Vanishing Points Research Articles

Automatic far‐field camera calibration for construction scene analysis

AbstractThe use of cameras for safety monitoring, progress tracking, and site security has grown significantly on construction and civil infrastructure sites over the past decade. Localization of construction resources is a crucial prerequisite for many applications in automated construction management. However, most existing vision‐based methods perform the analysis in the image plane, overlooking the effect of perspective and depth. The manual and labor‐intensive process of traditional calibration techniques, as well as the busy and restrictive construction environment, makes this a challenging task. This study proposes a framework for automatic camera calibration with no manual intervention. The framework utilizes convolutional neural networks for geometrical scene analysis and object detection, which are used to estimate the location of horizon line, vertical vanishing point, as well as objects with known height distributions. This enables automatic estimation of camera parameters and retrieval of scale. The proposed framework is evaluated on images from two major construction projects in Melbourne, Australia. Results show that the proposed method achieves a minimum accuracy of 90% in estimating proximity of points on the ground and can facilitate further development of vision‐based solutions for safety and productivity analysis.

Calibration Method for Central Catadioptric Camera Using Multiple Groups of Parallel Lines and Their Properties

This paper presents an approach for calibrating omnidirectional single‐viewpoint sensors using the central catadioptric projection properties of parallel lines. Single‐viewpoint sensors are widely used in robot navigation and driverless cars; thus, a high degree of calibration accuracy is needed. In the unit viewing sphere model of central catadioptric cameras, a line in a three‐dimensional space is projected to a great circle, resulting in the projections of a group of parallel lines intersecting only at the endpoints of the diameter of the great circle. Based on this property, when there are multiple groups of parallel lines, a group of orthogonal directions can be determined by a rectangle constructed by two groups of parallel lines in different directions. When there is a single group of parallel lines in space, the diameter and tangents at their endpoints determine a group of orthogonal directions for the plane containing the great circle. The intrinsic parameters of the camera can be obtained from the orthogonal vanishing points in the central catadioptric image plane. An optimization algorithm for line image fitting based on the properties of antipodal points is proposed. The performance of the algorithm is verified using simulated setups. Our calibration method was validated though simulations and real experiments with a catadioptric camera.

Visual-Inertial Odometry of Smartphone under Manhattan World

Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.

Did Tim Paint a Vermeer?

Tim’s Vermeer is a recent documentary feature film following engineer and self-described non-artist Tim Jenison’ extensive efforts to “paint a Vermeer” by means of a novel optical telescope and mirror-comparator procedure. His efforts were inspired by the controversial claim that some Western painters as early as 1420 secretly built optical devices and traced passages in projected images during the execution of some of their works, thereby achieving a novel and compelling “optical look.” The authors examine the proposed telescope optics in historical perspective, the particular visual evidence adduced in support of the comparator hypothesis, and the difficulty and efficacy of the mirror-comparator procedure as revealed by an independent artist/copyist’s attempts to replicate the procedure. Specifically, the authors find that the luminance gradient along the rear wall in the duplicate painting is far from being rare, difficult, or even “impossible” to achieve as proponents claimed; in fact, such gradients appear in numerous Old Master paintings that show no ancillary evidence of having been executed with optics. There is indeed a slight bowing of a single contour in the Vermeer original, which one would normally expect to be straight; however, the optical explanation for this bowing implies that numerous other lines would be similarly bowed, but in fact all are straight. The proposed method does not explain some ofthe most compelling “optical” evidence in Vermeer’s works suchas the small disk-shaped highlights, which appear like the blur spots that arise in an out-of-focus projected image. Likewise, the comparator-based explanations for the presence of pinprick holes at central vanishing points and the presence of underdrawings and pentimenti in several of Vermeer’s works have more plausible non-optical explanations. Finally, an independent experimentalattempt to replicate the procedure fails overall to provide support for the telescope claim. In light of these considerations and evidence, the authors conclude that it is extremely unlikely that Vermeer used the proposed mirror-comparator procedure.

