An Extension to Hough Transform Based on Gradient Orientation

The Hough transform is one of the most common methods for line detection. In this paper we propose a novel extension of the regular Hough transform. The proposed extension combines the extension of the accumulator space and the local gradient orientation resulting in clutter reduction and yielding more prominent peaks, thus enabling better line identification. We demonstrate benefits in applications such as visual quality inspection and rectangle detection.Comment: Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2015, Year

Simulation Models for Straight Lines Images Detection Using Hough Transform

The Hough transform (HT) is a robust parameter estimator of multidimensional features in images. The HT is an established technique which evidences a shape by mapping image edge points into a parameter space. Recently, the formulation of the HT has been extended to extract analytic arbitrary shapes which change their appearance according to similarity transformations. It finds many applications in astronomical data analysis. It enables, in particular, to develop autoadaptive, fast algorithms for the detection of automated arc line identification. The HT is a technique which is used to isolate curves of a given shape in an image. The classical HT requires that the curve be specified in some parametric form and, hence is most commonly used in the detection of regular curves. The HT has been generalized so that it is capable of detecting arbitrary curved shapes

Automatic detection of weld defects based on hough transform

Weld defect detection is an important application in the field of Non-Destructive Testing (NDT). These defects are mainly due to manufacturing errors or welding processes. In this context, image processing especially segmentation is proposed to detect and localize efficiently different types of defects. It is a challenging task since radiographic images have deficient contrast, poor quality and uneven illumination caused by the inspection techniques. The usual segmentation technique uses a region of interest ROI from the original image. In this article, a robust and automatic method is presented to detect linear defect from the original image without selection of ROI based on canny detector and a modified `Hough Transform' technique. This task can be subdivided into the following steps: firstly, preprocessing step with Gaussian filter and contrast stretching; secondly, segmentation technique is used to isolate weld region from background and non-weld using Adaptative Thresholding and to extract edges; thirdly, detection, location of linear defect and limiting the welding area by Hough Transform. The experimental results show that our proposed method gives good performance for industrial radiographic images

The vector-gradient Hough transform

The paper presents a new transform, called vector-gradient Hough transform, for identifying elongated shapes in gray-scale images. This goal is achieved not only by collecting information on the edges of the objects, but also by reconstructing their transversal profile of luminosity. The main features of the new approach are related to its vector space formulation and the associated capability of exploiting all the vector information of the luminosity gradien

Automatic visual inspection of placement of bare dies in multichip modules

Multichip Modules are gaining lot of popularity in today\u27s IC technology, as they are good solutions for high density packaging. This thesis presents a method for checking the placement of bare dies on a common substrate of an MCM. This testing is done using Automatic Visual Inspection (AVI), which is better and more reliable, compared to manual inspection. Comparison is the basis in this thesis to detect faults in an MCM. The MCM to be tested is compared with a known good ideal MCM using image processing techniques. The mismatches, if any, between these two images, i.e. image of an MCM which is being tested and image of known good reference MCM, are evaluated to find the exact location and nature of the fault. This AVI is implemented completely in software using C language. Test cases and their results are presented

A Phase Coded Disk Approach To Thick Curvilinear Line Detection

This paper examines the well-known problem of line detection, but where the lines are wider than one pixel. The motivation behind the paper is the extraction of road information from high resolution photogrammetry and Light Detection and Ranging (LIDAR) data. Wide lines cause varying problems during detection. The Hough or Radon transform approaches do not find the road centrelines accurately; diagonals of the thick lines are found instead whilst other methods also tend to be error prone. Our approach convolves a raw, pixelated, binary road classification with a complex-valued disk. The technique provides three separate pieces of information about the road or thick line: the centreline, the direction and the width of the road at any point along the centreline. The road centreline can be detected from the position of the peak of the magnitude image resulting from the complex convolution. Road width can also be estimated from the magnitude peak whilst the direction of the road may be obtained from the phase image

Vision based road lane detection system for vehicles guidance

Driver support system is one of the most important feature of the modern vehicles to ensure driver safety and decrease vehicle accident on roads. Apparently, the road lane detection or road boundaries detection is the complex and most challenging tasks. It is includes the localization of the road and the determination of the relative position between vehicle and road. A vision system using on-board camera looking outwards from the windshield is presented in this paper. The system acquires the front view using a camera mounted on the vehicle and detects the lanes by applying few processes. The lanes are extracted using Hough transform through a pair of hyperbolas which are fitted to the edges of the lanes. The proposed lane detection system can be applied on both painted and unpainted roads as well as curved and straight road in different weather conditions. The proposed system does not require any extra information such as lane width, time to lane crossing and offset between the center of the lanes. In addition, camera calibration and coordinate transformation are also not required. The system was investigated under various situations of changing illumination, and shadows effects in various road types without speed limits. The system has demonstrated a robust performance for detecting the road lanes under different conditions