A Framework for Symmetric Part Detection in Cluttered Scenes

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.Comment: 10 pages, 8 figure

A new method to analyse mosaics based on Symmetry Group theory applied to Islamic Geometric Patterns

[EN] This article presents a new method for analysing mosaics based on the mathematical principles of Symmetry Groups. This method has been developed to get the understanding present in patterns by extracting the objects that form them, their lattice, and the Wallpaper Group. The main novelty of this method resides in the creation of a higher level of knowledge based on objects, which makes it possible to classify the objects, to extract their main features (Point Group, principal axes, etc.), and the relationships between them. In order to validate the method, several tests were carried out on a set of Islamic Geometric Patterns from different sources, for which the Wallpaper Group has been successfully obtained in 85% of the cases. This method can be applied to any kind of pattern that presents a Wallpaper Group. Possible applications of this computational method include pattern classification, cataloguing of ceramic coatings, creating databases of decorative patterns, creating pattern designs, pattern comparison between different cultures, tile cataloguing, and so on.The authors wish to thank the Patronato de la Alhambra y Generalife (Granada, Spain) and the Patronato del Real Alcázar de Sevilla (Seville, Spain) for their valuable collaboration in this research work.Albert Gil, FE.; Gomis Martí, JM.; Blasco, J.; Valiente González, JM.; Aleixos Borrás, MN. (2015). A new method to analyse mosaics based on Symmetry Group theory applied to Islamic Geometric Patterns. Computer Vision and Image Understanding. 130:54-70. doi:10.1016/j.cviu.2014.09.002S547013

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment

P algorithm, a dramatic enhancement of the waterfall transformation

This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

Lane and Road Marking Detection with a High Resolution Automotive Radar for Automated Driving

Die Automobilindustrie erlebt gerade einen beispiellosen Wandel, und die Fahrerassistenz und das automatisierte Fahren spielen dabei eine entscheidende Rolle. Automatisiertes Fahren System umfasst haupts\"achlich drei Schritte: Wahrnehmung und Modellierung der Umgebung, Fahrtrichtungsplanung, und Fahrzeugsteuerung. Mit einer guten Wahrnehmung und Modellierung der Umgebung kann ein Fahrzeug Funktionen wie intelligenter Tempomat, Notbremsassistent, Spurwechselassistent, usw. erfolgreich durchf\"uhren. F\"ur Fahrfunktionen, die die Fahrpuren erkennen m\"ussen, werden gegenw\"artig ausnahmslos Kamerasensoren eingesetzt. Bei wechselnden Lichtverh\"altnissen, unzureichender Beleuchtung oder bei Sichtbehinderungen z.B. durch Nebel k\"onnen Videokameras aber empfindlich gest\"ort werden. Um diese Nachteile auszugleichen, wird in dieser Doktorarbeit eine \glqq Radar\textendash taugliche\grqq{} Fahrbahnmakierungerkennung entwickelt, mit der das Fahrzeug die Fahrspuren bei allen Lichtverh\"altnissen erkennen kann. Dazu k\"onnen bereits im Fahrzeug verbaute Radare eingesetzt werden. Die heutigen Fahrbahnmarkierungen k\"onnen mit Kamerasensoren sehr gut erfasst werden. Wegen unzureichender R\"uckstreueigenschaften der existierenden Fahrbahnmarkierungen f\"ur Radarwellen werden diese vom Radar nicht erkannt. Um dies zu bewerkstelligen, werden in dieser Arbeit die R\"uckstreueigenschaften von verschiedenen Reflektortypen, sowohl durch Simulationen als auch mit praktischen Messungen, untersucht und ein Reflektortyp vorgeschlagen, der zur Verarbeitung in heutige Fahrbahnmakierungen oder sogar f\"ur direkten Verbau in der Fahrbahn geeignet ist. Ein weiterer Schwerpunkt dieser Doktorarbeit ist der Einsatz von K\"unstliche Intelligenz (KI), um die Fahrspuren auch mit Radar zu detektieren und zu klassifizieren. Die aufgenommenen Radardaten werden mittels semantischer Segmentierung analysiert und Fahrspurverl\"aufe sowie Freifl\"achenerkennung detektiert. Gleichzeitig wird das Potential von KI\textendash tauglichen Umgebungverstehen mit bildgebenden Radardaten aufgezeigt