Realtime Color Stereovision Processing

Recent developments in aviation have made micro air vehicles (MAVs) a reality. These featherweight palm-sized radio-controlled flying saucers embody the future of air-to-ground combat. No one has ever successfully implemented an autonomous control system for MAVs. Because MAVs are physically small with limited energy supplies, video signals offer superiority over radar for navigational applications. This research takes a step forward in real time machine vision processing. It investigates techniques for implementing a real time stereovision processing system using two miniature color cameras. The effects of poor-quality optics are overcome by a robust algorithm, which operates in real time and achieves frame rates up to 10 fps in ideal conditions. The vision system implements innovative work in the following five areas of vision processing: fast image registration preprocessing, object detection, feature correspondence, distortion-compensated ranging, and multi scale nominal frequency-based object recognition. Results indicate that the system can provide adequate obstacle avoidance feedback for autonomous vehicle control. However, typical relative position errors are about 10%-to high for surveillance applications. The range of operation is also limited to between 6 - 30 m. The root of this limitation is imprecise feature correspondence: with perfect feature correspondence the range would extend to between 0.5 - 30 m. Stereo camera separation limits the near range, while optical resolution limits the far range. Image frame sizes are 160x120 pixels. Increasing this size will improve far range characteristics but will also decrease frame rate. Image preprocessing proved to be less appropriate than precision camera alignment in this application. A proof of concept for object recognition shows promise for applications with more precise object detection. Future recommendations are offered in all five areas of vision processing

Vehicle license plate detection and recognition

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%. After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably. The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features. Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%. The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulIncludes bibliographical references (pages 67-73)

Detection and Recognition of Traffic Signs Inside the Attentional Visual Field of Drivers

Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver\u27s 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver

Design, Commissioning and Performance of the PIBETA Detector at PSI

We describe the design, construction and performance of the PIBETA detector built for the precise measurement of the branching ratio of pion beta decay, pi+ -> pi0 e+ nu, at the Paul Scherrer Institute. The central part of the detector is a 240-module spherical pure CsI calorimeter covering 3*pi sr solid angle. The calorimeter is supplemented with an active collimator/beam degrader system, an active segmented plastic target, a pair of low-mass cylindrical wire chambers and a 20-element cylindrical plastic scintillator hodoscope. The whole detector system is housed inside a temperature-controlled lead brick enclosure which in turn is lined with cosmic muon plastic veto counters. Commissioning and calibration data were taken during two three-month beam periods in 1999/2000 with pi+ stopping rates between 1.3*E3 pi+/s and 1.3*E6 pi+/s. We examine the timing, energy and angular detector resolution for photons, positrons and protons in the energy range of 5-150 MeV, as well as the response of the detector to cosmic muons. We illustrate the detector signatures for the assorted rare pion and muon decays and their associated backgrounds.Comment: 117 pages, 48 Postscript figures, 5 tables, Elsevier LaTeX, submitted to Nucl. Instrum. Meth.

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Simulations of the physics and electronics in 2D semiconductor pixel detectors

In this thesis, simulations have been developed of entire 2D Semiconductor pixel detectors, including the physics and electronic responses. The most important concept of this is the separation of simulation into three distinct physical regimes, which should each be simulated in a different manner. The first stage is a particle level simulation of X-Ray photons and their secondary particles interacting with the detector, and recording where interactions depositing energy in the sensitive layer occur. This uses Geant4, as the energy range in use lies just within Geant4’s minimum energy bound. The second stage is a charge cloud simulation of the interaction energy conversion to electron hole pairs, and then the movement of these electrons or holes in the sensitive layer towards the collection area or pixels. This uses an analytic solution to the problem of a charge cloud moving and interacting with itself within a semiconductor, this work is based upon the earlier HORUS simulation and more detailed lattice simulations by R. F. Fowler, et al. The third stage is an electronics level simulation of how the collected charge is manipulated by the detector into a digital read out signal. This is done with a collection of functions representing the response of electronic components to a signal, that can be used in turn to represent the electronic response to an electrical signal. Work was also conducted to develop a model of simulating the charge plasma effects expected to be seen in European XFEL detectors at high charge densities, however a lack of suitable data to test or tune the model renders this a theoretical exercise. Simulations using the software have been compared to data taken with real detectors to test the accuracy of the simulation, with a focus on the charge cloud simulation. The software has been written to be adaptable so that it can simulate all three main detectors at European XFEL, this adaptability then means that it can in principle be used for any other pixel detector

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification

Scientific Prospects for Hard X-ray Polarimetry

X-ray polarimetry promises to give qualitatively new information about high-energy sources. Examples of interesting source classes are binary black hole systems, rotation and accretion powered neutron stars, Microquasars, Active Galactic Nuclei and Gamma-Ray Bursts. Furthermore, X-ray polarimetry affords the possibility for testing fundamental physics, e.g. to observe signatures of light bending in the strong gravitational field of a black hole, to detect third order Quantum Electrodynamic effects in the magnetosphere of Magnetars, and to perform sensitive tests of Lorentz Invariance. In this paper we discuss scientific drivers of hard (>10 keV) X-ray polarimetry emphasizing how observations in the hard band can complement observations at lower energies (0.1 - 10 keV). Subsequently, we describe four different technical realizations of hard X-ray polarimeters suitable for small to medium sized space borne missions, and study their performance in the signal-dominated case based on Monte Carlo simulations. We end with confronting the instrument requirements for accomplishing the science goals with the capabilities of the four polarimeters.Comment: Accepted for publication in Astroparticle Physic