IEEE Transactions on Pattern Analysis and Machine Intelligence
Kriging as a surrogate fitness landscape in evolutionary optimization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Infodynamics: Analogical analysis of states of matter and information
Information Sciences: an International Journal
Computational Optimization and Applications
Template Matching Techniques in Computer Vision: Theory and Practice
Template Matching Techniques in Computer Vision: Theory and Practice
Preserving and exploiting genetic diversity in evolutionary programming algorithms
IEEE Transactions on Evolutionary Computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Template matching using chaotic imperialist competitive algorithm
Pattern Recognition Letters
Diversity Management in Evolutionary Many-Objective Optimization
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. The TM approach seeks the best possible resemblance between a sub-image, known as template, and its coincident region within a source image. TM has two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized Cross-Correlation (NCC) value (similarity measurement) for all elements in the source image (search strategy). Unfortunately, the use of such approach is strongly restricted since the NCC evaluation is a computationally expensive operation. Recently, several TM algorithms that are based on evolutionary approaches, have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the states of matter phenomenon is proposed to reduce the number of search locations in the TM process. In the proposed approach, individuals emulate molecules that experiment state transitions which represent different exploration-exploitation levels. In the algorithm, the computation of search locations is drastically reduced by incorporating a fitness calculation strategy which indicates when it is feasible to calculate or to only estimate the NCC value for new search locations. Conducted simulations show that the proposed method achieves the best balance in comparison to other TM algorithms considering the estimation accuracy and the computational cost.