Feature Extraction Using the Hough Transform

This paper contains a brief literature survey of applications and improvements of the Hough transform, a description of the Hough transform and a few of its algorithms, and simulation examples of line and curve detection using the Hough transform

Cleaning sky survey databases using Hough Transform and Renewal String approaches

Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD. Though relatively small in number these spurious records present a significant problem in many situations where they can become a large proportion of the records potentially of interest to a given astronomer. Accurate and robust techniques are needed for locating and flagging such spurious objects, and we are undertaking a programme investigating the use of machine learning techniques in this context. In this paper we focus on the four most common causes of unwanted records in the SSS: satellite or aeroplane tracks, scratches, fibres and other linear phenomena introduced to the plate, circular halos around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Appropriate techniques are developed for the detection of each of these. The methods are applied to the SSS data to develop a dataset of spurious object detections, along with confidence measures, which can allow these unwanted data to be removed from consideration. These methods are general and can be adapted to other astronomical survey data.Comment: Accepted for MNRAS. 17 pages, latex2e, uses mn2e.bst, mn2e.cls, md706.bbl, shortbold.sty (all included). All figures included here as low resolution jpegs. A version of this paper including the figures can be downloaded from http://www.anc.ed.ac.uk/~amos/publications.html and more details on this project can be found at http://www.anc.ed.ac.uk/~amos/sattrackres.htm

Detection of Airport Runway Edges using Line Detection Techniques

Airport runway detection is a vital aspect for both military and commercial applications. An algorithm to extract runway edges based on edge detection and line detection techniques is discussed. The runway images are initially enhanced by dilation, thresholding and edge detection. Based on some unique characteristics like the runway being gray with two white lines indicating the runway boundaries, long and continuous edges of the runway are considered to be straight lines. The straight lines are detected using Convolution operators pertaining to vertical, 45° or -45° lines. Hough Transform is then applied to fit only the pair of lines corresponding to the runway boundaries in certain orientations. The test results prove that combination of Convolution and Hough transform is very competent in detecting runway edges accurately

New algorithms and technologies for the un-supervised reduction of Optical/IR images

This paper presents some of the main aspects of the software library that has been developed for the reduction of optical and infrared images, an integral part of the end-to-end survey system being built to support public imaging surveys at ESO. Some of the highlights of the new library are: unbiased estimates of the background, critical for deep IR observations; efficient and accurate astrometric solutions, using multi-resolution techniques; automatic identification and masking of satellite tracks; weighted co-addition of images; creation of optical/IR mosaics, and appropriate management of multi-chip instruments. These various elements have been integrated into a system using XML technology for setting input parameters, driving the various processes, producing comprehensive history logs and storing the results, binding them to the supporting database and to the web. The system has been extensively tested using deep images as well as images of crowded fields (e.g. globular clusters, LMC), processing at a rate of 0.5 Mega-pixels per second using a DS20E ALPHA computer with two processors. The goal of this presentation is to review some of the main features of this package.Comment: 12 pages, 9 figures, conferenc

Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform

Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders

The Hough transform estimator

This article pursues a statistical study of the Hough transform, the celebrated computer vision algorithm used to detect the presence of lines in a noisy image. We first study asymptotic properties of the Hough transform estimator, whose objective is to find the line that ``best'' fits a set of planar points. In particular, we establish strong consistency and rates of convergence, and characterize the limiting distribution of the Hough transform estimator. While the convergence rates are seen to be slower than those found in some standard regression methods, the Hough transform estimator is shown to be more robust as measured by its breakdown point. We next study the Hough transform in the context of the problem of detecting multiple lines. This is addressed via the framework of excess mass functionals and modality testing. Throughout, several numerical examples help illustrate various properties of the estimator. Relations between the Hough transform and more mainstream statistical paradigms and methods are discussed as well.Comment: Published at http://dx.doi.org/10.1214/009053604000000760 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3