Straight Line Extraction Research Articles

Optimizing and Implementing Fischlin's Transform for UC-Secure Zero Knowledge

Fischlin's transform (CRYPTO 2005) is an alternative to the Fiat-Shamir transform that enables straight-line extraction when proving knowledge. In this work we focus on the problem of using the Fischlin transform to construct UC-secure zero-knowledge from Sigma protocols, since UC security – that guarantees security under general concurrent composition – requires straight-line (non-rewinding) simulators. We provide a slightly simplified transform that is much easier to understand, and present algorithmic and implementation optimizations that significantly improve the running time. It appears that the main obstacles to the use of Fischlin in practice is its computational cost and implementation complexity (with multiple parameters that need to be chosen). We provide clear guidelines and a simple methodology for choosing parameters, and show that with our optimizations the running-time is far lower than expected. For just one example, on a 2023 MacBook, the cost of proving the knowledge of discrete log with Fischlin is only 0.41ms (on a single core). This is 15 times slower than plain Fiat-Shamir on the same machine, which is a significant multiple but objectively not significant in many applications. We also extend the transform so that it can be applied to batch proofs, and show how this can be much more efficient than individually proving each statement. We hope that this paper will both encourage and help practitioners implement the Fischlin transform where relevant.

VPDETR: End-to-End Vanishing Point DEtection TRansformers

In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing point detection as a set prediction problem, applicable to both Manhattan and non-Manhattan world datasets. By using the positional embedding of anchor points as queries in Transformer decoders and dynamically updating them layer by layer, our method is able to directly input images and output their vanishing points without the need for explicit straight line extraction and candidate points sampling. Additionally, we introduce an orthogonal loss and a cross-prediction loss to improve accuracy on the Manhattan world datasets. Experimental results demonstrate that VPDETR achieves competitive performance compared to state-of-the-art methods, without requiring post-processing.

DETECTION OF HIDDEN EDGES AND CORNERS IN SLAM-BASED INDOOR POINT CLOUDS

Abstract. Mobile mapping systems are commonly used for surveying buildings. The acquisition of the buildings’ indoor spaces with laser scanning or photogrammetry generates data in the form of point clouds. These point clouds are often used to create a model of those buildings, but so far with a low degree of automation. To automate this process, it is important to extract geometric information about corners, edges, and planes from unorganized indoor point clouds. In an indoor scenario consisting of several rooms including furniture and other objects, a point cloud is expected to show occlusions. Therefore, the detection of hidden corners and edges is of importance. In this work one approach based on contour point clouds and one approach based on planes are examined for the detection of corners and edges. Both approaches use RANSAC to extract either straight lines or planes. Through their intersection, edges and corners are determined. To examine the influence of the data quality on the results, the approaches are applied to and evaluated on different datasets of the same area of a building, which are captured by various measurement methods, including mobile mapping systems and terrestrial laser scanning. Therefore, we are creating a ground truth for parts of the building to evaluate the completeness and correctness of the corner detection. The approach based on planes presents itself to be more reliable in noisy and incomplete point clouds. The approach based on contour point clouds indicates advantages in terms of the complexity of a building´s indoor geometry.

RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot

This paper proposes a ray-model-based straight-line extraction method for the grid map of a mobile robot, call RM-Line. First, the edge map is obtained, with the help of the connectivity of the blank grid. Then, points containing complete line information, called active points, are obtained using a screening model. Lastly, a ray model is designed to extraction line segments. We evaluate the algorithm using the number of lines, the average distance from grids to the lines, and the running time. Experiments show that the proposed algorithm has better performance on grid maps compared to the state-of-the-art algorithms.

