A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Abstract

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images 'Lena', 'F16', and 'Peppers' in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

Brain MR image segmentation using NAMS in pseudo-color

“…NAMS algorithm is s an anti-packing problem based on the Non-symmetry and Anti-packing Model (NAM) [17][18][19]. In image pattern, the idea of the NAMS can be described as follows: give an image (packed pattern) and some squares (predefined sub-patterns) of different sizes, and then represent the image (packed pattern) using the combination of the squares (subpatterns) [20,21].…”

A Novel Algorithm for Color Image Representation Using Non-symmetry and Anti-Packing Model with Squares

“…Since the NAMS is based on the NAM which is an anti-packing problem [18][19][20], the idea of the NAMS can be described as follows: give a color picture (packed pattern) and squares (predefined sub-patterns), pick up these squares (sub-patterns) from the color picture (packed pattern), and then represent the color picture (packed pattern) with the combination of the squares (sub-patterns) [21][22][23].…”

Accurate Computation of Geometric Moments Using Non-symmetry and Anti-packing Model for Color Images

“…Inspired by the concept of the packing problem, Chen et al presented a novel non-symmetry and anti-packing model (NAM) for image representation in order to represent the image pattern more effectively [16]. By using the NAM representation and the extended shading approach, Zheng et al proposed a fast algorithm for computing the lower order moments of gray images [17]. However, the computation results of the geometric moments in [15] and [17] are both approximate since they used the Gouraud shading method and the extended Gouraud shading method, respectively.…”

“…By using the NAM representation and the extended shading approach, Zheng et al proposed a fast algorithm for computing the lower order moments of gray images [17]. However, the computation results of the geometric moments in [15] and [17] are both approximate since they used the Gouraud shading method and the extended Gouraud shading method, respectively. In fact, accurate computation of the geometric moments on gray or color images are also very important in the field of image processing [18]- [20].…”