Vanishing point detection with direct and transposed fast Hough transform inside the neural network

In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed fast Hough transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed fast Hough transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.

Visual SLAM With Drift-Free Rotation Estimation in Manhattan World

This letter presents an efficient and accurate simultaneous localization and mapping (SLAM) system in man-made environments. The Manhattan world assumption is imposed, with which the global orientation is obtained. The drift-free rotational motion estimation is derived from the structural regularities using line features. In particular, a two-stage vanishing points (VPs) estimation method is developed, which consists of a short-term tracking module to track the clustered line features and a long-term searching module to generate abundant sets of VPs candidates and retrieve the optimal one. A least square problem is constructed and solved to provide refined VPs with the clusters of structural line features every frame. We make full use of the absolute orientation estimation to benefit the whole SLAM process. In particular, we utilize the absolute orientation estimation to increase the localization accuracy in the front end, and formulate a linear batch camera pose refinement problem with the known rotations to improve the real time performance in the back end. Experiments on both synthesized and real-world scenes reveal results with high-precision in the real time camera pose estimation process and high-speed in pose graph optimization process compared with the existing state-of-the-art methods.

D-VPnet: A network for real-time dominant vanishing point detection in natural scenes

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115 fps.

Quasi-Globally Optimal and Near/True Real-Time Vanishing Point Estimation in Manhattan World.

Image lines projected from parallel 3D lines intersect at a common point called the vanishing point (VP). Manhattan world holds for the scenes with three orthogonal VPs. In Manhattan world, given several lines in a calibrated image, we aim to cluster them by three unknown-but-sought VPs. The VP estimation can be reformulated as computing the rotation between the Manhattan frame and camera frame. To estimate three degrees of freedom (DOF) of this rotation, state-of-the-art methods are based on either data sampling or parameter search. However, they fail to guarantee high accuracy and efficiency simultaneously. In contrast, we propose a set of approaches that hybridize these two strategies. We first constrain two or one DOF of the rotation by two or one sampled image line. Then we search for the remaining one or two DOF based on branch and bound. Our sampling accelerates our search by reducing the search space and simplifying the bound computation. Our search achieves quasi-global optimality. Specifically, it guarantees to retrieve the maximum number of inliers on the condition that two or one DOF is constrained. Our hybridization of two-line sampling and one-DOF search can estimate VPs in real time. Our hybridization of one-line sampling and two-DOF search can estimate VPs in near real time. Experiments on both synthetic and real-world datasets demonstrated that our approaches outperform state-of-the-art methods in terms of accuracy and/or efficiency.

Unstructured Road Vanishing Point Detection Using Convolutional Neural Networks and Heatmap Regression

Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this article, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm first adopted a lightweight backbone, i.e., depthwise convolution-modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multiscale supervised learning, heatmap superresolution, and coordinate regression techniques were utilized to carry out fast and high-precision unstructured road VP detection. The empirical results on Kong's data set showed that our proposed approach had the highest detection accuracy in real-time compared with the state-of-the-art methods under various conditions, and achieved the highest speed of 33 fps.

Vanishing point detection using the teaching learning‐based optimisation algorithm

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population-based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

Brain–Robot Interface-Based Navigation Control of a Mobile Robot in Corridor Environments

This paper proposes a brain–robot interface (BRI)-based control strategy in combination with the simultaneous localization and mapping (SLAM) to achieve the navigation and control of a mobile robot in uncertain environments. The BRI is based on steady state visually evoked potentials, utilizing the multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals in such a manner that human intentions can be recognized and motion commands can be produced for the brain controlled robot. The entire system is semi-autonomous since the navigation of mobile robot is commanded by the BRI, and the low-level motion of the mobile robot is autonomous with a designed kinematic controller. By utilizing vanishing points and door plates as the environmental features, a global metric map of the environment has been built by a sequential SLAM algorithm. The main contribution of this paper is the combination of an artificial potential field (APF) and the brain signals, which builds up the relationship between the strength of EEG signals and the intensity of the potential field. Through the proposed EEG-APF method, motion commands that would plan an obstacle-free trajectory in un-structured environments, can be obtained. The entire system has been tested with eight volunteer subjects, and all subjects are able to successfully fulfill manipulating mobile robot in the experiments.

