Abstract
Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.
Highlights
Cellular automata [1] are a discrete dynamic system in space, time, and state
The mapping is similar to cellular automata in some aspects, the mapping is discrete in time evolution, and the value of state variables of cellular automata is continuous
If mapping corresponds to ordinary differential equations in continuous dynamical systems, cellular automata correspond to partial differential equations in continuous dynamical systems, because partial differential equations are in time
Summary
Cellular automata [1] are a discrete dynamic system in space, time, and state. The mapping is similar to cellular automata in some aspects, the mapping is discrete in time evolution, and the value of state variables of cellular automata is continuous. If mapping corresponds to ordinary differential equations in continuous dynamical systems, cellular automata correspond to partial differential equations in continuous dynamical systems, because partial differential equations are in time. The values of space and state variables are continuous. Cellular automata have become an extreme representative in the field of discrete dynamical systems. The space of cellular automata is composed of a series of cells arranged in grid distribution. The grid space can be one-dimensional, two-dimensional, or high-dimensional. Grille space can be limited or infinite
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