An automatic method for matching salient structures in optical remote sensing images

ABSTRACT Feature matching in optical remote sensing images usually suffers from significant intensity differences and background variations. In this paper, an effective image matching approach is proposed to match salient structures in optical remote sensing images. The proposed method consists of three main steps. In the first step, EDLines algorithm is implemented to extract straight lines from two corresponding images, and these lines are further processed and divided into line segments for feature matching. In the second step, a robust descriptor namely line segment context (LSC) descriptor is computed for each line segment. An iterative procedure that combines line segment description and feature matching is presented for accurate matching. Finally, the consistency check is implemented to further refine the matching results. We conduct the experiments on pairs of optical remote sensing images, and experimental results show that the proposed method can automatically and robustly generate line segment features and achieve favourable performance compared to existing image matching approaches.

FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism.

Research on steel barrel flattened seam recognition based on machine vision

Aiming at the problems of low efficiency and poor accuracy in manually identifying the flattened weld seam. The method of combining machine vision technology with multi-thread and multi-process is proposed, and the detection and recognition system of flattened weld seam is designed. OTSU adaptive threshold algorithm combined with Canny operator edge detection method is used for image segmentation. The probabilistic Hough transform algorithm is used to extract straight lines to identify flattened weld seam. OpenCV function library and Python are used to implement image processing algorithm, and PyQt5 is used to design human-computer interface to monitor the status of weld seam detection in real time. Meanwhile, a processing pool for parallel processing of recognition algorithm is established, and Modbus communication and image acquisition are realized by multi-thread programming. The performance of multi-core CPU is fully utilized. The experimental results show that the system meets the design requirements, has the characteristics of small amount of calculation and accurate identification, improves the processing efficiency, and solves the practical production problems.

Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

The purpose of this article is to identify football and related environmental variables through its VR images under the current situation where the vision system has become the only way for football robots to obtain the external environment, so as to improve the chances of winning the game. First, this article uses color filters to enhance the VR football data to distinguish between shadow games and aliens. The best environment for enhancing the image is automatically determined by the Ostu method, so that the image is not affected by shadows as much as possible, and the outline of the image can be sealed. At the same time, using the humanoid medium-sized football game machine system as the platform, the relevant processing algorithms of the humanoid football robot front-view system are studied to realize the work of color image segmentation, edge extraction, straight line extraction, cross-line recognition and target post-recognition. PA-SIFT algorithm is used to quickly identify the graphics. Data verification results show that the recognition rate of the PA-SIFT algorithm can reach 96%, ensuring the real-time and feasibility of the algorithm. In addition, the divide-and-conquer algorithm and the related processing algorithm of the vision system are combined to determine the central area of the image, so that the algorithm is not affected by the external environment, and the algorithm is robust and can improve actual competition.

A Fast-Efficient parallel processing algorithm for straight line detection

<p>This work presents a novel method for detecting straight lines in an image at a very high speed with optimum number of processors and their functionalities. The method can be used to extract straight lines directly from an image without noise removal and pre-processing. First the square image is converted to a binary edge image using a parallel edge detection mechanism. The parallel edge detection mechanism used in this work is capable of producing edge image within a short time. Then the binary square image is transferred to a system having large number of Processing Elements (PEs). A PE has only limited jobs such as pixel scanning, compare line length with nearby PEs and transmit data to the Main Control Unit (MCU). The MCU collects data from all PEs and evaluates straight lines. Even if the number of PEs is high, it is comparatively very much less than the parallel Hough Transform method and practically implementable using recent ULSI technologies.</p>

Fusing texture, edge and line features for smoke recognition

To improve recognition accuracy, the authors fuse texture, edge and line information to propose a feature extraction method for smoke recognition. The Canny operator is proposed to generate an edge image from an original image, and then adopt the Hough transform to extract straight lines from the edge image. The lines are rasterised to generate a discrete line image and two local patterns are proposed for the edge and line images. The first one is local boundary summation pattern (LBSP) that computes the sum of binary pixel values along the boundary of a local region around a centre pixel. The second one is called local region summation pattern (LRSP) that sums up the binary values of pixels in a local region around the centre pixel. Besides LBSP and LRSP, LBPs with three mapping modes (LBP_M3) to achieve traditional texture information are also extracted. Finally, the authors concatenate the histograms of LBP_M3, LBSP and LRSP to generate a feature vector, and use support vector machine for classifying and testing. Experiments show that authors’ method outperforms most of existing traditional methods for smoke recognition. Although this method has low dimensional features, it also obtains good performance for multi-class texture classification.

