A backprojection algorithm for electrical impedance imaging
SIAM Journal on Applied Mathematics
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Stability-based validation of clustering solutions
Neural Computation
New indices for cluster validity assessment
Pattern Recognition Letters
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
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Electrical tomography (ET) is a technique to visually reconstruct inhomogeneous medium distributions by injecting currents or voltages at the boundary of the medium and measuring the resulted changes in the investigated fields. The ET techniques have been widely used in industrial practices owing to the low cost, rapid response time, non-existent radiation exposure, and non-intrusive characteristics comparing to other tomographic modalities. However, the spatial resolution of ET images using single modality or single-driven patterns (adjacent pattern vs. opposite pattern for imaging reconstruction) is low, which may limit its applications. In this research, the application of fuzzy clustering based fusion techniques for ET imaging is studied. Both multi-modality imaging and multi-driven patterns are of interest. Specifically, two modality images are fused: Electrical Capacitance Tomography (ECT), which performs well for imaging material of large permittivity difference, and Electrical Resistance Tomography (ERT), which is suited for imaging materials having large conductivity differences. The research also explores the fusion of adjacent and opposite patterns for either ECT or ERT modalities. Experiments show that the proposed method can construct high quality ET images by discovering the strong complementary natures of the modalities and/or driven patterns.