Abstract :
This is a design approach of software on image or pattern-recognition while eliminating the color, gray-code of an image for comparison. The approach is quite effective for both black and white scenes or color scenes and images. A particular part or parts of a given image is selected and compared with the contents of database (image data). The approach will be the key idea for all the verification and identification systems. The input is image (black and white or color) and the output is a set of pixels. The approach uses i) Minterm (minimized term) Extraction for black and white image analysis that handles various gray levels through variables like A,B,C,D. White pixels can be maintained through their inverse i.e. A´, B´, C´, D´, respectively and ii) Maxterm (maximized term) Extraction for color images that the RGB (Red ,Green, Blue and Colors) through nested minterm extraction. Color Green is processed through G1, G2, G3, G4, color Red is processed through R1, R2 R3, R4 and color Blue is processed through B1 B2 B3 B4.
Keywords :
feature extraction; image colour analysis; image recognition; Quine Mc Cluskey Tabular method; black and white image analysis; image color analysis; image database; image extraction; image recognition; maxterm extraction; minterm extraction; pattern recognition; Gray level; Minterm; Prime implicant; Sum of Products; probabilistic determination;