Multi-Lane Detection Using CNNs and A Novel Region-grow Algorithm

In this paper, we propose a novel approach to detect lanes robustly in different scenarios. Theoretically, HT (Hough Transform) can extract straight lines efficiently from images, but it cannot distinguish whether the straight lines are belong to lane-markings. To solve this problem, we integrate a simple convolutional neural networks modified from Lenet and geometry constraints to distinguish the types of lines. And in the following part, a region-grow algorithm is adopted in this paper to fit lanes, which is realized by growing local ROI (Region- of-Interest) gradually. We use RANSAC to extract the main direction in local ROI and guide the growth of the ROI. At the same time, in order to ensure right growth of ROI when the lane is dashed, we use a DVF (Direction Vector Field, modified from GVF [19]), which has large capture range and is generated by the edge-direction map. Tests of the proposed approach under kinds of conditions are discussed.

Automatic Registration of Optical Images with Airborne LiDAR Point Cloud in Urban Scenes Based on Line-Point Similarity Invariant and Extended Collinearity Equations.

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an “infinitesimal feature analysis method” fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level.

A Quantitative Evaluation Model of Interactive Whiteboard Classroom Based on K-Means Algorithm

In order to apply the K-means algorithm to the quantitative evaluation model of interactive whiteboard class, the automatic correction method of the natural scene text plane image in the interactive whiteboard class is put forward. This method makes full use of the whiteboard border line information, and completes the whiteboard correction through the Canny operator edge detection, the PPHT straight line extraction, the K-means algorithm linear clustering, H matrix transformation and other steps. Finally, experiments are carried out to test the performance of the algorithm. The research results show that the algorithm has a high success rate of correction for basic simple background and simple content whiteboard, and the algorithm performance is relatively stable. It can be seen that the K-means algorithm has broad application prospects in the quantitative evaluation model of interactive whiteboard classroom.

A New Accurate and Fast Homography Computation Algorithm for Sports and Traffic Video Analysis

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.

Rapid Line-Extraction Method for SAR Images Based on Edge-Field Features

This letter proposes a rapid line-extraction (RLE) method for synthetic aperture radar (SAR) images. RLE first transforms an image in the space domain into an image in the frequency domain. Then, using the central-slice theorem, RLE skilfully maps the image in the frequency domain into a parameter space, which effectively accelerates the straight-line extraction process. Unlike the traditional Hough transform, RLE is performed directly on an edge-field image rather than on a binary edge map. Theoretical analysis proves the advantages of using the edge-field map. Notably, the computational complexity can be greatly reduced relative to the complexity of obtaining a binary edge map, and the method can efficiently avoid the negative influence of false edges in the binary edge map. More importantly, because speckle, clutter, and blurred edges in real-world images decrease the sharpness of peaks, edge-field images that include the strength and direction information of SAR images are adopted to reduce the diffusion of peaks and improve the detection accuracy. Experimental studies show that RLE works independently, is robust to noise, has low computational complexity, achieves high true-positive detection rates, and yields satisfactory detection precision.

Real-time Accurate Runway Detection based on Airborne Multi-sensors Fusion

<p>Existing methods of runway detection are more focused on image processing for remote sensing images based on computer vision techniques. However, these algorithms are too complicated and time-consuming to meet the demand for real-time airborne application. This paper proposes a novel runway detection method based on airborne multi-sensors data fusion which works in a coarse-to-fine hierarchical architecture. At the coarse layer, a vision projection model from world coordinate system to image coordinate system is built by fusing airborne navigation data and forward-looking sensing images, then a runway region of interest (ROI) is extracted from a whole image by the model. Furthermore, EDLines which is a real-time line segments detector is applied to extract straight line segments from ROI at the fine layer, and fragmented line segments generated by EDLines are linked into two long runway lines. Finally, some unique runway features (e.g. vanishing point and runway direction) are used to recognise airport runway. The proposed method is tested on an image dataset provided by a flight simulation system. The experimental results show that the method has advantages in terms of speed, recognition rate and false alarm rate.</p>

