This paper presents a software architecture for the network-transparent control of distributed robotic systems. The system consists of two main components: a generic and easily extensible CORBA-based interface to distributed services, and a high-level XML-based description language for specifying the behavior of the robots. The architecture makes it possible to create dynamically modifiable, extensible control software with ease. It is successfully utilized in implementing a coffee serving system in which the co-operation of two very different robots and two other distributed services are needed.

Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3/spl times/3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture description which can achieve better classification rates in comparison with the whole LBP histogram. Experiments on classification of textures from the Columbia-Utrecht (CURET) database demonstrate the robustness of the approach.

This paper benchmarks the simple local binary pattern (LBP) approach in the supervised texture segmentation problems of the recent comparative study of Randen and Husoy (1999). A multi-predicate version of LBP is proposed, making our approach even more powerful for images containing textures at multiple scales.