Martin Drauschke

Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures*

Currently available morphometric and genetic techniques that can accurately identify Africanized honey bees are both costly and time consuming. We tested two new morphometric techniques (ABIS — Automatic Bee Identification System and geometric morphometrics analysis) on samples consisting of digital images of five worker forewings per colony. These were collected from 394 colonies of Africanized bees from all over Brazil and from colonies of African bees, Apis mellifera scutellata (n = 14), and European bees, A. m. ligustica (n = 10), A. m. mellifera (n = 15), and A. m. carnica (n=15) from the Ruttner collection in Oberursel, Germany (preserved specimens). Both methods required less than five minutes per sample, giving more than 99% correct identifications. There was just one misidentification (based on geometric morphometrics analysis) of Africanized bees compared with European subspecies, which would be the principal concern in newly-colonized areas, such as the southern USA. These new techniques are inexpensive, fast and precise.ZusammenfassungDie Afrikanisierten Honigbienen sind unter den verschiedenen Unterarten und Rassengruppen der Honigbiene (Apis mellifera L.) in den Neotropen und den Nachbarregionen am meisten respektiert und gefürchtet, insbesondere da sie in neue Gebiete einwandern. Die Identifizierung der Afrikanisierten Bienen ist in diesen Regionen für die Bewirtschaftung der Bienenvölker daher unverzichtbar. Sie ermöglicht die Bestimmung ihres Verbreitungsgebiets und ihrer Ausbreitungsgeschwindigkeit, dies ist sowohl für die Imker als auch für die damit befassten Regierungseinrichtungen von Bedeutung.Wir benutzten zwei kürzlich entwickelte morphometrische Techniken (ABIS — Automatic Bee Identification System und die Geometrische Morphometrische Analyse), um Proben aus jeweils fünf rechten Vörderflügeln pro Volk zu analysieren (Tab. I). Beide dieser Methoden benötigten in einem Vergleich von 394 über ganz Brasilien verteilten Völkern weniger als 5 Minuten pro Volk und erreichten eine mehr als 99% korrekte Identifizierung. Diese ergaben 14 Völker von A. m. scutellata, 10 Völker von A. m. ligustica, 15 Völker von A. m. mellifera und 15 Völker von A. m. carnica (Tab. II und III). Mit ABIS können einzelne Bienen bestimmt werden, während die Geometrische Morphometrische Analyse eine auf jeweils 5 Flügeln beruhende Identifikationen auf Kolonieebene durchführt. Die meisten der Fehleinordnungen fanden zwischen Afrikanisierten und Afrikanischen Bienen sowie zwischen den europäischen Unterarten statt. Nur eines der Afrikanisierten Bienenvölker wurde irrtümlich als eine europäische Unterart eingeordnet, dies ist die Fehlerart die insbesondere innerhalb von neubesiedelten Gebieten wie den Südstaaten der USA von Bedeutung wäre. Die erreichten Fortschritte in Computertechnologie, statistischen Analysen und Bilderkennungssoftware sowie die verbesserten Informationen über die relevanten Messgrößenbereiche und die höhere Genauigkeit und größere Geschwindigkeit der Messungen selbst machen es nun möglich, Afrikanisierte Bienen ausschließlich anhand von Digitalaufnahmen der Vörderflügel in Minutenschnelle zu identifizieren.

Detection of repeated structures in facade images

We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used; thus, the method also appears to be able to identify other manmade structures, e.g., paths with regular tiles. A method for detection of dominant symmetries is adapted for detection of multiply repeated structures. A compact description of the repetitions is derived from the detected translations in the image by a heuristic search method and the criterion of the minimum description length.

An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts

We present an irregular image pyramid which is derived from multi-scale analysis of segmented watershed regions. Our framework is based on the development of regions in the Gaussian scale-space, which is represented by a region hierarchy graph. Using this structure, we are able to determine geometrically precise borders of our segmented regions using a region focusing. In order to handle the complexity, we select only stable regions and regions resulting from a merging event, which enables us to keep the hierarchical structure of the regions. Using this framework, we are able to detect objects of various scales in an image. Finally, the hierarchical structure is used for describing these detected regions as aggregations of their parts. We investigate the usefulness of the regions for interpreting images showing building facades with parts like windows, balconies or entrances.

Cornice Detection Using Façade Image and Point Cloud

puter vision. The Internet of things is another application for high resolution interpreted 3D scenes. According to LEBERL et al. (2012), information down to 3 cm resolution is needed for virtual navigation through urban spaces. Therefore, also the recognition of small details on building façades will be required for realistic models of the environment. LEBERL et al. (2012) derive large and highly detailed 3D models of urban spaces from various data sources. Aerial images are used to generate coarse building and landscape models, whereas laser scans and terrestrial images from mobile sensing platforms are employed for the determination of models with more detail. If these results can be merged with future results of automated approaches for the interpretation of façades such as HOHMANN et al. (2009), the combination will make automatic

A Bayesian approach for scene interpretation with integrated hierarchical structure

We propose a concept for scene interpretation with integrated hierarchical structure. This hierarchical structure is used to detect mereological relations between complex objects as buildings and their parts, e. g., windows. We start with segmenting regions at many scales, arranging them in a hierarchy, and classifying them by a common classifier. Then, we use the hierarchy graph of regions to construct a conditional Bayesian network, where the probabilities of class occurrences in the hierarchy are used to improve the classification results of the segmented regions in various scales. The interpreted regions can be used to derive a consistent scene representation, and they can be used as object detectors as well. We show that our framework is able to learn models for several objects, such that we can reliably detect instances of them in other images.