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ImageJ is a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). [ 2 ] [ 3 ] Its first version, ImageJ 1.x, is developed in the public domain , while ImageJ2 and the related projects SciJava , ImgLib2 , and SCIFIO are ...
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.
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 ...
Examples of conversion from a full-color image to grayscale using Adobe Photoshop's Channel Mixer, compared to the original image and colorimetric conversion to grayscale Conversion of an arbitrary color image to grayscale is not unique in general; different weighting of the color channels effectively represent the effect of shooting black-and ...
[1] [2] When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of information. [3] [4] Blob extraction is generally performed on the resulting binary image from a thresholding step, but it can be applicable to gray-scale and color images as well. Blobs may ...
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.
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 .
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.