The unsupervised learning algorithm for detecting ellipsoid objects

This paper is devoted to the analysis and implementation of the algorithms for automatic detection of the circular objects in the image. The practical aim of this task is development of the algorithm for automatic detection of log abuts in the images of roundwood batches. Based on literature review four methods were chosen for the further analysis and the best performance out of them was provided by ELSD algorithm. Some modifications were implemented to the algorithm to fulfill the requirements of the given task. After all, the modified ELSD algorithm was tested on the dataset of the images. The relative accuracy of the algorithm in comparison with manual measurement is 95.2% for the images with total area of background scene less than 20%. © 2019 International Association of Computer Science and Information Technology

Joint A Contrario Ellipse and Line Detection.

This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TPAMI.2016.2558150We propose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line segment or an elliptical arc is present (model validation). If both interpretations are possible for the same region, the detector chooses the one that best explains the data (model selection ). We describe a statistical criterion based on the a contrario theory, which serves for both validation and model selection. The experimental results highlight the performance of the proposed approach compared to state-of-the-art detectors, when applied on synthetic and real images.This work was partially funded by the Qualcomm postdoctoral program at École Polytechnique Palaiseau, a Google Faculty Research Award, the Marie Curie grant CIG-334283-HRGP, a CNRS chaire d’excellence and chaire Jean Marjoulet, and EPSRC grant EP/L010917/1

High-Level Facade Image Interpretation using Marked Point Processes

In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planning, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evidence for the occurrence of individual object classes which we derive from data, and prior knowledge which guides the image interpretation in its entirety. We present a three-step procedure which generates features that are suited to describe relevant objects, learns a representation that is suited for object detection, and that enables the image interpretation using the results of object detection while incorporating prior knowledge about typical configurations of facade objects, which we learn from training data. According to these three sub-tasks, our major achievements are: We propose a novel method for facade image interpretation based on a marked point process. Therefor, we develop a model for the description of typical configurations of facade objects and propose an image interpretation system which combines evidence derived from data and prior knowledge about typical configurations of facade objects. In order to generate evidence from data, we propose a feature type which we call shapelets. They are scale invariant and provide large distinctiveness for facade objects. Segments of lines, arcs, and ellipses serve as basic features for the generation of shapelets. Therefor, we propose a novel line simplification approach which approximates given pixel-chains by a sequence of lines, circular, and elliptical arcs. Among others, it is based on an adaption to Douglas-Peucker's algorithm, which is based on circles as basic geometric elements We evaluate each step separately. We show the effects of polyline segmentation and simplification on several images with comparable good or even better results, referring to a state-of-the-art algorithm, which proves their large distinctiveness for facade objects. Using shapelets we provide a reasonable classification performance on a challenging dataset, including intra-class variations, clutter, and scale changes. Finally, we show promising results for the facade interpretation system on several datasets and provide a qualitative evaluation which demonstrates the capability of complete and accurate detection of facade objectsHigh-Level Interpretation von Fassaden-Bildern unter Benutzung von Markierten PunktprozessenDas Thema dieser Arbeit ist die Interpretation von Fassadenbildern als wesentlicher Beitrag zur Erstellung hoch detaillierter, semantisch reichhaltiger dreidimensionaler Stadtmodelle. In rektifizierten Einzelaufnahmen von Fassaden detektieren wir relevante Objekte wie Fenster, Türen und Balkone, um daraus eine Bildinterpretation in Form von präzisen Positionen und Größen dieser Objekte abzuleiten. Die digitale dreidimensionale Rekonstruktion urbaner Regionen ist ein aktives Forschungsfeld mit zahlreichen Anwendungen, beispielsweise der Herstellung digitaler Kartenwerke für Navigation, Stadtplanung, Notfallmanagement, Katastrophenschutz oder die Unterhaltungsindustrie. Detaillierte Gebäudemodelle, die nicht nur als geometrische Objekte repräsentiert und durch eine geeignete Textur visuell ansprechend dargestellt werden, erlauben semantische Anfragen, wie beispielsweise nach der Anzahl der Geschosse oder der Position der Balkone oder Eingänge. Die semantische Interpretation von Fassadenbildern ist ein wesentlicher Schritt für die Erzeugung solcher Modelle. In der vorliegenden Arbeit lösen wir diese Aufgabe, indem wir aus Daten abgeleitete Evidenz für das Vorkommen einzelner Objekte mit Vorwissen kombinieren, das die Analyse der gesamten Bildinterpretation steuert. Wir präsentieren dafür ein dreistufiges Verfahren: Wir erzeugen Bildmerkmale, die für die Beschreibung der relevanten Objekte geeignet sind. Wir lernen, auf Basis abgeleiteter Merkmale, eine Repräsentation dieser Objekte. Schließlich realisieren wir die Bildinterpretation basierend auf der zuvor gelernten Repräsentation und dem Vorwissen über typische Konfigurationen von Fassadenobjekten, welches wir aus Trainingsdaten ableiten. Wir leisten dazu die folgenden wissenschaftlichen Beiträge: Wir schlagen eine neuartige Me-thode zur Interpretation von Fassadenbildern vor, die einen sogenannten markierten Punktprozess verwendet. Dafür entwickeln wir ein Modell zur Beschreibung typischer Konfigurationen von Fassadenobjekten und entwickeln ein Bildinterpretationssystem, welches aus Daten abgeleitete Evidenz und a priori Wissen über typische Fassadenkonfigurationen kombiniert. Für die Erzeugung der Evidenz stellen wir Merkmale vor, die wir Shapelets nennen und die skaleninvariant und durch eine ausgesprochene Distinktivität im Bezug auf Fassadenobjekte gekennzeichnet sind. Als Basismerkmale für die Erzeugung der Shapelets dienen Linien-, Kreis- und Ellipsensegmente. Dafür stellen wir eine neuartige Methode zur Vereinfachung von Liniensegmenten vor, die eine Pixelkette durch eine Sequenz von geraden Linienstücken und elliptischen Bogensegmenten approximiert. Diese basiert unter anderem auf einer Adaption des Douglas-Peucker Algorithmus, die anstelle gerader Linienstücke, Bogensegmente als geometrische Basiselemente verwendet. Wir evaluieren jeden dieser drei Teilschritte separat. Wir zeigen Ergebnisse der Liniensegmen-tierung anhand verschiedener Bilder und weisen dabei vergleichbare und teilweise verbesserte Ergebnisse im Vergleich zu bestehende Verfahren nach. Für die vorgeschlagenen Shapelets weisen wir in der Evaluation ihre diskriminativen Eigenschaften im Bezug auf Fassadenobjekte nach. Wir erzeugen auf einem anspruchsvollen Datensatz von skalenvariablen Fassadenobjekten, mit starker Variabilität der Erscheinung innerhalb der Klassen, vielversprechende Klassifikationsergebnisse, die die Verwendbarkeit der gelernten Shapelets für die weitere Interpretation belegen. Schließlich zeigen wir Ergebnisse der Interpretation der Fassadenstruktur anhand verschiedener Datensätze. Die qualitative Evaluation demonstriert die Fähigkeit des vorgeschlagenen Lösungsansatzes zur vollständigen und präzisen Detektion der genannten Fassadenobjekte