Approach for transient interference detection based on straight line extraction for high‐frequency sky‐wave radar

Transient interference cancellation is necessary for high-frequency sky-wave radar and usually requires accurate interference detection and localisation which is complicated due to the non-uniform noise level caused by sea-clutter mitigation in Doppler domain. A novel approach is proposed by converting the detection of interference into a problem of straight line extraction in the constructed fast- and slow-time images. The line edges are detected by an edge detector, such as Canny method, followed by Hough transform. Then, each edge is classified as a rising, falling or false-alarm edge based on multi-threshold segmentation of image sub-blocks. Experimental results demonstrate the effectiveness of the proposed approach.

A Markov-Random-Field Approach for Extracting Straight-Line Segments of Roofs From High-Resolution Aerial Images

This paper proposes a method for extracting groups of straight lines that represent roof boundary sides and roof ridgelines from high-resolution aerial images using corresponding airborne laser scanner (ALS) roof polyhedrons as initial approximations. Our motivation for this research is the possibility of future use of resulting image-space straight lines in several applications. For example, straight lines that represent roof boundary sides and precisely extracted from a high-resolution image can be back-projected onto the ALS-derived building polyhedron for refining the accuracy of its boundary. The proposed method is based on two main steps. First, straight lines that are candidates to represent roof ridgelines and roof boundary sides of a building are extracted from the aerial image. The ALS-derived roof boundary sides and roof ridgelines are projected onto the image space, and bolding boxes are constructed around the projected straight lines while considering the projection errors. This allows the extraction of straight lines within the bounding boxes. Second, a group of straight lines that represent roof boundary sides and roof ridgelines of a selected building is obtained through the optimization of a Markov random field-based energy function using the genetic algorithm optimization method. The formulation of this energy function considers several attributes, such as the proximity of the extracted straight lines to the corresponding projected ALS-derived roof polyhedron and the rectangularity (extracted straight lines that intersect at nearly 90o). In order to validate the proposed method, four experiments were accomplished using high-resolution aerial images, along with interior and exterior orientation parameters, and available ALS-derived building roof polyhedrons. The obtained results have shown that the method works properly and this will be qualitatively and quantitatively demonstrated in this research.

Wheat rows detection at the early growth stage based on Hough transform and vanishing point

A simple and effective wheat rows detection method is presented in this paper. It includes five steps: image segmentation, feature points extraction, candidate wheat rows estimation, vanishing point detection and real wheat rows detection. Firstly, a color image was converted into gray-level image and segmented in two parts, the foreground and background. Secondly, to extract feature points indicating centers of wheat rows, a moving window and multiple interlaced scanning strategies were constructed. Thirdly, the Hough transform method was employed to extract straight lines for estimating all possible candidate wheat rows. Fourthly, k-means clustering was performed to look for a clustering center representing the vanishing point. And lastly the real wheat rows were extracted based on the vanishing point. Test results indicate that the proposed method can effectively detect wheat rows at the early growth stage, and the detected rate is up to 90%.

A fast and effective algorithm based on improved hough transform

Comparing to those problems of High complexity and endpoint being difficult to be detected in traditional Line Detection Algorithm Based on Hough Transform, a fast and effective straight line extraction algorithm based on improved Hough Transform(HT) is put forward. First, a reduction sequence parameter array for the cumulative parameter matrix need to establish in proposed algorithm; then the parameter track line is established by analyzing the first parameter of the array, and with the parameter track line the endpoint of the line can be confirmed and the line is extracted. Eventually, the experimental results show that principle of the algorithm is simple, which can effectively solve the traditional HT problems, such as the not high enough accuracy, the complex calculation. Meanwhile the algorithm which is suitable for parallel processing is robust to handle different types of the image data.