The Detection and Quantification of Straight-Lined Irregularities on Surfaces

Under the microscope, scratches or abrasions on hard otherwise flat surfaces are usually revealed as straight-lined irregularities. At a more macroscopic level creases in thin sheets such as of paper and textile fabrics are also observed to be straight-lined. A computer-aided image analytical method is described here not only for identifying such features but also for counting them, measuring their lengths and evaluating their contrast. Further measures are derived that are in accord with the qualitative visual impact of each line within the milleau of lines in the original image. The method makes use of a parametric transformation from two orthogonally-illuminated images of the surface using the equation p=x∙cos(θ) + y∙sin(θ) where x,y are image coordinates, θ is the angle that a straight line makes with the x-axis and p is the perpendicular distance of that line from the coordinate origin. As distinct from the well-known Hough transform, estimates are made for θ at all points in the initial images that are illuminated at a low angle from two orthogonal directions

Linear feature detection on SAR images

We aim at defining an automatic or semi-automatic method for the extraction of road network in radar images . To realize the low level detection, line detectors are proposed . They extract linear features which become candidates for road segments . Two loca l structure detectors are developed taking into account the speckle phenomenon, and an extensive study of their behaviour as wel l as the behaviour of the fusion of their responses is made (theoretical detection and false alarm probabilities are evaluated an d computed on real images) . One of this detector is based on an edge detector widely used in coherent imagery, and the secon d one is based on a specific adapted filtering . The line detection is eventually made by the fusion of both detector responses .Dans l'objectif d'établir une méthode de détection semi-automatique du réseau routier sur des images radar, des détecteur de lignes sont développés. Ils ont pour but d'extraire des structures linéaires de l'image, qui seront ensuite considérées comme des segments « candidats routes ». Deux détecteurs de lignes prenant en compte les statistiques du speckle sont proposés, et une étude détaillée de leurs comportements et du comportement de leur fusion (probabilités de détection et de fausse alarme) est réalisée. L'un de ces détecteurs est fondé sur un détecteur de contours largement utilisé en imagerie radar, et l'autre plus original sur un filtrage adapté. La détection de lignes est finalement réalisée par la fusion des réponses de ces deux détecteurs

Fully-Automated Packaging Structure Recognition of Standardized Logistics Assets on Images

Innerhalb einer logistischen Lieferkette müssen vielfältige Transportgüter an zahlreichen Knotenpunkten bearbeitet, wiedererkannt und kontrolliert werden. Dabei ist oft ein großer manueller Aufwand erforderlich, um die Paketidentität oder auch die Packstruktur zu erkennen oder zu verifizieren. Solche Schritte sind notwendig, um beispielsweise eine Lieferung auf ihre Vollständigkeit hin zu überprüfen. Wir untersuchen die Konzeption und Implementierung eines Verfahrens zur vollständigen Automatisierung der Erkennung der Packstruktur logistischer Sendungen. Ziel dieses Verfahrens ist es, basierend auf einem einzigen Farbbild, eine oder mehrere Transporteinheiten akkurat zu lokalisieren und relevante Charakteristika, wie beispielsweise die Gesamtzahl oder die Anordnung der enthaltenen Packstücke, zu erkennen. Wir stellen eine aus mehreren Komponenten bestehende Bildverarbeitungs-Pipeline vor, die diese Aufgabe der Packstrukturerkennung lösen soll. Unsere erste Implementierung des Verfahrens verwendet mehrere Deep Learning Modelle, genauer gesagt Convolutional Neural Networks zur Instanzsegmentierung, sowie Bildverarbeitungsmethoden und heuristische Komponenten. Wir verwenden einen eigenen Datensatz von Echtbildern aus einer Logistik-Umgebung für Training und Evaluation unseres Verfahrens. Wir zeigen, dass unsere Lösung in der Lage ist, die korrekte Packstruktur in etwa 85% der Testfälle unseres Datensatzes zu erkennen, und sogar eine höhere Genauigkeit erzielt wird, wenn nur die meist vorkommenden Packstücktypen betrachtet werden. Für eine ausgewählte Bilderkennungs-Komponente unseres Algorithmus vergleichen wir das Potenzial der Verwendung weniger rechenintensiver, eigens designter Bildverarbeitungsmethoden mit den zuvor implementierten Deep Learning Verfahren. Aus dieser Untersuchung schlussfolgern wir die bessere Eignung der lernenden Verfahren, welche wir auf deren sehr gute Fähigkeit zur Generalisierung zurückführen. Außerdem formulieren wir das Problem der Objekt-Lokalisierung in Bildern anhand selbst gewählter Merkmalspunkte, wie beispielsweise Eckpunkte logistischer Transporteinheiten. Ziel hiervon ist es, Objekte präziser zu lokalisieren, als dies insbesondere im Vergleich zur Verwendung herkömmlicher umgebender Rechtecke möglich ist, während gleichzeitig die Objektform durch bekanntes Vorwissen zur Objektgeometrie forciert wird. Wir stellen ein spezifisches Deep Learning Modell vor, welches die beschriebene Aufgabe löst im Fall von Objekten, welche durch vier Eckpunkte beschrieben werden können. Das dabei entwickelte Modell mit Namen TetraPackNet wird evaluiert mittels allgemeiner und anwendungsbezogener Metriken. Wir belegen die Anwendbarkeit der Lösung im Falle unserer Bilderkennungs-Pipeline und argumentieren die Relevanz für andere Anwendungsfälle, wie beispielweise Kennzeichenerkennung

Implementation of a real time Hough transform using FPGA technology

This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions