Cloudiness Images Multilevel Segmentation by Piecewise Linear Approximation of Cumulative Histogram

Abstract

The Ramer-Douglas-Peucker algorithm for piecewise approximation is used for image multilevel segmentation. The cumulative histogram is selected as a function for approximation. The algorithm allows you to determine threshold values of continuous and discrete images. The algorithm is used to separate cloudiness from background and to calculate cloudiness intensity. The found points of the approximated function have been accepted to change pixel intensity by proposed formulas. The algorithm efficiency is compared with those based on ordinary and cumulative histograms. By controlling the number of points for piecewise linear approximation function, the necessary segmentation accuracy can be achieved. The algorithm complexity is linear to the number of image pixels and to the number of intensity steps. The developed algorithm is applied to the satellite map images to separate clouds of different intensity. The extracted clouds of different intensity are used to classify regions by cloudiness with a developed clustering algorithm. Testing and experimental results are presented.

Chains Defects Detection in a Printed Circuit Board Image by the Plane Partition and Flood-filling of Traces

“…To minimize the number of intersections of lines and PCB elements the partition procedure is considered. The procedure is based on the method of multilevel segmentation [11] of a special cumulative histogram. To illustrate the main idea of the ordinary and the cumulative special histograms they are calculated.…”

“…To visualize defects a distributed cumulative histogram (DCH) [11] is more productive. It is possible to notice changes in the difference between pixels on the etalon and real samples.…”

“…It is possible to notice changes in the difference between pixels on the etalon and real samples. To increase the difference of the intensity function and visualize data reflecting defects, the following approach from the work [11] is used. In Fig.…”