Gauging the difficulty of image segmentation
Marek Franaszek · 2022
Image segmentation is the first step in a complex process of object recognition. This report presents a method to gauge the difficulty of segmentation by calculating a scalar parameter Q for an image. This parameter depends on a distribution of the intensity of the grayscale image and the distribution of the clustering of pixels. It is assumed that images with a smaller number of clusters are easier to segment than images with a large number of clusters. Since segmentation precedes any human perception and categorization, the distribution of parameter Q introduced in this study may be useful in characterizing the variability of images collected in a training database for the development of object recognition algorithms which use Machine Learning (ML) methods. Parameter Q can be especially useful for building a representative dataset of images for training ML algorithms. To demonstrate a link between particular values of Q and different segmentation conditions, a few grayscale images were distorted by some common transformations (like Gaussian noise, median filtering, reduction in grayscale) and the corresponding values of parameter Q were calculated. To demonstrate a possible use of the parameter Q on data other than grayscale images, depth images of flat planar targets taken by two depth cameras were also processed.