Fast tracking of natural textures using fractal snakes

The natural environments that robotic applications often encounter can present difficult problems for image-based task execution. Prior efforts have used both grayscale and color as statistical appearance descriptors in these applications. In the case of natural environments, the statistical measures of luminosity and chromaticity are often ineffective due to relatively constant shades and colors of soil, flora, and fauna. Texture can provide an alternative/additional appearance descriptor in many of these environments; however the common approaches to textural segmentation are computationally intensive and cannot be used for real-time robotic visual servoing. We present a technique for textural segmentation and tracking that can discriminate between natural textures that are otherwise similar in color and brightness. The technique builds upon earlier work in fractal imaging and in statistical deformable models (a.k.a. snakes) to provide a simple and efficient method for extracting target shape and location from an initial textural example. We then give results from the application of this technique on standard texture test patterns. We then demonstrate the effectiveness of the method on natural imagery. Finally, we show how the technique can be applied to challenging robotic applications.