Noise-dependent optimal scale in edge detection

Image processing techniques have common problems that have had many researchers spending time and money addressing them and trying to find solutions. These problems include poor edge detection in low contrast images, speed of recognition and high computational cost. Scale space analysis is an efficient solution to the edge detection of objects in low to high contrast images. However, this approach is time consuming and computationally expensive. These expenses can be marginally reduced if an optimal scale (ideal scale) is defined in scale space edge detection. This paper reports on a new approach to detecting 3-dimensional objects in their 2-dimensional projected images using noise within the images. The novel idea is based on selecting one optimal scale (ideal scale) for the entire image at which scale space edge detection can be applied. The selection of an ideal scale is based on the hypothesis that "the optimal edge detection scale (ideal scale) depends on the noise within an image". This paper aims at providing the experimental evidence on the dependency of optimal scale on noise within images.