Sub Pixel Analysis on Hypothetical Image by using Colorimetry
Merugu Suresh1, Kamal Jain2
1Merugu Suresh, Research Scholar, Department of Civil Engineering, Indian Institute of Technology (IIT) Roorkee (Uttarakhand), India.
2Dr. Kamal Jain, Professor, Department of Civil Engineering, Indian Institute of Technology (IIT) Roorkee (Uttarakhand), India.
Manuscript received on 21 September 2013 | Revised Manuscript received on 28 September 2013 | Manuscript published on 30 September 2013 | PP: 84-91 | Volume-2 Issue-4, September 2013 | Retrieval Number: D0780092413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Color, more generally it is a signature associated to each object which makes it recognizable and is highly dependent on the nature of the light source, which can be either natural (sun) or artificial (light bulbs). As the perception of color involves a complex processing by the brain, and could not be restricted to something like “the spectrum of the light captured by the human eye”. The increased use of color has brought with its new challenges. In order to meaningfully record and process color images, it is essential to understand the mechanisms of color vision and the capabilities and limitations of color imaging devices. It is also necessary to develop algorithms that minimize the impact of device limitations and preserve color information as images are exchanged between devices. The colours in real world have sharp boundaries we know exactly where a colour starts and where it ends. But when we take image of such an area the image is expressed in pixels, each pixel representing one value often should be a grey value in each band. These pixels don’t express the boundaries exactly as sharp as they are in reality; we observe a transition from one colour to some colour other than the second colour.
Keywords: Colorimetry, CIE Chromaticity Diagram, Tristimulus values, Pixel Analysis, Statistical Measures, Spectral Colors, Color Matching Function.
Scope of the Article: Image Processing and Pattern Recognition