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Gauging Difficulty of Image Segmentation

Published

Author(s)

Marek Franaszek

Abstract

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.
Citation
Technical Note (NIST TN) - 2207
Report Number
2207

Keywords

Database of 2D images, image segmentation, object recognition, training Machine Learning algorithms.

Citation

Franaszek, M. (2022), Gauging Difficulty of Image Segmentation, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2207, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932431 (Accessed August 7, 2022)
Created March 25, 2022