Investigation on the effect of a Gaussian Blur in image filtering and segmentation

The present work investigates the qualitative and quantitative effects of the convolution of a Gaussian function with an image. Besides the evaluation of the commonly called "Gaussian-blur" in the filtering of images, this work also investigates a methodology of segmentation using Gaussian blurring. Noise is inherent to the physical process of acquisition. Therefore, to know the effects of a filtering technique it is fundamental to choose the right technique to filter the image properly, since the segmentation process could be very expensive and time-consuming. An automated method for segmentation that saves time and human labor is always desirable. To evaluate the filtering characteristics, we chose a Quality Index in order to analyze in a quantitative way the effects of the convolution. Results show that the Gaussian Blur technique is to be used in images with high noise and with a Gaussian function of small variance whereas larger variance Gaussian function is more relevant in segmentation of images.