Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads

Detection of straight lines in an image is a fundamental requirement for many applications in computer vision. We formulate the straight line detection task as an energy minimization problem. This formulation helps the detection of lines in a global manner in contrast to the local detection methods used in conventional algorithms. As a result the proposed straight line detection algorithm can handle virtually co-located straight lines, slightly curved lines and edge linking in a unified manner. In addition, due to its the global nature, the algorithm is not deceived by image noise giving rise to spurious line segments. Therefore, the proposed algorithm can robustly detect straight lines. The main component of the algorithm is formulating the energy to be minimized. The contribution to this energy function is less at a pixel which is a good candidate to be a member of an existing line segment depending on the directional gradients. A pixel choosing a part of a line segment is costly, but not impossible. This energy optimization is done using dynamic programming snakes. Since the algorithm is a global one and since no gradient calculations are used for local motion of nodes, our algorithm is robust. However, the optimization process takes a longer time than the existing straight line detection algorithms. Results are given for detecting straight lines in indoor environments

Serial versus parallel processing in visual search tasks and IQ

The performance of supporting systems can be significantly affected by aggravating factors that contribute to segmental tunnel lining damage. Based on previous studies and statistical data, the excessive thrust force and defect in support conditions are the crucial factors that can aggravate the damage of segmental linings during the installation stage. This study aims to evaluate the effect of influential factors, including the excessive thrust force, the incomplete contact of loading shoes, configuration of loading shoes, defect gap thickness, and segment type. In addition, a series of numerical simulation are performed to investigate the impact of the influential factors on the performance of three different segmental lining types, including conventional reinforced concrete segment, recycled steel fiber reinforced concrete segment, and new hybrid design recycled steel fiber reinforced concrete segment. The results indicated that numerical modeling can accurately simulate stress concentration and damage patterns of segmental lining in different conditions. This method can provide useful information about the performance of segmental tunnel linings, considering essential phenomena that are neglected in traditional design methods, such as the effect of incomplete contact of the support area, and the thickness of circumferential gap. Numerical analysis of segmental tunnel lining behavior by considering different conditions can help reduce undesirable consequences and construction costs. The results indicated that the increase in the thickness of the gap significantly increase in crack opening and destructive damages. While, the configuration of the loading shoes affects the amount of crack openings, the number and position of these cracks; the effect of this parameter is very lower compared to the presence of defects in the supporting area. Finally, it was concluded that the hybrid design showed high efficiency for all investigated conditions, in terms of structural performance, environmental impact and economic compatibility.

Characterization and generation of straight line segments on triangular cell grid

Dominant vanishing point detection in the wild with application in composition analysis

Fast and accurate visual scene understanding in autonomous vehicles is necessary but still very challenging. An autonomous vehicle must be taught to read the road like a human driver for better controlling the vehicle, so it is important to efficiently detect the road area and road markings. In this paper, we mainly focus on the vanishing point detection and its application in inverse perspective mapping (IPM) for road marking understanding. We first propose a fast and accurate vanishing point detection method for various types of roads, by adopting and improving Weber local descriptor to obtain salient representative texture and orientation information of the road area, and then voting for the dominant vanishing point with a simple line-voting scheme. Experimental results demonstrate that the proposed vanishing point detection approach gains a better performance than some state-of-the-art methods in terms of accuracy and computation time. Furthermore, we introduce the detected vanishing point into the IPM algorithm in the structured road environment, since some important calibration parameters can be automatically calculated by the vanishing point, especially on the rough road. Experiments also show that our proposed vanishing point-based IPM method is adaptive and accurate, which is conducive to the subsequent road marking detection and recognition.

Old and new straight-line detectors: Description and comparison

For decades, the slide drilling system (SDS) has played a critical role in the area of directional drilling. Drilling in the sliding mode is much more time-consuming than drilling in the rotating mode for a SDS. Therefore, it generally takes more time to drill a curved wellbore segment than a straight segment of the same length. Based on this fact, a novel wellbore trajectory design for SDSs is proposed in this paper to reduce the total drilling time and improve economy. The basic idea of our strategy is to replace a long curved wellbore section, e.g., the build-up and drop-off sections, with a series of short circular-arc and straight-line segments in the design stage. Assuming that rotating and sliding modes are only used for straight-line and circular-arc segments, respectively, the total drilling time is reduced by increasing the average rate of penetration. Therefore, the problem lies in how to replace a long circular arc with multiple short circular arcs and straight lines to minimize the total drilling time, which is the aim of this research. In this paper, a piecewise trial strategy is proposed, where the optimized wellbore trajectory is generated by adding circular-arc and straight-line segments with equal length one by one according to the “line first” principle. This strategy converts the original optimization problem, which involves both combinatorial and parameter optimization of unknown dimension, to a much easier one, where there are only three design variables and one set of constraint equations. Due to the multimodal characteristics of the objective function, genetic algorithm (GA) is adopted to obtain the optimal solution. A great reduction of the drilling time is obtained by applying our method to two typical cases, which shows a clear potential for future field applications.

