing the surrounding snapshots from different perspectives. Different to the traditional method which employing the expensive and difficult maintaining laser range devices to calibrate the range of the real 3D objects, MVS has achieved its success by seeking the geometrical correlations between the correspondences from the snapshot of different perspectives. The concerning of MVS keeps rising thanks to the fast development of digital maps and 3D printing. Several algorithms with regard to MVS has been well developed and achieved their success with regard to reconstruction of 3D ranges. Meanwhile, most of the algorithms were mainly focusing on the fusion and merging of different scenes and surface refinement. Less capability of the feature matching algorithms on the affine invariant images renders the current MVS algorithms need huge amount of images with tiny perspective differences. In this paper, we will propose a new MVS algorithm, deploying our previous published Affine Invariant Feature Descriptor (AIFD) to detect and match the correspondences from different perspectives and applying Homograph matrix and segmentation to define the planes of the objects. Thanks to the AIFD and Homograph based projection model, our proposed MVS algorithm outperform other MVS algorithms in terms of speed and efficiency. [ABSTRACT FROM AUTHOR]

To increase the performance of sport team, the tactical analysis of team from game video is essential. Trajectories of the players are the most useful cues in a sport video for tactical analysis. In this paper, we propose a technique to reconstruct the trajectories of players from broadcast basketball videos. We first propose a mosaic based approach to detect the boundary lines of court. Then, the locations of players are determined by the integration of shape and color visual information. A layered graph is constructed for the detected players, which includes all possible trajectories. A dynamic programming based algorithm is applied to find the trajectory of each player. Finally, the trajectories of players are displayed on a standard basketball court model by a homography transformation. In contrast to related works, our approach exploits more spatio-temporal information in video. Experimental results show that the proposed approach works well and outperforms some existing technique. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes. [ABSTRACT FROM AUTHOR]

We present a novel approach to estimate the rotation and translation between two camera views from a minimum of five matched points in the images. Our approach simultaneously recovers the 3D structure of the points up to a common scale factor, and is immune to a variety of problems that plague existing methods that are based on the Euclidean homography or Essential matrix. Methods based on homography only function when feature points are coplanar in 3D space. Methods based on the Essential matrix often lose accuracy as the translation between two camera views goes to zero or when points are coplanar. By recovering the rotation and translation independently using quaternions, our algorithm eschews the shortcomings of these methods. Moreover, we do not impose any constraints on the 3D configuration of the points (such as coplanar or non-coplanar constraints). Our method is particularly well-suited for Position-Based Visual Servoing (PBVS) applications. Investigations using both simulations and experiments validate the new method. Comparisons between the proposed algorithm and the existing algorithms establish that our algorithm is robust to noise. A Matlab implementation of our algorithm is available online and free. [ABSTRACT FROM AUTHOR]

Line matching across views is a fundamental task in many applications. Existing methods are hardly applicable to across view scenarios due to the limitation of line descriptors and matching strategy. In this paper, we present a novel line-points invariant based on a new projective invariant named characteristic number. The invariant is able to reflect the intrinsic geometry of line and points, which keeps invariant across views. The construction of this invariant uses intersections of coplanar lines instead of endpoints, rendering more robust matching across views. Accurate homography recovered from the invariant allows all lines, even those without interest points around them, a chance to be matched. Extensive comparisons with the state-of-the-art validate the matching accuracy and robustness of the proposed method to projective transformations. The method also performs well for image pairs with similar textures and those of low textures. [ABSTRACT FROM AUTHOR]

Background and objective The development of biometric identification technology and intelligent medication has enabled researchers to analyze subcutaneous veins from near-infrared images. However, the stereo reconstruction of subcutaneous veins has not been well studied, and the results are difficult to utilize in clinical practice. Methods We present a hybrid constraint optimization (HCO) matching algorithm for vein reconstruction to solve the matching failure problems caused by the incomplete segmentation of vein structures captured from different views. This algorithm initially introduces the existence of the epipolar and homography constraints in the subcutaneous vein matching. Then, the HCO matching algorithm of the vascular centerline is established by homography point-to-point matching, homography matrix optimization, and vascular section matching. Finally, the 3D subcutaneous vein is reconstructed on the basis of the principle of triangulation and system calibration parameters. Results To validate the performance of the proposed matching method, we designed a series of experiments to evaluate the effectiveness of the hybrid constraint optimization method. The experiments were performed on simulated and real datasets. 42 real vascular images were analyzed on the basis of different matching strategies. Experimental result shows that the matching accuracy increased significantly with the proposed optimization matching method. To calculate the reconstruction accuracy, we reconstructed seven simulated cardboards and measured 10 vascular distances in each simulated cardboard. The average vascular distance error of each simulated image was within 1.0 mm, and the distance errors of 75% feature points were less than 1.5 mm. Also, we printed a 3D simulated vein model to improve the illustration of this system. The reconstruction error extends from −3.58 mm to 1.94 mm with a standard deviation of 0.68 mm and a mean of 0.07 mm. Conclusions The algorithm is validated in terms of homography optimization, matching efficiency, and simulated vascular reconstruction error. The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed algorithm. [ABSTRACT FROM AUTHOR]

