x1 = area x2 = perimeter ... xd = arc_length / straight_line_distance
A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512x512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.
This work addresses position estimation of a micro-rover mobile robot as a larger robot tracks it through large spaces with unstructured lighting. We use the Spherical Coordinate Transform color segmenter commonly used in medical applications. Data was collected from 50 images taken in five types of lighting: fluorescent, tungsten, daylight lamp, natural daylight indoors and outdoors. The results show that average pixel error was 1.5, with an average error in distance estimation of 6.3 cm. The size of the error did not vary greatly with the type of lighting. In addition to giving segmentation results comparable to stereo triangulation, our approach has other advantages including low computational complexity O(n^2) and lightweight, inexpensive hardware.
Tungsten Lighting
Daylight lamp (halogen with blue filter)
Indoor sunlight
Outdoor sunlight