The multiple window parameter transform

The multiwindow transform, an extension of parameter transform techniques that increase performance and scope by exploiting the long-range correlated information contained in multiple portions of an image, is presented. Multiple-window transforms allow the extraction of high-dimensional features with improvement in accuracy over conventional techniques while keeping linear to low-order-polynomial computational and space requirements with respect to image size and dimensionality of the features. Using correlated information provides a direct link between extracted features and supporting regions in the image. This, coupled with evidence integration techniques, is used to suppress noisy or nonexistent feature hypotheses. Parameter spaces are implemented as constraint satisfaction networks, where feature hypotheses with overlapping support in the image compete. After an iterative relaxation phase, surviving hypotheses have disjoint support, forming a segmentation of the image. Examples show the performance and provide insight about the behavior