A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of petrographical images of marbles
Computers & Geosciences
Simulating lava flows by an improved cellular automata method
Computers & Geosciences
Edge detection in petrographic images using the rotating polarizer stage
Computers & Geosciences
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Image Processing of Geological Data
Image Processing of Geological Data
Automated separation of touching grains in digital images of thin sections
Computers & Geosciences
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
An image processing algorithm for the reversed transformation of rotated microscope images
Computers & Geosciences
Data mining with cellular automata
ACM SIGKDD Explorations Newsletter
Expert Systems with Applications: An International Journal
Digital Analysis of Remotely Sensed Imagery
Digital Analysis of Remotely Sensed Imagery
Parallel cellular automata for large-scale urban simulation using load-balancing techniques
International Journal of Geographical Information Science
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Cellular automata (CA) are widely used in geospatial dynamic modeling and image processing. Here, we explore the application of two-dimensional cellular automata to the problem of grain boundary detection and extraction in digital images of thin sections from deformed rocks. The automated extraction of boundaries, which contain rich sources of information such as shape, orientation, and spatial distribution of grains, involves a CA Moore's neighborhood-based rules approach. The Moore's neighborhood is a 3x3 matrix that is used for changing states by comparing differences between a central pixel and its neighbors. In this dynamic approach, the future state of a pixel depends upon its current state and that of its neighbors. The rules that are defined determine the future state of each cell (i.e., on or off) while the number of iterations to simulate boundaries detection are specified by the user. Each iteration outputs different detection scenarios of grain boundaries that can be evaluated and assessed for accuracy. For a deformed quartz arenite, an r^2 of 0.724 was obtained by comparing manually digitized grains to model derived grains. The value of this proposed method is compared against a traditional manual digitization approach and a recent GIS-based method developed for this purpose by Li et al. (2007).