Abstract: Digital image data is manipulated for improved image visualization and analysis. A two-dimensional image includes relevant subject matter and a substantially homogeneous background that at least partially borders on the relevant subject matter. A first sub-image is defined from the two-dimensional image that includes the relevant subject matter and a reduced amount of the substantially homogeneous background. A second sub-image is defined from the first sub-image that substantially defines just the relevant subject matter. A plurality of image processing options are selected from a library of image processing options. The second sub-image is then processed in accordance with the image processing options to generate image output data. The image output data can be displayed for viewing and/or analysis.

Abstract: An image processing method improves contrast of relevant subject matter surrounded or partially bordered by a substantially homogeneous background. A first sub-image is defined that includes the relevant subject matter and a reduced amount of the substantially homogeneous background. A second sub-image is defined from the first sub-image that is substantially just the relevant subject matter. If any of the homogeneous background remains, a smoothing routine can be applied to the edges of the second sub-image. The second sub-image can then be re-scaled to a given dynamic range for improved contrast of the relevant subject matter.

Abstract: A method is presented for identifying clutter in an image such as a sonar age. A detection scheme identifies portions of the image having a signal-to-noise ratio greater than a given threshold. A classification scheme is then applied to each such portion to generate fractal feature values associated therewith in order to classify the portion as a target or non-target. For clutter identification, each portion is assigned to a group based on a distance from the position of each portion to another of the portions that is its nearest neighbor. A series of tests are then performed for each group using the fractal feature values associated with each portion in each group. A failure of any of the series of tests by a group identifies each portion associated with that group as clutter in the image.

Abstract: A method of identifying and classifying pre-detected target candidates in an image using pixel intensity and a fractalization process applied to the image. A raw analog image is digitized and normalized. The normalized pixel intensity content of the image is converted to fractal dimensions using a small and a large fractal box, sequentially. An array of special fractal features satisfying predetermined classification thresholds is prepared from the fractal dimensions for each box centered about each pre-detected target candidate in the image, thus classifying the detected objects as targets.