To increase the robustness of detection in intelligent video surveillance systems, homography has been widely used to fuse foreground regions projected from multiple camera views to a reference view. However, the intersections of non-corresponding foreground regions can cause phantoms. This paper proposes an algorithm based on geometry and colour cues to cope with this problem, in which the homography between different camera views and the Mahalanobis distance between the colour distributions of every two associated foreground regions are considered. The integration of these two matching algorithms improves the robustness of the pedestrian and phantom classification. Experiments on real-world video sequences have shown the robustness of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an advanced thermal camera-based system for detection of objects on rail tracks is presented. Developed system is powered by advanced image processing algorithm, in order to achieve greater reliability and robustness, and tested on set of infrared images captured at night conditions. The goal of this system is to detect objects on rail tracks and next to them and estimate distances between camera stand and detected objects. For that purpose, different edge detection methods are tested, and finally Canny edge detector is selected for rail track detection and for determination of region of interest, further used for analysis in object detection process. In determined region of interest, region-based segmentation is used for object detection. For estimation of distances between camera stand and detected objects, homography based method is used. Validation of estimated distances is done, in respect to real measured distances from camera stand to objects (humans) involved in experiment. Distances are estimated with a maximum error of 2%. System can provide reliable object detection, as well as distance estimation, and for improved robustness and adaptability, artificial intelligence tools can be used. [ABSTRACT FROM AUTHOR]

Using an item-method directed forgetting task, we presented homographic homophonic nouns embedded in sentences. At study, each sentence was followed by an instruction to remember or forget the embedded word. On a subsequent yes-no recognition test, each word was again embedded within a sentence. In Experiments 1, 2, and 4 we varied the embedding sentence at test so that it was identical to that at study, changed but retained the meaning of the studied word, or changed to alter the meaning of the studied word. Repeated context – whether the sentence and/or the word meaning – proved to be as useful a retrieval cue for TBF items as for TBR items. In Experiment 3, we demonstrated that physical repetition was insufficient to produce context effects for either TBR or TBF items. And, in Experiment 4, we determined that participants were equally accurate in reporting context repetition/change following the correct recognition of TBR and TBF items. When considered in light of the existing literature, our results suggest that when context can be dissociated from the study item, it is encoded in “one shot” and not vulnerable to subsequent efforts to limit unwanted encoding. [ABSTRACT FROM AUTHOR]

Highlights • A systematic approach to generate pan-shots from motion blurred videos. • A graph-cut based foreground segmentation by using a cost derived from flow and homography. • A novel approach to harness motion from the background along with object velocity to perform non-blind deblurring of dynamic foreground. • A generic algorithm that can handle all the three cases arising from hand-held videos along with a case-identification scheme to reduce computations based on the specific scenario. • A multi-frame non-uniform blind deblurring of background using sparse motion weights (L 1) and image gradient (L p , p = 0.8) priors that outperforms existing single and multi-frame deblurring methods. Graphical abstract Abstract In this paper, we demonstrate a method to generate a pan photo from a video captured using a hand-held camera. We handle three relevant scenarios (no blur, only foreground blurred, both foreground and background blurred) that arise while capturing a video with a fixed frame-rate and under different relative object velocities and camera motion. Our method first segments out the moving object by motion compensation of the background and then estimates the inter-frame velocity and relative depth of the object. Automatic gradient-based identification is then performed to classify the video into one of the above three scenarios. If only the foreground is blurred, we perform non-blind restoration by judiciously harnessing the background motion and foreground velocity to estimate the foreground blur. When the background is blurred too, a blind multi-frame deblurring approach is advocated to get the background motion which is used to infer the foreground blur to obtain the latent frames. Once clean frames are obtained, we align the object and rewarp the background with respect to the net displacement of the object in each frame which when averaged produces the required realistic pan-photo. We demonstrate our method on a number of videos captured using different consumer cameras as well as on videos downloaded from the Internet. [ABSTRACT FROM AUTHOR]

