Fireworks Algorithm Based Image Registration

In the Image Processing (IP) domain, optimization algorithms have to be applied in many cases. Nature-inspired heuristics allow obtaining near optimal solutions using lower computing resources. In this paper the Fireworks Algorithm (FWA) behavior is studied for Image Registration (IR) problems. The IR results accuracy is analyzed for different types of images, mainly in case of pixel based registration using the Normalized Mutual Information. FWA is compared to Particle Swarming (PSO), Cuckoo Search (CSA) and Genetic Algorithms (GA) in terms of results accuracy and number of objective function evaluations required to obtain the optimal geometric transform parameters. Because the pixel based IR may fail in case of images containing graphic drawings, a features based IR approach is proposed for this class of images. Comparing to other nature inspired algorithms, FWA performances are close to those of PSO and CSA in terms of accuracy. Considering the required computing time, that is determined by the number of cost function evaluations, FWA is little slower than PSO and much faster than CSA and GA.

[1]  Silviu-Ioan Bejinariu,et al.  Social behavior in bacterial foraging optimization algorithm for image registration , 2014, 2014 18th International Conference on System Theory, Control and Computing (ICSTCC).

[2]  Ying Tan,et al.  S-metric based multi-objective fireworks algorithm , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[3]  Silviu-Ioan Bejinariu,et al.  Image processing by means of some bio-inspired optimization algorithms , 2015, 2015 E-Health and Bioengineering Conference (EHB).

[4]  Ying Tan,et al.  Fireworks Algorithm for Optimization , 2010, ICSI.

[5]  Silviu-Ioan Bejinariu,et al.  Automatic multi-threshold image segmentation using metaheuristic algorithms , 2015, 2015 International Symposium on Signals, Circuits and Systems (ISSCS).

[6]  Silviu-Ioan Bejinariu.  Image registration using Bacterial Foraging Optimization Algorithm on multi-core processors , 2013, 2013 4th International Symposium on Electrical and Electronics Engineering (ISEEE).

[7]  Ying Tan,et al.  Fireworks Algorithm: A Novel Swarm Intelligence Optimization Method , 2015 .

[8]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[9]  Ying Tan,et al.  Enhanced Fireworks Algorithm , 2013, CEC 2013.

[10]  Silviu Ioan Bejinariu,et al.  Medical Image Registration by means of a Bio-Inspired Optimization Strategy , 2012, Comput. Sci. J. Moldova.

[11]  Ying Tan,et al.  Adaptive Fireworks Algorithm , 2014 .

[12]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[13]  Ramona Luca,et al.  Parallel Processing and Bio-inspired Computing for Biomedical Image Registration , 2014, Comput. Sci. J. Moldova.

[14]  Ying Tan,et al.  A Cooperative Framework for Fireworks Algorithm , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.