Progressive Probabilistic Hough Transform for line detection

We present a novel Hough Transform algorithm referred to as Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT where Standard HT is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference an the fraction of votes needed to detect reliably lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of the input data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show that in many circumstances PPHT has advantages over the Standard HT

Mobile Robot Perception In Unknown And Unstructured Dynamic Voxel Environments

The Occupancy Grid Method is a probabilistic spatial modelling technique. In this method, the space to be navigated is subdivided into a grid of cells. Associated with each cell is a probability that indicates the likelihood that that cell is occupied. This method was developed for handling a world where the robot was the only moving object. Thus, it lacks the ability to predict the location of moving objects over time.;In my thesis, an Extended Occupancy Grid Method is presented. The extension handles objects moving at constant velocity. In this method, a four-dimensional location-velocity space is subdivided into cells. Each cell (location-velocity combination) is associated with a probability indicating the likelihood that there is an object at that location moving at that velocity. This probabilistic information is updated over time incrementally using the Bayesian reasoning formula. To calculate such a probability, two pieces of probabilistic information are considered, one indicating the likelihood that there are objects at that location and the other indicating the likelihood that an object at a given location moves with a particular velocity. Methods are presented for deriving the probability for each cell in the extended occupancy grid being occupied by combining the original Occupancy Grid Method with motion detection mechanisms such as the Hough transform.;The Hough transform, originally formulated for recognizing lines in a two dimensional space, has been used to recognize lines in a three-dimensional space for identifying initial location and motion information of objects from sensor data. The approach of combining the Occupancy Grid Method with the Hough transform is robust in the case of occlusion. It can be easily extended to handle other motions, such as rotation and acceleration

Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform

Data fusion is a common issue of mobile robotics, computer assisted medical diagnosis or behavioral control of simulated character for instance. However data sources are often noisy, opinion for experts are not known with absolute precision, and motor commands do not act in the same exact manner on the environment. In these cases, classic logic fails to manage efficiently the fusion process. Confronting different knowledge in an uncertain environment can therefore be adequately formalized in the bayesian framework. Besides, bayesian fusion can be expensive in terms of memory usage and processing time. This paper precisely aims at expressing any bayesian fusion process as a product of probability distributions in order to reduce its complexity. We first study both direct and inverse fusion schemes. We show that contrary to direct models, inverse local models need a specific prior in order to allow the fusion to be computed as a product. We therefore propose to add a consistency variable to each local model and we show that these additional variables allow the use of a product of the local distributions in order to compute the global probability distribution over the fused variable. Finally, we take the example of the Randomized Hough Transform. We rewrite it in the bayesian framework, considering that it is a fusion process to extract lines from couples of dots in a picture. As expected, we can find back the expression of the Randomized Hough Transform from the literature with the appropriate assumptions

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

Arbitrary shape detection by genetic algorithms.

Wang Tong.Thesis submitted in: June 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 64-69).Abstracts in English and Chinese.ABSTRACT --- p.I摘要 --- p.IVACKNOWLEDGMENTS --- p.VITABLE OF CONTENTS --- p.VIIILIST OF FIGURES --- p.XIIVChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Hough Transform --- p.2Chapter 1.2 --- Template Matching --- p.3Chapter 1.3 --- Genetic Algorithms --- p.4Chapter 1.4 --- Outline of the Thesis --- p.6Chapter CHAPTER 2 --- HOUGH TRANSFORM AND ITS COMMON VARIANTS --- p.7Chapter 2.1 --- Hough Transform --- p.7Chapter 2.1.1 --- What is Hough Transform --- p.7Chapter 2.1.2 --- Parameter Space --- p.7Chapter 2.1.3 --- Accumulator Array --- p.9Chapter 2.2 --- Gradient-based Hough Transform --- p.10Chapter 2.2.1 --- Direction of Gradient --- p.11Chapter 2.2.2 --- Accumulator Array --- p.14Chapter 2.2.3 --- Peaks in the accumulator array --- p.16Chapter 2.2.4 --- Performance of Gradient-based Hough Transform --- p.18Chapter 2.3 --- Generalized Hough Transform (GHT) --- p.19Chapter 2.3.1 --- What Is GHT --- p.19Chapter 2.3.2 --- R-table of GHT --- p.20Chapter 2.3.3 --- GHT Procedure --- p.21Chapter 2.3.4 --- Analysis --- p.24Chapter 2.4 --- Edge Detection --- p.25Chapter 2.4.1 --- Gradient-Based Method --- p.25Chapter 2.4.2 --- Laplacian of Gaussian --- p.29Chapter 2.4.3 --- Canny edge detection --- p.30Chapter CHAPTER 3 --- PROBABILISTIC MODELS --- p.33Chapter 3.1 --- Randomized Hough Transform (RHT) --- p.33Chapter 3.1.1 --- Basics of the RHT --- p.33Chapter 3.1.2 --- RHT algorithm --- p.34Chapter 3.1.3 --- Advantage of RHT --- p.37Chapter 3.2 --- Genetic Model --- p.37Chapter 3.2.1 --- Genetic algorithm mechanism --- p.38Chapter 3.2.2 --- A Genetic Algorithm for Primitive Extraction --- p.39Chapter CHAPTER 4 --- PROPOSED ARBITRARY SHAPE DETECTION --- p.42Chapter 4.1 --- Randomized Generalized Hough Transform --- p.42Chapter 4.1.1 --- R-table properties and the general notion of a shape --- p.42Chapter 4.1.2 --- Using pairs of edges --- p.44Chapter 4.1.3 --- Extend to Arbitrary shapes --- p.46Chapter 4.2 --- A Genetic algorithm with the Hausdorff distance --- p.47Chapter 4.2.1 --- Hausdorff distance --- p.47Chapter 4.2.2 --- Chromosome strings --- p.48Chapter 4.2.3 --- Discussion --- p.51Chapter CHAPTER 5 --- EXPERIMENTAL RESULTS AND COMPARISONS --- p.52Chapter 5.1 --- Primitive extraction --- p.53Chapter 5.2 --- Arbitrary Shape Detection --- p.54Chapter 5.3 --- Summary of the Experimental Results --- p.60Chapter CHAPTER 6 --- CONCLUSIONS --- p.62Chapter 6.1 --- Summary --- p.62Chapter 6.2 --- Future work --- p.63BIBLIOGRAPHY --- p.6