Investigation on parameters optimization to produce hydrochar without carbohydrate carbon

Owing to its superior energy-saving function and strong potential to be applied as a soil amendment and fertilizer, hydrochar has gained wide attention in recent years. However, hydrochar contains greater amounts of labile fractions than traditional biochar and may exacerbate the short-term greenhouse effect. To lower the risk of greenhouse gas release due to labile fractions, optimize parameters must be determined to produce hydrochar without carbohydrate carbon. In addition, the effects of varying feedstocks and process conditions on hydrochar structure as well as its dissolved organic matter (DOM) must be investigated. Spartina alterniflora and pig manure were used to produce two hydrochars (HSAs and HPMs) and their corresponding DOM samples (DSAs and DPMs) at various production temperatures (Tp) and reaction times (tr). The carbohydrate vanishing points (CVPs) were 265 °C-1 h, 250 °C-2 h, and 245 °C-4 h for the HSAs and 260 °C-1 h, 250 °C-2 h, and 250 °C-4 h for the HPMs. With the isolation of DOM, 1.09–4.33% organic carbon of the hydrochar was released. The aromaticity of DSAs decreased with increasing Tp and tr. The molecular weights of the DSAs and DPMs decreased with increasing Tp and tr. This study uncovered hydrochar's molecular structure as well as the content and properties of its labile fractions. Results can be used to help design specific hydrochars for potential applications, based on the trend of the molecular change under the condition of the studied parameters optimization to produce hydrochar in this study.

Exploration of the correlation between welding flaw geometrical parameters and floating potential based on an ultrasonic duplex finite element model

Non-destructive testing (NDT), as an indispensable technique for assessing welding quality, is expected to continuously improve. In this study, to obtain welding flaw geometrical parameters more simply and conveniently, their correlations with floating potential were explored using groups of simulation experiments. All the simulation experiments were performed with a well-designed duplex finite element model, which can simulate ultrasonic guided wave testing (UGWT) of flaws embedded in the metro traction beam butt weld. Experimental results proved that different geometrical parameters of welding flaws exhibited different traits on floating potential. Position, length and orientation parameters of flaw related individually to the energy vanishing point, the number of peaks, the travelling time between peaks, the energy decay rate and so on. Thus, recording the floating potential of the entire UGWT process shows great promise in elucidating flaw geometrical parameter information and makes the proposed approach possible for practical use.

Critical Behavior of Non-intersecting Brownian Motions

We study $n$ non-intersecting Brownian motions corresponding to initial configurations which have a vanishing density in the large $n$ limit at an interior point of the support. It is understood that the point of vanishing can propagate up to a critical time, and we investigate the nature of the microscopic space-time correlations near the critical point and critical time. We show that they are described either by the Pearcey process or by the Airy line ensemble, depending on whether a simple integral related to the initial configuration vanishes or not. Since the Airy line ensemble typically arises near edge points of the macroscopic density, its appearance in the interior of the spectrum is surprising. We explain this phenomenon by showing that, even though there is no gap of macroscopic size near the critical point, there is with high probability a gap of mesoscopic size. Moreover, we identify a path which follows the Airy$_2$ process.

