Abstract:
A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R_L, R_U, and R_V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a nullsoftnull image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The nullsoftnull classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.
Abstract:
A system and method for processing image data converts a pixel of image data having a first resolution to a plurality of subpixels, the plurality of subpixels representing a second resolution, the second resolution being higher than the first resolution. The plurality of subpixels are thresholded to generate a group of subpixel values for each pixel and a threshold error value. It is then determined if the group of subpixel values from the thresholding process produce a pattern containing an isolated subpixel. If the group of subpixel values from the thresholding process produce a pattern containing an isolated subpixel, the group of subpixel vales is modified to produce a pattern without an isolated subpixel. The modification process produces a subpixel error value which is compensated for localized error before being diffused to adjacent pixels.
Abstract:
A method of determining image skew in a scanned document includes scanning the image and determining on a pixel-by-pixel basis whether or not pixels are ON or OFF on scan lines and columns along both the fast and slow scan directions for a particular document rotation angle. Through one read of the image, image data is sampled simultaneously at a plurality of predetermined document rotation angles From the sampled data, the second order moment of the number of ON pixels is calculated as a function of document rotation angle for scan lines along both the fast and slow scan directions, yielding two independent skew angle estimates. Skew angle estimates corresponding to valid second order moment data sets are compared and combined to provide a resultant skew angle estimate for the document.
Abstract:
An annular window-shaped structuring element is provided for image processing to remove speckles from a scanned image. The window-shaped structuring element is composed of two differently sized squares sharing the same geometric center-point. The pixel to be analyzed with the structuring element is at the center-point. The structuring element is used in a method to remove speckles from binary, grayscale, and/or color images by first eroding the image, detecting speckles relative to other pixels in the image, and removing declared speckles. The method may additionally include a halftoning module to protect halftone images.