1. BY ABUBAKAR SADIQ MUHAMMAD MEVLANA UNIVERSITY KONYA,TURKEY Email: alsiddiq_uhd@yahoo.com

2. Introduction Colourspace & models RGB image edge detection YUV image edge detection Analysis of Results Conclusions 3. INTRODUCTION Edge detection is an important aspect of image processing whose goal is extracting edges in an image. It is often the first step in image segmentation A lot of researches have proposed many arithmetic on RGB(colour) image edge detection 4. However a number of shortcomings were noted that include -low speed of processing of each image -colour losses after processing of each image 5. Thispaper proposes a method of image edge detection using YUV colour space and histogram equalisation 6. The RGB colour space is the simplest colour space that comprises the primary colours Red, Green and BlueThis colour space explores a wide range of colour when mixed together among which are 7. YELLOW(RG)MAGENTA(RB)CYAN(GB) 8. The drawback of this model is that it does not suit the intuition(insight) of human psychologyWhy?Human intuition bring with it the aspect of lightness of the colour and the amount we use to colour a specific region 9. Also the distance between colour points is not equal to the vision characteristicsThus, it is not easy to obtain the Hue, Saturation and Brightness attribute in an RGB image 10. YUV model The YUV model defines a colour space in terms of one luma and two chrominance (U,V) components. The basic characteristics of these model is that each component is independent of the otherRGBYUV 11. Luminance(lightness) : is a measurement of the eyes perception of light intensity (brightness).Luma: is the component of a digital image that carries a monochrome portion that determines image lightness- it is often defined as gamma corrected luminanceChrominance: stands for the colour components obtained by deducting the luminance value Y from R and B 12. The fact that human visual systems is more sensitive to difference in lightness than in colour makes the model application in video standard. Application - Used in PAL, SECAM and composite colour video standards 13. Thecolour difference U and V in YUV colour space are given by the equation U= 0.493(B -Y) V=0.877( R Y)So that the conversion between RGB and YUV is as given by the equation 14. Y0.2990.5870.114RU0.147130.288860.436GV0.6150.514990.10001BR G B1 =0.0001.1400Y1-0.369-0.581U12.0290.000V 15. Theedge detection method employs the use of three filters Gradientfilter Laplacian filter Laplacian with control parameter()each RGB component is processed independently using the respective filter 16. Analysing the RGB imageEach R,G and B component is computedEach component is separately processed using the horizontal and vertical sobel operatorEach component is separately processed using the Laplacian operator and Laplacian with control parameter()The resulting RGB image is obtained from the separately processed components for each operator 17. Theresulting RGB image obtained from the separately processed components for each operator is then histogram equalised. 18. Thebasic idea is to process the Y component in the YUV image The YUV is obtained from the RGB image Each component Y, U & V are computed from the YUV image The Y component is processed using the respective filters listed earlier The Y component is then histogram equalised and the corresponding RGB is obtained 19. Flow chart representation of YUV image processing Obtain YUV image from corresponding RGBCompute each component of YUV imageProcess the Y component using respective filtersObtain the histogram equalisation of the Y componentObtain the corresponding RGB image of the HISTEQ YUV image 20. ORIGINALRGB IMAGE & YUV IMAGE 21. Horizontal & Vertical sobel on each RGB component 22. H & Vertical sobel on Y component of YUV image 23. Observe that single component in YUV produces equivalent of 3 components in RGB 24. Laplacian/ Laplacian() in RGB 25. Laplacian/ Laplacian() in YUV 26. Observe that a single component in YUV produces effect of 3 RGB component 27. Resultingprocessed images in RGB 28. Resulting processed Y components images in YUV 29. Observe that in RGB images losses its colour when compared to YUV after processing 30. HistogramEQU on RGB images 31. HistogramEQU on YUV images 32. Observe that the colour edges are still visible in YUV as compared to RGB with HISTEQ applied 33. CorrespondingHISTEQ YUV in RGB 34. Itcan be observed that the detected edges are more exact based on the proposed algorithm Theprocessing is faster and simple using a single component(Y-YUV) as compared to 3 RGB components Thecolour edges are also detected effectively when compared to the former 35. Thanks for listening