Measuring distinct regions of grayscale image using pixel values

Abstract

Grayscale is a series of shades of gray without apparent color. The total absence of transmitted or reflected light, which is the darkest shade, black. The total reflection or transmission of light at all observable wavelengths, which is nothing but lightest possible shade i.e., white. Intermediate shades of gray are characterized by equal brightness levels of the primary colors (red, green and blue) for transmitting light, or equal amounts of the three primary pigments (magenta,cyan, and yellow) for reflected light. This paper focuses mainly on measuring the properties of objects in a grayscale image using Regionprops to calculate the standard Deviation. This is achieved by segmenting a grayscale image to get objects of a binary image. Although, the common problem of including chromatic values to a grayscale image has objective solution,not exact, the present approach tries to provide an approach to help minimize the amount of human labor required for this task. We transfer the source’s whole color “mood” to the target image by matching texture information and luminance between the images rather than selecting RGB colors from a group of colors to an individual color components. We pick out to transfer only chromatic information and retain the target image’s original luminance values. Further, the procedure is improved by permitting the user to match areas of the two images with rectangular swatches. It is essential to develop grayscale image pixel value, resultant to each object in the binary image to inspect the original grayscale image.Based on the original grayscale image pixel values, the pixel value properties in grayscale image are used to do routine calculations.