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In image processing, computer vision and related fields, an image moment is a certain particular weighted average of the image pixels' intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. Image moments are useful to describe objects after segmentation.
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.
hists is a 2D-histogram of grayscale value and neighborhood average grayscale value pair. total is the number of pairs in the given image.it is determined by the number of the bins of 2D-histogram at each direction. threshold is the threshold obtained.
This technique is commonly used for simplifying images, reducing storage requirements, and facilitating processing operations. In grayscale quantization, an image with N intensity levels is converted into an image with a reduced number of levels, typically L levels, where L<N. The process involves mapping each pixel's original intensity value ...
Users may add images to the data set by upload, and add labels or annotations to existing images. Due to its open nature, LabelMe has many more images covering a much wider scope than Caltech 101. However, since each person decides what images to upload, and how to label and annotate each image, the images are less consistent.
Grayscale images are distinct from one-bit bi-tonal black-and-white images, which, in the context of computer imaging, are images with only two colors: black and white (also called bilevel or binary images). Grayscale images have many shades of gray in between. Grayscale images can be the result of measuring the intensity of light at each pixel ...
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.
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.