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max is the maximum value for color level in the input image within the selected kernel. min is the minimum value for color level in the input image within the selected kernel. [4] Local contrast stretching considers each range of color palate in the image (R, G, and B) separately, providing a set of minimum and maximum values for each color palate.
histogramCounts is a 256-element histogram of a grayscale image different gray-levels (typical for 8-bit images). level is the threshold for the image (double).
Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed. [1] [2] When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of ...
For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.
A co-occurrence matrix or co-occurrence distribution (also referred to as : gray-level co-occurrence matrices GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in ...
In mathematical morphology and digital image processing, a top-hat transform is an operation that extracts small elements and details from given images.There exist two types of top-hat transform: the white top-hat transform is defined as the difference between the input image and its opening by some structuring element, while the black top-hat transform is defined dually as the difference ...
Color images can also be thresholded. One approach is to designate a separate threshold for each of the RGB components of the image and then combine them with an AND operation. This reflects the way the camera works and how the data is stored in the computer, but it does not correspond to the way that people recognize color.
Erosion (usually represented by ⊖) is one of two fundamental operations (the other being dilation) in morphological image processing from which all other morphological operations are based. It was originally defined for binary images, later being extended to grayscale images, and subsequently to complete lattices.