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In image analysis, basic objects and their relationships are extracted from a picture given
in unstructured form. Color is one of the powerful descriptors of an image. The color, we
see, depends on the type of graphic file format, resolution of an image, and resolution and
type of input/output devices. Also, the computer identifies color by its Red, Green and
Blue components value and human eyes identify color by its name. Moreover, human eye
has several limitations in processing the color like it cannot distinguish between all colors
and cannot resolve the components of a color, i.e., visually one cannot find and input values
of individual R, G and B components of a particular color. The model for color analysis
is designed and developed to filter patterns of colors from the whole image or
interested Region of Image (ROI), as per the control parameter. The control parameter could be
color value, one point of an image, color name or range of colors. Depending on the value of
the control parameter, the model filters the color information from the image. The model
helps the domain experts of diverse area to study the various relationships among the
different parameters in an image.
Image processing and analysis techniques are used to change the visual
presentation of a picture in such a way that human perception of the data contained in the
picture is improved (Shah and Patel, 2003). Typical methods
include: filtering, contrast improvement, and noise suppression
(Rafael and Richard, 2002). These methods can be used to develop models so that human beings can more easily perceive the
structures within the pictures. Color is one of the powerful descriptor of an image. Several
color models exist to represent a color. Some of them are
hardware-oriented and some, application-oriented. The color monitor uses the RGB color model to represent a
color (Earl et al., 1999). In this model, a color is represented by its Red (R), Green (G) and
Blue (B) component values. A digital image is made up of a set of pixels. In the RGB
color model, the triplet (r, g, and b) represents each pixel of an image. The color of a
pixel depends on the type of graphic file format in which it is stored
(Keith Rule, 1998),
the resolution in which it is stored and the resolution and type of input/output
devices. At present, powerful hardware and advanced graphics formats facilitate the generation of more detailed pixel information. Nowadays, most common hardware and software
can represent colors in an image using 24-bits. Thus, computers can process more than
16 million distinct colors. Hence, computers can represent colors very accurately.
Also, computers understand colorS by their R, G and B component
values, whereas human beings understand colors by their names and human eye cannot resolve
the components of a color mixture (Anil, 1997). Moreover, the eye has several limitations
in processing the color and it cannot distinguish all the colors;
hence, it cannot filter a particular color from an image. There is a need to have a model, which filters
patterns of color from an image. The model is developed with
the same objective. |