An a-contrario approach to quasi-periodic noise removal

International audienceImages can be affected by quasi-periodic noise. This undesirable feature manifests itself by spurious repetitive patterns covering the whole image, well localized in the Fourier domain. While notch filtering permits to get rid of this phenomenon , this however requires to first detect the resulting Fourier spikes, and, in particular, to discriminate between noise spikes and spectrum patterns caused by spatially localized textures or repetitive structures. This paper proposes a statistical a-contrario detection of noise spikes in the Fourier domain. A Matlab code is also provided

BOLD Features to Detect Texture-less Objects

Object detection in images withstanding significant clut-ter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and dis-tinctive representation of groups of neighboring line seg-ments aggregated over limited spatial supports and invari-ant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlu-sion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects. 1

Deformable Linear Objects 3D Shape Estimation and Tracking From Multiple 2D Views

This letter presents DLO3DS , an approach for the 3D shapes estimation and tracking of Deformable Linear Objects (DLOs) such as cables, wires or plastic hoses, using a cheap and compact 2D vision sensor mounted on the robot end-effector. DLO3DS can be applied in all those scenarios in which the perception and manipulation of DLO-like structures are needed, such as in the case of switchgear cabling, wiring harness manufacturing and assembly in the automotive and aerospace industries, or production of hoses for medical applications. The developed procedure is based on a pipeline that first processes the images coming from the 2D camera extracting key topological points along the DLOs. These points are then used to model each DLO with a B-spline curve. Finally, the set of splines obtained from all the images is matched by exploiting a multi-view stereo-based algorithm. DLO3DS is validated both on a real scenario and on simulated data obtained by exploiting a rendering engine for photo-realistic images. In this way, reliable ground-truth data are retrieved and utilized for assessing the estimation error achievable by DLO3DS , which on the employed test set is characterized by a mean reconstruction error of 0.82 mm

Deviation magnification: Revealing departures from ideal geometries

Structures and objects are often supposed to have idealized geometries such as straight lines or circles. Although not always visible to the naked eye, in reality, these objects deviate from their idealized models. Our goal is to reveal and visualize such subtle geometric deviations, which can contain useful, surprising information about our world. Our framework, termed Deviation Magnification, takes a still image as input, fits parametric models to objects of interest, computes the geometric deviations, and renders an output image in which the departures from ideal geometries are exaggerated. We demonstrate the correctness and usefulness of our method through quantitative evaluation on a synthetic dataset and by application to challenging natural images.Shell ResearchQatar Computing Research InstituteUnited States. Office of Naval Research (Grant N00014-09-1-1051)National Science Foundation (U.S.) (Grant CGV-1111415