The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image...The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.展开更多
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 develope...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 tile 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.展开更多
基金Supported by the National High Technology Research and Development Program of China (No. 2006AA04Z211)
文摘The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.
文摘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 tile 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.
