[PDF][PDF] Parallel Approach of Adaptive Image Thresholding Algorithm on GPU.
Segmentation is a process that partitions image into segments [1]. Segmentation is useful for
changing image representation into something more meaningful and easier to analyze, eg,
finding objects and boundaries. One of the methods to perform image segmentation is
image thresholding. The method partitions image into background and foreground using a
given threshold. This process is also called binarization because the segmentation result is
a binary image that maps “0” pixel as background and “1” pixel as foreground. In order to …
changing image representation into something more meaningful and easier to analyze, eg,
finding objects and boundaries. One of the methods to perform image segmentation is
image thresholding. The method partitions image into background and foreground using a
given threshold. This process is also called binarization because the segmentation result is
a binary image that maps “0” pixel as background and “1” pixel as foreground. In order to …
Segmentation is a process that partitions image into segments [1]. Segmentation is useful for changing image representation into something more meaningful and easier to analyze, eg, finding objects and boundaries. One of the methods to perform image segmentation is image thresholding. The method partitions image into background and foreground using a given threshold. This process is also called binarization because the segmentation result is a binary image that maps “0” pixel as background and “1” pixel as foreground.
In order to perform image thresholding, the threshold value can be determined manually by observation or experiment. However, in the adaptive image thresholding method, the threshold is generated using a specific algorithm. The algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold. Therefore, it can be costly for a high-resolution image.
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