Feature recognition using loose gray scale template matching

Details Feature recognition using loose gray scale template matching Feature recognition using loose gray scale template matching

2000-12-26

2004-10-19

Mariam, Daniel (Department: 2623)

Image analysis

Pattern recognition

Template matching

C382S217000

Reexamination Certificate

active

06807304

ABSTRACT:

FIELD OF INVENTION
This invention relates to systems and methods for processing images using filters. More specifically, this invention relates to systems and methods for designing and implementing image processing filters using templates wherein the filters operate on gray-scale images and the templates identify gray-scale image features for the purposes of modification or for extracting some image statistic, and for the purposes of optimization for the human visual system, or compatibility with other system modules, such as, compression algorithms, recognition algorithms and those occurring in printing and display devices.
CROSS REFERENCES
The following patents and publications are hereby incorporated by reference for their teachings:
“Method for design and implementations of an image resolution enhancement system that employs statistically generated lookup tables,” Loce et al., U.S. Pat. No. 5,696,845;
“Method and apparatus for the resolution enhancement of grayscale images that include text and line art”, Lin et al., U.S. Pat. No. 5,742,703.
Barski, L., and R. Gaborski, “Image Character Enhancement using a Stroke Strengthening Kernel,” U.S. Pat. No. 4,791,679, Dec. 13, 1988.
Bassetti, L. W., “Fine Line Enhancement,” U.S. Pat. No. 4,544,264, Oct. 1, 1985.
Bunce, R., “Pixel Image Enhancement Employing a Reduced Template Memory Store,” U.S. Pat. No. 5,237,646, Aug. 17, 1993.
Carely, A. L., “Resolution Enhancement in Laser Printers,” Copyright 1993, XLI Corp., Woburn, Mass.
Crow, F. C., “The Use of Gray-scale for Improved Raster Display of Vectors and Characters,”
Computer Graphics
, Vol. 12, August, 1978.
Curry, D. N., “Hyperacuity Laser Imager,”
Journal of Electronic Imaging
, Vol. 2, No. 2, pp 138-146, April 1993.
Curry, D. N., R. St. John, and S. Filshtinsky, “Enhanced Fidelity Reproduction of Images by Hierarchical Template Matching,” U.S. Pat. No. 5,329,599, Jul. 12, 1994.
Denber, M., “Image Quality Improvement by Hierarchical Pattern Matching with Variable Size Templates,” U.S. Pat. No. 5,365,251, Nov. 15, 1994.
Frazier, A. L., and J. S. Pierson, “Resolution Transforming Raster-Based Imaging System,” U.S. Pat. No. 5,134,495, Jul. 28, 1992.
Frazier, A. L., and J. S. Pierson, “Interleaving Vertical Pixels in Raster-Based Laser Printers,” U.S. Pat. No. 5,193,008, Mar. 9, 1993.
Handley, J., and E. R. Dougherty, “Model-Based Optimal Restoration of Fax Images in the Context of Mathematical Morphology,
Journal of Electronic Imaging
, Vol. 3, No. 2, April 1994.
Kang, H., and R. Coward, “Area Mapping Approach for Resolution Conversion,”
Xerox Disclosure Journal
, Vol. 19, No. 2, March/April, 1994.
Loce, R. and E. Dougherty,
Enhancement and Restoration of Digital Documents
, SPIE Press, Bellingham Wash., 1997. Chapters 1-4.
Loce, R. P., M. S. Cianciosi, and R. V. Klassen, “Non-integer Image Resolution Conversion Using Statistically Generated Look-Up Tables,” U.S. Pat. No. 5,387,985, Feb. 7, 1995.
Mailloux, L. D., and R. E. Coward, “Bit-Map Image Resolution Converter,” U.S. Pat. No. 5,282,057, Jan. 25, 1994.
Miller, S., “Method and Apparatus for Mapping Printer Resolution Using Look-up Tables,” U.S. Pat. No. 5,265,176, Nov. 23, 1993.
Petrick, B., and P. Wingfield, “Method and Apparatus for Input Picture Enhancement by Removal of Undesired Dots and Voids.” U.S. Pat. No. 4,646,355, Feb. 24, 1987.
Sanders, J. R., W. Hanson, M. Burke, R. S. Foresman, J. P. Fleming, “Behind Hewlett-Packard's Patent on Resolution Enhancement® Technology,” B. Colgan, ed., Prepared by Torrey Pines Research Carlsbad Calif., Distributed by BIS CAP International, Newtonville Mass., 1990. [Dicusses early template matching patents—Tung, Walsh, Basseti, . . . ]
Tung, C., “Piece-Wise Print Image Enhancement for Dot Matrix Printers,” U.S. Pat. No. 4,847,641, Jul. 11, 1989, U.S. Pat. No. 5,005,139, Apr. 2, 1991.
