Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
Fingerprint matching by genetic algorithms
Pattern Recognition
Lossless data hiding for color images based on block truncation coding
Pattern Recognition
A robust method for partial deformed fingerprints verification using genetic algorithm
Expert Systems with Applications: An International Journal
Gender classification based on feature selection using genetic algorithms
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Combined structure and motion extraction from visual data using evolutionary active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
A robust iterative hypothesis testing design of the repeated genetic algorithm
Image and Vision Computing
Using genetic algorithms for fingerprint core point detection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
An effective colour feature extraction method using evolutionary computation for face recognition
International Journal of Biometrics
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Investigates the application of genetic algorithms (GAs) for recognizing real 2D or 3D objects from 2D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e. we assume a pre-defined set of models), while our recognition strategy relies on the theory of algebraic functions of views. According to this theory, the variety of 2D views depicting an object can be expressed as a combination of a small number of 2D views of the object. This implies a simple and powerful strategy for object recognition: novel 2D views of an object (2D or 3D) can be recognized by simply matching them to combinations of known 2D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time-consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic searching in the transformation space, we use singular value decomposition and interval arithmetic to restrict the genetic search to the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and quickly