Unformatted text preview: noise image.
A more general approach takes gray level occurrences inside a local neighborhood into
consideration when constructing a gray-level histogram, the goal being to build a histogram with
a better peak to valley ratio.
Weight histogram contributions to suppress the influence of pixels with a high image
gradient. This means that a histogram will consist mostly of the gray values of objects
and background, and that border will not contribute . Faculty of Engineering Robotics Technology MECH 4041 8
B.Eng (Hons.) Mechatronics S. Venkannah Mechanical and Production Engineering Department Another method uses only high gradient pixels to form the gray level histogram, meaning
that the histogram will consist mostly of border gray-levels and should be uni-modal in
which the peak corresponds to the gray-level of borders between objects and background.
Based on approximation of the histogram of an image using a weighted sum of two or more
probability densities with normal distribution. The threshold is set as the closest gray level
corresponding to the minimum probability between the maxima of two or more normal
distributions, which results in minimum error segmentation. The difficulty with these
methods is in estimating normal distribution parameters together with the uncertainty that the
distribution may be considered normal. These difficulties may be overcome if an optimal
threshold is sought that maximizes gray-level variance between objects and background.
The method performs well under a large variety of image contrast conditions.
Many practical segmentation problems need more information than is contained in one
spectral band. Color images are a natural example, in which information is coded in three
spectral bands, e.g., red, green and blue; multi-spectral remote sensing images or
meteorological satellite images may have even more spectral bands. One segmentation
approach determines thresholds independently in each spectral band and combines them into
a single segmented image.
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