Tung, C., “Resolution Enhancement in Laser Printers,” in Proc. SPIE 1912
, Color Hard Copy and Graphics Arts II
, Jan. 31, 1993, San Jose, Calif.
Walsh, B., F., and D. E. Halpert, “Low Resolution Raster Images,” U.S. Pat. No. 4,437,122, Mar. 13, 1984.
DESCRIPTION OF RELATED ART
A wide variety of digital document processing tasks are performed using template-based filters. Illustratively, digital document processing tasks include resolution conversion, enhancement, restoration, appearance tuning and de-screening of images. These tasks are commonly performed on monochrome and color images, as well as binary and continuous tone images. Although, due to the binary nature of conventional templates, implementing many digital document-processing tasks on continuous tone images has been problematic prior to the present invention. A continuous tone image may also be referred to as a gray-scale image.
In conventional systems and methods, a typical filter includes template operators to perform filtering of the images, where, a filter may be characterized as an operator or device that transforms one image into another image or transforms an image to a collection of information, such as image statistics. The filter is formed of a number of imaging template operators, often simply referred to as templates. These templates may be, for example, stored in a look-up table and implemented using a look-up table formalism. Or other equivalent formalisms, such as Boolean logic may be employed. The number of templates in a filter may vary between a small number of templates to thousands of templates. Due to its versatility in design, a look-up table is typically used to implement a template-based filter.
A raster is a one-dimensional array of image data, reflecting a single line of data across a single dimension, i.e., the length or the width, of the image. Further, each location, or “picture element,” in an image may be called a “pixel.” In an array defining an image in which each item of data provides a value, each value indicating the properties of a location may be called a pixel value. Each pixel value is a bit in a binary form of an image, a gray-scale value in a gray-scale form of an image, or a set of color-spaced coordinates in a color coordinate form of an image. The binary form, gray-scale form, and color coordinate form are each arranged typically in a two-dimensional array, which defines an image. An N-dimensional array is typically used for an N-dimensional images, where for example, N=3 for 3-dimensional topographic images.
Using the typical binary image processing setting as an example, the filter, using the templates, transforms certain observed pixel patterns in a binary image, for example, into a corresponding enhanced binary pixel pattern. Specifically, the filter observes an arrangement of pixels using a suitable window or mask. A window is an imaging algorithmic device that observes a plurality of pixels at the same time, where the plurality of pixels is located about a target pixel. The values and locations of the observed pixels are inputted into the template matching operations. After observing the arrangement of pixels, about a target pixel, the filter then attempts to match the observed pixel pattern with one or more of the templates in the look-up table. If the look-up table contains a match to the observed pixel pattern, the look-up table generates an appropriate output. The output may be an enhanced pixel pattern for the target pixel that corresponds to the observed pixel pattern. The output could also be information in other forms; for example, the output could be a code denoting the match condition, or a data to be used for a statistical characterization of image regions.
A wide variety of types and sizes of observation windows or masks are known. The particular window used in a particular application depends on the image to be analyzed and the particular process to be performed on the image. Illustratively, a 3×3 window may be used to process an image. The 3×3 window, at various locations in the image, observes a 3×3 block, i.e., a 9-pixel block, of binary-valued pixels, for example. One pixel in the window is the target pixel, which is typically the

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