grayscale

(redirected from Gray level)
Related to Gray level: Grey scale

gray·scale

 (grā′skāl′)
n.
1. A series of shades ranging from pure white to pure black, used in displaying monochromatic images.
2. An image displayed using such a series of shades.

grayscale

(ˈɡreɪˌskeɪl)
adj
(Computer Science) same as greyscale
Translations

grayscale

n (US) (Comput) → Graustufen pl
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References in periodicals archive ?
The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image.
The threshold is calculated on a pixel basis by moving a kernel or a window over the input gray level image.
For HF, individual pixel intensities were counted over an image area and were used to calculate the gray level histogram (Materka and Strzelecki, 1998).
Neighborhood filters create a new pixel by taking an average of similar gray level value of neighboring pixels.
where GLE represents the gray level energy with 256 bins and p(i) refers to the probability distribution functions of different gray levels, that is, the histogram count.
Hao and Zhang [8] extracted the first-order statistical feature, gray level cooccurrence matrix feature, and gray stroke matrix feature of normal liver CT image and primary liver cancer CT images and then selected features by t-test method.
The best matched domain block having high normalized cross correlation [24, 25] value may have large average gray level difference.
Image contrast enhancement using singular value decomposition for gray level images, Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on, IEEE, 1-5 (2011)
The topics include efficient color image segmentation by a parallel optimized (ParaOptiMUSIG) activation function, chaotic map model-based interference employed in a quantum-inspired genetic algorithm to determine the optimum gray level image thresholding, multi-objective genetic and fuzzy approaches in rule mining problems of knowledge discovery in databases, concept generation in knowledge representation using formal concept analysis, applying a functional approach to lists for developing relational model databases and Petri net analysis, and a Shannon-like solution for the fundamental equation of information science.
This model captures normal patterns in each mammographic case and enhances texture abnormalities based on gray level distribution of pixels within a local neighborhood.
Texture analysis on images are native and complex visual patterns that reproduce the data of gray level statistics, anatomical intensity variations, texture, spatial relationships, shape, structure and so on.