Abstract: A method and apparatus are provided to process spatial data including seismic data. Given a spatial dataset of n dimensions, a data-driven partitioning of the dataset is effected into topographically contiguous regions using feature specific indices. Then on each region, a set of features is calculated according to a computer process, wherein the features are considered sufficiently descriptive of the region. Thereupon, two or more regions which are topographically contiguous are grouped together and the associated features are assembled in an analytical structure to be input to a classifier.

Abstract: In an analysis of a set of discrete multidimensional data which can be represented in an array with a topology, where the array that can be mapped to an image space of discrete elements, such as digitized image data, seismic data and audio data, genotype/phenotype classifications are imposed on the topology, and then molecular biological-like processes (annealing, fragmentation, chromatographic separation, fingerprinting, footprinting and filtering) are imposed upon that topology to perceive classifiable regions such as edges. More specifically, an image feature probe constructed of strings of contiguous image fragments of the class of N-grams called linear N-grams, anneals genotypes of topological features by complementary biological-like techniques in the same manner that complex biological systems are analyzed by genetic mapping, sequencing and cloning techniques. For example, molecular biological probes anneal with molecular biological genotypes and then are used to classify those genotypes.

Abstract: Given a spatial dataset of n dimensions, a data-driven partitioning of the dataset is effected into topographically contiguous regions using feature specific indices (for example, by examining the change in polarity of the impedance of seismic data). Then on each region, a set of features (which may be mathematical functions) is calculated (e.g., mean value of all data in the region), wherein the features are considered sufficiently descriptive of the region. Thereupon, two or more regions which are topographically contiguous are grouped together and the associated features are assembled in a structure (e.g., a vector or a matrix) to be input to a classifier.

Abstract: Given a spatial dataset of n dimensions, a data-driven partitioning of the dataset is effected into topographically contiguous regions using feature specific indices (for example, by examining the change in polarity of the impedance of seismic data). Then on each region, a set of features (which may be mathematical functions) is calculated (e.g., mean value of all data in the region), wherein the features are considered sufficiently descriptive of the region. Thereupon, two or more regions which are topographically contiguous are grouped together and the associated features are assembled in a structure (e.g., a vector or a matrix) to be input to a classifier.

Abstract: Methods are provided for carrying out a selection step and a blending step of a process of photorealistically reconstructing and manipulating a portion of a photograph or digital image, such as a segment or a layer, in order to minimize transition discontinuities and to provide optimum speed in the segmenting of the image, which is a function of increasing importance in a production environment. In the selection step, an image is classified into foreground and background states and regions, which could be limited to a selected layer of a final image. A vector is constructed which maps separation distances in a specialty space such as HSV space between the background region in a background state and the foreground region in a new foreground state. Difference vectors between the old and new foreground states are used to facilitate the mapping via a transition path which occupies the portion of specialty space where all the edge pixels between the foreground and the background parameters are located.

Abstract: A color palette mapped to an image obtained by any known process is decomposed to separate contrast components from hue/saturation pairs, then only the hue/saturation pairs are remapped to a new set of hue/saturation parameters according to available mapping techniques, including remapping of ordered color sets, into a new ordered set wherein at least a portion of the contrast components are held at fixed values and not altered. By fixing the contrast components, color remapping is constrained to certain symmetric transformations in Munsell color space which preserves features of the source image in the resultant image.

Abstract: Colors for a two-dimensional ordered image such as a fractal map are selected by first sampling colors from a real or natural source of colors, such as a tree or a feather and storing values representing the colors in Munsell color space (the three-dimensional space defined by hue, saturation and value) in a source color file, then ordering colors by distance in Munsell color space from a seed reference color (e.g. black), thereafter storing a representation of the color whose distance is closest to the seed reference color as the first color in a storage file known as an ordered color file, using the first color as a new reference color, then sorting the remaining colors according to distance from the new reference color, and storing a representation of the color closest to the new reference color as the second color in the ordered color file. The process is recursively performed until all colors in the source color file have been ordered in the ordered color file.