How can adaptive thresholding be applied in image processing?

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Adaptive thresholding is a technique used in image processing to binarize an image by segmenting the image into two parts -- pixels that are above a threshold value and pixels that are below a threshold value.

In adaptive thresholding, the threshold value is not fixed as it varies from one region to another in the image. This variation in the threshold value is based upon the local image characteristics of the region being processed. Adaptive thresholding is performed using a sliding window over the entire image area, called an adaptive window.

The following steps describe the process of adaptive thresholding in image processing:

1. The image is divided into small sub-regions, called tiles.
2. For each tile, the threshold value is calculated based on the mean or median intensity value of the pixels within the tile.
3. The threshold value obtained in step 2 is then used to binarize the tile, with pixels below the threshold value being set to black and pixels above being set to white.
4. Finally, the tiles are merged to form the output image.

Adaptive thresholding can be used in various applications of image processing such as object detection, background subtraction, and image segmentation. It is particularly useful in processing images with varying lighting conditions, low contrast, or complex background.