Abstract: Systems, methods, and computer readable media to categorize a pixel (or other element) in an image into one of a number of different categories are described. In general, techniques are disclosed for using properties (e.g., statistics) of the regions being categorized to determine the appropriate size of window around a target pixel (element) and, when necessary, the manner in which the window may be changed if the current size is inappropriate. More particularly, adaptive window size selection techniques are disclosed for use when categorizing an image's pixels into one of two categories (e.g., black or white). Statistics of the selected region may be cascaded to determine whether the current evaluation window is acceptable and, if it is not, an appropriate factor by which to change the currently selected window's size.

Abstract: Systems, methods, and computer readable media to categorize a pixel (or other element) in an image into one of a number of different categories are described. In general, techniques are disclosed for using properties (e.g., statistics) of the regions being categorized to determine the appropriate size of window around a target pixel (element) and, when necessary, the manner in which the window may be changed if the current size is inappropriate. More particularly, adaptive window size selection techniques are disclosed for use when categorizing an image's pixels into one of two categories (e.g., black or white).

Abstract: An apparatus for detecting a feature in an image includes an image input section for receiving at least part of the image in the form of image data having a plurality of pixels, the plurality of pixels comprising a plurality of non-border pixels, a feature detection module adapted to attribute a feature probability value to each of the pixels of the image data, and an extremum determination module for determining at least one local extremum among the feature probability values, wherein the extremum determination module is adapted to output, for each of the plurality of pixels, a final indication if the feature probability value of the pixel in question is a local extremum.

Abstract: An apparatus for detecting a feature in an image includes an image input section for receiving at least part of the image in the form of image data having a plurality of pixels, the plurality of pixels comprising a plurality of non-border pixels, a feature detection module adapted to attribute a feature probability value to each of the pixels of the image data, and an extremum determination module for determining at least one local extremum among the feature probability values, wherein the extremum determination module is adapted to output, for each of the plurality of pixels, a final indication if the feature probability value of the pixel in question is a local extremum.

Abstract: An apparatus for detecting a feature in an image includes an image input section for receiving at least part of the image in the form of image data having a plurality of pixels, the plurality of pixels comprising a plurality of non-border pixels, a feature detection module adapted to attribute a feature probability value to each of the pixels of the image data, and an extremum determination module for determining at least one local extremum among the feature probability values, wherein the extremum determination module is adapted to output, for each of the plurality of pixels, a final indication if the feature probability value of the pixel in question is a local extremum.

Abstract: An apparatus for detecting a feature in an image includes an image input section for receiving at least part of the image in the form of image data having a plurality of pixels, the plurality of pixels comprising a plurality of non-border pixels, a feature detection module adapted to attribute a feature probability value to each of the pixels of the image data, and an extremum determination module for determining at least one local extremum among the feature probability values, wherein the extremum determination module is adapted to output, for each of the plurality of pixels, a final indication if the feature probability value of the pixel in question is a local extremum.