In this approach, we will process a self-calibration problem of camera characterized by varying parameters. Our approach is based on estimating of homography of the plane at infinity and depths of interest points. This estimation is made from resolution of nonlinear equation system that is formulated from the projection of some points of the scene in the planes of different images. The relationships established between the homography of the plane at infinity and matches, between images, and those established between points of the 3D scene and their projections, in image planes, allow formulating the second nonlinear equations. This system is minimized by using the Levenberg-Marquardt algorithm to estimate the intrinsic parameters of used camera. This approach has several strong points: i) The use of any cameras (having varying parameters), ii) The use of any scenes (3D) and iii) the use of a minimum number of images (two images only). Experiments and simulations show the performance of this approach in terms of stability, accuracy and convergence. [ABSTRACT FROM AUTHOR]

Localization is an integral part of reliable robot navigation, and long-term autonomy requires robustness against perceptional changes in the environment during localization. In the context of vision-based localization, such changes can be caused by illumination variations, occlusion, structural development, different weather conditions, and seasons. In this paper, we present a novel approach for localizing a robot over longer periods of time using only monocular image data. We propose a novel data association approach for matching streams of incoming images to an image sequence stored in a database. Our method exploits network flows to leverage sequential information to improve the localization performance and to maintain several possible trajectories hypotheses in parallel. To compare images, we consider a semidense image description based on histogram of oriented gradients features as well as global descriptors from deep convolutional neural networks trained on ImageNet for robust localization. We perform extensive evaluations on a variety of datasets and show that our approach outperforms existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The segmentation of moving objects become challenging when the object motion is small, the shape of object changes, and there is global background motion in unconstrained videos. In this paper, we propose a fully automatic, efficient, fast and composite framework to segment the moving object on the basis of saliency, locality, color and motion cues. First, we propose a new saliency measure to predict the potential salient regions. In the second step, we use the RANSAC homography and optical flow to compensate the background motion and get reliable motion information, called motion cues. Furthermore, the saliency information and motion cues are combined to get the initial segmented object (seeded region). A refinement is performed to remove the unwanted noisy details and expand the seeded region to the whole object. Detailed experimentation is carried out on challenging video benchmarks to evaluate the performance of the proposed method. The results show that the proposed method is faster and performs better than state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This article presents a novel method for estimating 19 parameters of RGB and depth camera calibration simultaneously. The proposed algorithm is based on applying metaheuristic methods for deep optimization and estimating all parameters of intrinsic, extrinsic, and lens distortions of cameras. This paper compares four metaheuristic algorithms, i.e. a genetic algorithm, particle swarm optimization, the colonial competitive algorithm and the shuffled frog leaping algorithm, with a numerical algorithm called singular value decomposition. The proposed method does not need the initial estimation for optimization and it can avoid being trapped in local minima. By using nondirect estimation, we achieve middle computing matrices such as the homography matrix, which is used in the pinhole camera model. Both versions of Kinect sensors are used for the experimental evaluation. The mean square of the reprojection error criteria is defined as the objective function in the proposed algorithm. The experimental results show that the proposed method is more efficient and accurate than traditional numerical solutions. [ABSTRACT FROM AUTHOR]

Extracting 3D information from 2D images is an inverse estimation problem and a challenging task in itself. The aim of 2D to 3D reconstruction is to generate either a volume or a surface representing the object from multiple views. This paper presents a simple and accurate multiple-view volumetric 3D reconstruction method using an integrated approach based on homography estimation and voxel mapping. The homography-based approaches give accurate estimates but do not provide system dynamics. The voxel-based volumetric reconstruction methods provide system dynamics that are essential for system modeling. However, they face challenges while modeling the concavities. This paper presents a proposed 3D reconstruction method that combines homography estimation and the voxel mapping approach for improving the accuracy of 3D reconstruction. Experimental results show that the method efficiently reconstructs objects of known and unknown shape, fragile objects and complex scenes with multiple objects. The use of homography along with voxel mapping in a multiple-camera environment brings out more details of the object for improving the quality of reconstruction. [ABSTRACT FROM AUTHOR]

In non-orthoscopic video see-through (VST) head-mounted displays (HMDs), depth perception through stereopsis is adversely affected by sources of spatial perception errors. Solutions for parallax-free and orthoscopic VST HMDs were considered to ensure proper space perception but at expenses of an increased bulkiness and weight. In this work, we present a hybrid video-optical see-through HMD the geometry of which explicitly violates the rigorous conditions of orthostereoscopy. For properly recovering natural stereo fusion of the scene within the personal space in a region around a predefined distance from the observer, we partially resolve the eye-camera parallax by warping the camera images through a perspective preserving homography that accounts for the geometry of the VST HMD and refers to such distance. For validating our solution; we conducted objective and subjective tests. The goal of the tests was to assess the efficacy of our solution in recovering natural depth perception in the space around said reference distance. The results obtained showed that the quasi-orthoscopic setting of the HMD; together with the perspective preserving image warping; allow the recovering of a correct perception of the relative depths. The perceived distortion of space around the reference plane proved to be not as severe as predicted by the mathematical models. [ABSTRACT FROM AUTHOR]

