By Diego Oliva, Erik Cuevas
This booklet offers a research of using optimization algorithms in complicated photo processing difficulties. the issues chosen discover parts starting from the speculation of picture segmentation to the detection of advanced gadgets in clinical photographs. in addition, the thoughts of computing device studying and optimization are analyzed to supply an summary of the applying of those instruments in picture processing.
The fabric has been compiled from a instructing viewpoint. consequently, the publication is essentially meant for undergraduate and postgraduate scholars of technology, Engineering, and Computational arithmetic, and will be used for classes on synthetic Intelligence, complicated photo Processing, Computational Intelligence, and so on. Likewise, the fabric may be valuable for study from the evolutionary computation, synthetic intelligence and snapshot processing communities.
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Neural network training [11]. Image processing [12, 13]. Medical image [14]. Considering the implementations of EMO in the state-of-the-art, it is possible to say that EMO is a promising optimization technique, with a huge field of applications. In the following subsection, first the basic structure of EMO will be analyzed. After that their implementation using a numerical example will be presented. 2 Optimization Inspired in Electromagnetism As was mention previously, the EMO algorithm solve optimization problems defined as: minimize f ðXÞ; X 2 ½l; u ð3:1Þ where l and u areÈthe lower and upper bounds of theÉ search space Expert Syst. Appl. 37(12), 8871–8878 (2010) 12. : Template matching using an improved electromagnetism-like algorithm. Appl. Intell. 41, 791–807 (2014) 13. : Circle detection using electro-magnetism optimization. Inf. Sci. (Ny) 182(1), 40–55 (2012) 14. : White blood cell segmentation by circle detection using electromagnetism-like optimization. Comput. Math. Methods Med. 2013 (2013) 15. : Basic Electromagnetism. Academic Press, New York (1968) 16. : The global optimization problem: an introduction. 16) depending on the thresholding method (Otsu or Kapur respectively). Step 7: Compute the charge of each particle and the total force vector according with the method described in Chap. 3. Step 8: Move the entire population Sct along the total force vector. Step 9: Apply the local search to the moved population and select the best elements of this search based on their objective function values. Step 10: The t index is increased in 1, If t ! Itermax or if the stop criteria is satisfied the algorithm finishes the iteration process and jump to step 11.