Data processing: artificial intelligence – Knowledge processing system – Knowledge representation and reasoning technique
Reexamination Certificate
1999-09-10
2003-12-02
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
Knowledge representation and reasoning technique
C706S900000, C706S014000
Reexamination Certificate
active
06658399
ABSTRACT:
BACKGROUND
1. Field
This disclosure is related to image processing, and, more particularly, to image segmentation.
2. Background Information
As is well-known, image segmentation, in particular, image segmentation of a digital image, has a variety of applications. For example, such approaches may be employed in target tracking and acquisition, navigation systems, recognition systems, video conferencing, robotic vision, etc. These are just a few examples of the types of applications in which image segmentation may be employed. For example, an image may be segmented to be stored more efficiently or to be transmitted across a communications system having scalable bandwidth capabilities and so forth. Nonetheless, image segmentation faces a number of challenges.
One such challenge is segmenting the image where the image content may be blurred, rather than sharp. It is generally easier to segment a sharp image than a blurred image. Another challenge in segmenting an image arises where a histogram of signal values is created based upon the content of the image and that histogram contains discontinuities. Known techniques for segmenting an image do not perform well, typically, in such circumstances. A need, therefore, exists for a method or technique of segmenting an image that addresses these foregoing challenges.
SUMMARY
Briefly, in accordance with one embodiment of the invention, a method of segmenting an image includes the following. At least one signal value level is determined, of the potential signal values of the image, along which to divide a fuzzy histogram into at least two measurement distributions, the histogram being based, at least in part, on the image. The at least one signal value level is the at least one of the potential signal value levels of the image that produces a divided fuzzy histogram having, based on calculating entropy, an extreme value of one of distinctiveness and fuzziness. The image is segmented using the at least one signal value level.
Briefly, in accordance with another embodiment of the invention, a method of As segmenting an image includes the following. A fuzzy histogram is constructed based, at least in part, on the signal values levels of the image. The fuzzy histogram is divided into at least two measurement distributions along at least one of the potential signal value levels of the image to produce a divided fuzzy histogram. One of the distinctiveness and the fuzziness for the divided fuzzy histogram is computed using entropy. The prior operations are repeated for every potential signal value level of the image. The at least one signal value level of the potential signal value levels that provides the divided fuzzy histogram with an extreme value of one of the distinctiveness and the fuzziness is determined. The image is segmented using the at least one signal value level.
Briefly, in accordance with yet another embodiment of the invention, an article includes: a storage medium having stored thereon instructions that, when executed by a computing platform, result in the following operations by the computing platform to segment an image. At least one signal value level of the potential signal value levels of the image along which to divide a fuzzy histogram is determined, the fuzzy histogram being based, at least in part, on the image, the at least one signal value level being the at least one of the potential signal value levels that produces a divided fuzzy histogram having an extreme based on calculating entropy. The image is segmented image using the at least one signal value level.
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Acharya Tinku
Rao A. K. V. Subba
Ray Ajay K.
Greenwald Brad A.
Hirl Joseph P.
Intel Corporation
Starks, Jr. Wilbert L.
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