Real-time location estimation for indoor navigation using a visual-inertial sensor

PurposeThe purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.Design/methodology/approachFirst, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.FindingsIn this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.Originality/valueThis paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

Time after History: Derrida’s Two Readings of Heidegger

ABSTRACT The essay situates and dissects Derrida’s two catalytic interventions into Heidegger’s thought on time and history—the seminar Heidegger: The Question of Being & History (1964-5) and the essay Ousia and Grammē (1968). The first aim is to explicate the relation of history to time in Heidegger’s seemingly untroubled passage from a textured and striated temporality into historicity, understood as structured, inscribed, and in a privileged sense, human time. Sustaining the difference of the inseparably intertwined notions of time and history is paramount for Heidegger, yet the place and function accorded to history will mark for Derrida, nothing less than the ultimate impasse of the project of Being and Time. Accordingly, the second aim of the essay is to present and question the productive elision of time and history that Derrida effects through narrative. On the one hand, this elision offers the ground of a forceful critique of the project of Being and Time. On the other hand, this pivotal gesture of deconstruction will always be compelled to seek, through a proliferation of names, those a-temporal and a-historical vanishing points for time and history that undercut the mutual conditioning of the two that it has laboured to effect.

Fast and Accurate Self-calibration Using Vanishing Point Detection in Manmade Environments

Interests of auto-calibration have been increased in several camera systems. This paper presents a novel self-calibration method using fast and accurate vanishing point detection algorithm that works in manmade environments. The proposed algorithm estimates focal length assuming that the principal point is the center of an image to satisfy the orthogonality of three vanishing points. By using proposed vanishing point detection algorithm and minimization of the proposed objective function, the proposed system detects accurate vanishing points with focal length outperforming other methods. The proposed vanishing point detection algorithm detects vanishing points by using J-linkage based method that is more delicate by fragmentation and re-merging strategies. The proposed objective function finally detects vanishing points that meets orthogonality among estimated hypotheses for vanishing points by checking several geometric relationships. We believe that the proposed method can be used for automatic camera calibration, localization of a camera in an autonomous navigation system, and three-dimensional reconstruction of a single-view image.

Mary Wroth, Ovid, and the Metamorphosis of Petrarch

AbstractThe language of arboreal metamorphosis in Lady Mary Wroth’s pastoral song “The Spring Now Come att Last” from Pamphilia to Amphilanthus (1621) may invoke the myth of Apollo and Daphne. However, the Ovidian narrative so central to Petrarchan poetics celebrates the male poet by erasing the female voice. This essay instead explores parallels between Wroth’s poem and the metamorphosis of the Heliades, who turn into poplars while mourning their brother Phaeton in book 2 of the Metamorphoses. Their transformation is predicated on an act of female speech, however precarious and evanescent. This alternative Ovidian scenario offers a model of lyric that capitalizes on the brief resonance that the female voice acquires at the point of vanishing. By deploying it in her song, Wroth not only rewrites Petrarch through Ovid in order to articulate a gendered lyric voice but shows herself a poet attuned to the crucial developments in English lyric of the sixteenth and seventeenth centuries, in particular the complex relationship between the Petrarchan and the Ovidian legacies.

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

In modern railway systems, video surveillance and machine vision analysis have been widely used to detect perimeter intrusions. For pan-tilt-zoom (PTZ) cameras, the machine vision system needs to detect adjustments in PTZ cameras and then automatically determine the new alarm region in real time. In this paper, we propose a deep multi-task learning based algorithm for simultaneous vanishing point (VP) detection and rail segmentation, which can identify camera adjustment from changes in VP, and then automatically determine the alarm region from segmented rails. The multi-task based neural network consists of a feature extraction base network and three sub-task networks. The first sub-task network is a convolution regression network for VP detection. The second sub-task network utilizes an encoder-decoder structure for vanishing region (VR, a fixed region centered on VP) segmentation. The third sub-task network shares the encoder-decoder structure with the VR segmentation task and is used for rail segmentation. The VR segmentation task is activated only at the training stage, serving as an auxiliary task to enhance feature learning ability and increase VP detection accuracy. To further improve the accuracies of VP detection and rail segmentation, low-level features is modulated by high-level semantic information before feeding to the decoder stage. With the help of shared feature extraction and auxiliary training, the proposed VP prediction method needs very small training dataset and outperforms other methods in both efficiency and accuracy.

Оценка точки схода отрезков методом максимального правдоподобия

Оценка точки схода отрезков методом максимального правдоподобия