This work concerns the automatic registration of spectral images of paintings upon planar, or approximately planar, surfaces. An approach that capitalizes upon this planarity is proposed, which estimates homography transforms that register the spectral images into an aligned spectral cube. Homography estimation methods are comparatively evaluated for this purpose. A non-linear, robust estimation method that is based on keypoint features is adopted, as the most accurate. A marker-based, quantitative evaluation method is proposed for the measurement of multispectral image registration accuracy and, in turn, utilized for the comparison of the proposed registration method to the state of the art. For the same purpose, characteristic for this application domain, benchmark datasets that are annotated with correctly corresponding points have been compiled and are publicly availed. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel real-time video stitching method using camera path estimation and homography refinement. The method can stably stitch multiple frames acquired from moving cameras in real time. In the proposed method, one initial between-camera (BC) homography and each camera path (CP) homography are used to estimate the BC homography at every frame. The BC homography is refined by using block matching to adjust the errors of estimated CPs (homography refinement). For fast processing, we extract features using the difference of intensities and use the optical flow to estimate camera motion (CM) homographies, which are multiplied with the previous CMs to calculate CPs (camera path estimations). In experiments, we demonstrated the performance of the CP estimation and homography refinement approach by comparing it with other methods. The experimental results show that the proposed method can stably stitch two image sequences at a rate exceeding 13 fps (frames per second). [ABSTRACT FROM AUTHOR]

In this paper, a homography-based visual servo controller is developed for a rigid body to track a moving object in three-dimensional space with a fixed relative pose. Specifically, a monocular camera is mounted on the rigid body, and the desired relative pose is expressed by a pre-recorded reference image. Homography is exploited to obtain the orientation and scaled position for controller design. Considering the unknown moving object's velocities and distance information, a continuous nonlinear visual controller is developed using the robust integral of the signum of the error methodology. To facilitate the stability analysis, the system uncertainties regarding the moving object's velocities and distance information are divided into the error-unrelated system uncertainties and the error-related system uncertainties. After that, the upper bounds of the error-related system uncertainties are derived with composited system errors. An asymptotic tracking of the leading object is proved based on the Lyapunov methods and the derived upper bounds. In addition, the proposed controller is extended to address the trajectory tracking problem. Simulation results validate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Real-time seabed tracking applications play an important role in underwater systems. A lot of them use computer vision for servoing, positioning, navigation, odometry and simultaneous localisation and mapping. They are mostly based on local image features, therefore feature detection, description and matching are crucial for their efficient operations. The aim of this study was to investigate the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HARRIS, ORB, BRISK and FREAK. Additionally, the image correction technique was presented and image enhancement methods were analysed in order to increase efficiency of image features matching. The matching algorithm was based on the homography matrix and random sample consensus technique. Our results indicate that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency. [ABSTRACT FROM AUTHOR]

If the local area of a three-dimensional object surface can be considered as a plane, the deformation between its two images captured from different camera placements is modelled by a homography. By tuning the parameters in a homographic mapping, all possible deformations caused by the change of camera placement can be simulated for the local feature matching method. Since aliasing may happen when resampling the original image to the geometry of the simulated image, an antialiasing convolution must be applied before resampling. However, the antialiasing convolution itself must also be homography-adaptive. In the scale invariant feature transform (SIFT) or affine-SIFT (ASIFT) method, the similitude or affine rectification scheme of the convolution is applied to solve this problem under similitude or affine mapping. However, these schemes will not work under homographic or perspective mapping. Although the perspective invariant matching method (perspective-SIFT or PSIFT) has been proposed in some references, the antialiasing scheme with perspective-adaption has not been proposed. This paper will show that the standard convolution is not adaptive to the change of planar mapping, and the simulated images under the same simulated camera placement will not be identical if they are resampled from different original images captured from different camera placements. To solve this issue, a natural extension of the standard convolution, the mapping-adaptive convolution (MA-convolution), is proposed, and its mapping-adaption is proved mathematically in this paper. Based on this novel convolution, the homography invariant simulation scheme can be modelled. We have applied the MA-convolution to the antialiasing scheme in the PSIFT method, and the effectiveness of the MA-convolution has been verified experimentally. [ABSTRACT FROM AUTHOR]