Edge detection plays an important role in image processing, pattern recognition and computer vision applications. Most of edge detection schemes are based on finding maximum in the first derivative of the image function or zero crossings in the second derivative of the image function. Various methods of edge detection for color images, including techniques extended from monochrome edge detection as well as vector space methods are presented. This research presents a comparative study on different methods of edge detection of color images. The methods are based on vector space, color space and numerical methods. Seven different colored images are test in this research. Performance is analyzed depending on Mean Square Error (MSE). The experimental results show that applying vector value (Jacobian method )will create a thick and disconnected edge with all operators Sobel, Prewitt and Log. While the least square method produce edges that are much thicker but continuous. The best performance was found when using YCbCr luminance (Y) and chrominance (Cb and Cr) method, the edges are sharpened, continuous, and not thickness. They are similar with Sobel and Prewitt operators nonetheless with some missing edges while it is better with Log operator.
Noise reduction or noise removal is an important task in image processing. In general, Results of the noise removal have a strong influence on the quality of the following image processing techniques. On the other side, the integrated system of neurofuzzy networks are more interesting and applied for different applications. In this contribution, two neurofuzzy network schemes have been presented for impulsive noise removal. The computation is reduced by using an artificial image in training. High performances are obtained. Results of neurofuzzy schemes show that the performance is increased as the ratio of the noise is increased. The presented schemes are used for grayscale and also for true color images.
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