Researchers spend time and money developing techniques to solve some common problems associated with the rapidly developing field of image processing. 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. However, this approach is time consuming and computationally expensive. These expenses can be marginally reduced if a single ideal (optimal) scale is found in scale space analysis and then edge detection is performed using only that single ideal scale. This paper reports on a new approach to detecting 3-dimensional objects in their 2-dimensional projections using noise within the images. The novel idea is based on selecting one ideal scale for the entire image at which edge detection can be applied. The selection of an ideal scale is based on the hypothesis that "an ideal edge detection scale depends on the noise within an image". This paper aims at presenting the above hypothesis mathematically and throughout some experiments made on simple 3-dimensional objects.