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If we use Harris corner detector in a color image, the first step is to convert it into a grayscale image, which will enhance the processing speed. The value of the gray scale pixel can be computed as a weighted sums of the values R, B and G of the color image, {,,}, where, e.g.,
% Note if input image I was already a grayscale image, grayscale channel % would have simply been equal to input image, i.e., gray channel = I gray_channel = rgb2gray (I); It is clear from the above examples that a channel can be generated by either simply extracting specific information from the original image or by manipulating the input ...
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 ...
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.
A channel in this context is the grayscale image of the same size as a color image, [citation needed] made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel.
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.
For each edge pixel x in the image, find the gradient ɸ and increment all the corresponding points x+r in the accumulator array A (initialized to a maximum size of the image) where r is a table entry indexed by ɸ, i.e., r(ɸ). These entry points give us each possible position for the reference point.
It has a probability density function p r (r), where r is a grayscale value, and p r (r) is the probability of that value. This probability can easily be computed from the histogram of the image by = Where n j is the frequency of the grayscale value r j, and n is the total number of pixels in the image.