A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works

on a fast discrete straight line segment detection

Detecting lines is one of the fundamental problems in image processing. In addition for real time applications, detection should be achieved in real time. In this paper weinvestigate the use of fast trigger processor technology used in highenergy physics experiments. We propose a method for detecting discrete straight lines segments in binary images based on a simple resistor network trigger processor

Soccer line mark segmentation and classification with stochastic watershed transform

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detection rely on edge detection and Hough transform, but radial distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment and classify line markings. First, line points are segmented thanks to a stochastic watershed transform that is robust to radial distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball. The line points are then linked to primitive structures (straight lines and ellipses) thanks to a very efficient procedure that makes no assumptions about the number of primitives that appear in each image. The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed strategy is more robust and accurate than existing approaches, achieving successful line mark detection even in challenging conditions.Comment: 18 pages, 11 figure

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

Thick Line Segment Detection with Fast Directional Tracking

International audienceThis paper introduces a fully discrete framework for a new straight line detector in gray-level images, where line segments are enriched with a thickness parameter intended to provide a quality criterion on the extracted feature. This study is based on a previous work on interactive line detection in gray-level images. At first, a better estimation of the segment thickness and orientation is achieved through two main improvements: adaptive directional scans and control of assigned thickness. Then, these advances are exploited for a complete unsupervised detection of all the line segments in an image. The new thick line detector is left available in an online demonstration

Feature recognition and obstacle detection for drive assistance in indoor environments

The goal of this research project was to develop a robust feature recognition and obstacle detection method for smart wheelchair navigation in indoor environments. As two types of depth sensors were employed, two different methods were proposed and implemented in this thesis. The two methods combined information of colour, edge, depth and motion to detect obstacles, compute movements and recognize indoor room features. The first method was based on a stereo vision sensor and started with optimizing the noisy disparity images, then, RANSAC was used to estimate the ground plane, followed by a watershed based image segmentation algorithm for ground pixel classification. Meanwhile, a novel algorithm named a standard deviation ridge straight line detector was performed to extract straight lines from the RGB images. The algorithm is able to provide more useful information than using the Canny edge detector and the Hough Transform. Then, the novel drop-off detection and stairs-up detection algorithms based on the proposed straight line detector were carried out. Moreover, the camera movements were calculated by optical flow. The second method was based on a structured light sensor. After RANSAC ground plane estimation, morphology operations were applied to smooth the ground surface area. Then, an obstacle detection algorithm was carried out to create a top-down map of the ground plane using inverse perspective mapping and segment obstacles using a region growing-based algorithm. Both the drop-off and open door detection algorithms employ the straight lines extracted from depth discontinuities maps. The performance and accuracy of the two proposed methods were evaluated. Results show that the ground plane classification using the first method achieved 98.58% true positives, and the figure improved with the second method to 99%. The drop-off detection algorithms using the first method also achieved good results, with no false negatives found in the test video sequences. The system provided the top-down maps of the surroundings to detect and segment obstacles correctly. Overall, the results showing accurate distances to various detected indoor features and obstacles, suggests that this proposed colour/edge/motion/depth approach would be useful as a navigation aid through doorways and hallways