Objektklassifizerung anhand der Modalität Textur

Service Roboter müssen eine Vielzahl an sich ständig ändernden Objekten erkennen. Um mit der Menge an Objekten umgehen zu können, muss es möglich sein, Objekte schnell und einfach zu beschreiben. Eine wichtige Eigenschaft zur Identifizierung der Objekte ist deren Textur. In dieser Arbeit wird daher ein System entwickelt, dass eine Klassifizierung von Objekten anhand von Textur- und Farbeigenschaften vornehmen kann, die dem menschlichen Empfinden entsprechen. Dazu wird ein Farbbild in homogene Segmente unterteilt und diese in eine normalisierte Ansicht transformiert. Dadurch kann eine einheitliche Auswertung vorgenommen werden. Auf den Bildausschnitten werden die Farb- und Textureigenschaften ausgewertet und anhand derer eine Klassifizierung durchgeführt. Es wird untersucht, wie gut die Eigenschaften dem menschlichen Empfinden entsprechen und mit anderen Ansätzen aus diesem Themengebiet verglichen. Es wird außerdem eine Methode implementiert die Daten zum Trainieren eines Klassifizierers aus der Beschreibung eines Menschen erzeugt.Service robots have to categorize a big amount of varying objects. Therefore the texture of an object is an important feature. This paper introduces a system, which categorizes objects by means of human-readable color and texture features. The first part of the system is an image segmentation based on depth, color and texture information. The resulting segments are transformed into a normalized view for reliable computation of the human-readable color and texture features which are used by machine learning approach for categorization. The color and texture features are analyzed according to their ability to represent the human perception. They are also compared to other approaches of human readable features. A procedure to train a classifier based on a human description of texture is presented

Automatic Classification of Seafloor Image Data by Geospatial Texture Descriptors

A novel approach for automatic context-sensitive classification of spatially distributed image data is introduced. The proposed method targets applications of seafloor habitat mapping but is generally not limited to this domain or use case. Spatial context information is incorporated in a two-stage classification process, where in the second step a new descriptor for patterns of feature class occurrence according to a generically defined classification scheme is applied. The method is based on supervised machine learning, where numerous state-of-the-art approaches are applicable. The descriptor computation originates from texture analysis in digital image processing. Patterns of feature class occurrence are perceived as a texture-like phenomenon and the descriptors are therefore denoted by Geospatial Texture Descriptors. The proposed method was extensively validated based on a set of more than 4000 georeferenced video mosaics acquired at the Haakon Mosby Mud Volcano north-west of Norway recorded during cruise ARK XIX3b of the German research vessel Polarstern. The underlying classification scheme was derived from a scheme developed for manual annotation of the same dataset applied in the course of Jerosch [2006]. Features of interest are related to methane discharge at mud volcanoes, which are considered a significant source of methane emission. In the experimental evaluation, based on the prepared training and test data, a major improvement of the classification precision compared to local classification as well as classification based on the raw data from the local spatial context was achieved by the application of the proposed method. The classification precision was particularly improved for rarely occurring classes. In a further comparison with annotated data available from Jerosch [2006] the regional setting of the investigation area obtained by the application of the proposed method was found almost equivalent to the results of an experienced scientist