Regarding the autonomous of robot navigation, vanishing point (VP) plays an important role in visual robot applications such as iterative estimation of rotation angle for automatic control as well as scene understanding. Autonomous navigation systems must be able to recognize feature descriptors. Consequently, this navigating ability can help the system to identify roads, corridors, and stairs; ensuring autonomous navigation along the environments mentioned before the vanishing point detection is proposed. In this paper, the authors propose solutions for finding the vanishing point in real time based density-based spatial clustering of applications with noise (DBSCAN). First, we proposed to extract the longest segments of lines from the edge frame. Second, the set of intersection points for each pair of line segments are extracted by computing Lagrange coefficients. Finally, by using DBSCAN the VP is estimated. Preliminary results are performed and tested on a group of consecutive frames undertaken at Nam-gu, Ulsan, South Korea to prove its effectiveness.

Straight line detection in a binary image is a basic but difficult task in image processing and machine vision. Recently, a fast fixed-point BYY harmony learning algorithm has been established to efficiently make model selection automatically during the parameter learning on Gaussian mixture. In this paper, we apply the fixed-point BYY harmony learning algorithm to learning the Gaussians in the dataset of a binary image and utilize the major principal components of the covariance matrices of the estimated Gaussians to represent the straight lines in the image. It is demonstrated well by the experiments that this fixed-point BYY harmony learning approach can both determine the number of straight lines and locate these straight lines accurately in a binary image.

The purpose of this study is to develop an indoor guidance method based on augmented reality (AR) that superimposes annotations on an indoor environment (e.g., wall surfaces) and works on devices with limited computing power such as smartphones. Such a method requires a simple algorithm for the object registration. For the purpose, this paper proposes a two-dimensional (2D) object registration method based on perspective distortion correction using vanishing points in the scene and a stable vanishing point detection method. Current vanishing point detection methods have the problem of slight drift of the vanishing point in the videos despite maintaining a fixed camera position. The proposed method has solved the problem by employing a weighted particle filtering method. Recent approaches often employ deep neural networks (DNNs) for the registration and the stable vanishing point detection, however DNNs require rich resources for computing such as Graphics Processing Unit (GPU). Since the proposed method requires at most two vanishing points for the registration, the method can be said a lightweight method. This paper shows that the proposed weighted particle filtering method suppresses the vanishing point drift better than the conventional moving average method, and that it enables registration of  objects based on the method.

Architectural photogrammetry has been practised for more than a century for the documentation of cultural heritage. Nowadays, the emphasis is on the construction of computer models for virtual reality applications. Since the introduction of the computer, and later the digital camera, research in photogrammetry aims at automation. This thesis reports on research on automation in architectural photogrammetry for efficient reconstruction of detailed building models from one or more, possibly widely separated, digital close-range images. This research lies on the fringes of photogrammetry and computer vision. It treats topics frequently studied in computer vision in a photogrammetric way and offers new solutions. Examples cover interior orientation and reconstruction from a single mage, vanishing point detection, and the wide-baseline stereo problem. A semi-automatic approach is chosen that exploits knowledge of the object shape, such as planarity of facades, rectangular and repeating structures in the building, and shape symmetries. Automatically or manually extracted straight image line features are the main observations in the line-photogrammetric approaches presented in this thesis. Furthermore, the methods developed are characterised by the use of robust direct solutions for approximate value computation, followed by least-squares adjustment in which the knowledge of the shape of the building is processed together with the image line observations. This integral adjustment provides optimal estimates for the object model parameters and facilitates quality assessment.

A line segment on the plane is guarded by n defenders. One invader wants to pass through the line segment, but he has to keep the distance from each defender no less than a given constant. All defenders can move along a straight line only with maximal speed 1. The invader can move on the whole plane with maximal speed greater than 1. Other than this, no kinematic or dynamic constraints are imposed on the defenders' and the invader's motions. The maximal length of the line segment which can be guarded by defenders is established in this paper.

Detection of transmission line is an important and challenging topic in low altitude flight. Different from the traditional transmission line detection method, in which the transmission line is detected based on the straight line, in order to extract the transmission line more completely, this paper proposes a new algorithm of transmission curve detection based on the morphological relation of finite element under the sky background. In the process of image processing, firstly, the whole sky background is extracted, and the line segment of the image under the sky background is extracted. Secondly, in the detection process, according to the idea of finite element, multiple groups of transmission line targets with certain curvature changes are regarded as composed of multiple groups of local line segment pairs. Using the morphological characteristics of the finite element line segment pairs of transmission line, different from other linear targets, line segment screening is carried out to remove the interference of pseudo linear targets. Finally, the finite element line pairs with obvious collinear characteristics are connected by collinear conditions to get the complete extracted transmission line. The experimental results show that this algorithm can detect the transmission line in the field of view and extract them completely.

Automatic road-side extraction from large scale imagemaps