Download e-book for kindle: Applications of Evolutionary Computation in Image Processing by Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros

By Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros

ISBN-10: 3319264605

ISBN-13: 9783319264608

ISBN-10: 3319264621

ISBN-13: 9783319264622

This booklet offers using effective Evolutionary Computation (EC) algorithms for fixing assorted real-world photograph processing and development acceptance difficulties. It presents an summary of different points of evolutionary equipment with a view to allow the reader in achieving an international figuring out of the sphere and, in undertaking stories on particular evolutionary concepts which are regarding functions in photograph processing and development attractiveness. It explains the elemental principles of the proposed purposes in a manner which may even be understood by means of readers outdoor of the sector. photograph processing and trend reputation practitioners who're now not evolutionary computation researchers will savor the mentioned concepts past basic theoretical instruments considering that they've been tailored to resolve major difficulties that typically come up on such components. nonetheless, participants of the evolutionary computation group can find out how within which snapshot processing and trend popularity difficulties will be translated into an optimization activity. The booklet has been established in order that each one bankruptcy might be learn independently from the others. it may function reference booklet for college students and researchers with uncomplicated wisdom in photograph processing and EC methods.

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Additional resources for Applications of Evolutionary Computation in Image Processing and Pattern Recognition

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7 dB respectively. 65. 13. 5). 7 Conclusions In this chapter, an algorithm based on Artificial Bee Colony (ABC) is presented to reduce the number of search locations in the BM process. The algorithm uses a simple fitness calculation approach which is based on the NNI algorithm. The method is able to save computational time by identifying which fitness value can be just estimated or must be calculated instead. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of ABC.

3. Determine the scout bees. 3 3 Motion Estimation Based on Artificial Bee Colony (ABC) Initializing the Population The algorithm begins by initializing Np food sources. Each food source is a Ddimensional vector containing the parameter values to be optimized which are randomly and uniformly distributed between the pre-specified lower initial parameter bound xlow and the upper initial parameter bound xhigh . j j high xj;i ¼xlow À xlow j þ randð0; 1Þ Á ðxj j Þ; j ¼1; 2; . ; D; i ¼ 1; 2; . ; Np : ð3:1Þ with j and i being the parameter and individual indexes respectively.

When the fitness function (nectar amount) cannot be further improved after a maximum number of cycles is reached, its related food source is assumed to be abandoned and replaced by a new randomly chosen food source location. The performance of ABC algorithm has been compared to other optimization methods such as GA, Differential Evolution (DE) and Particle Swarm Optimization (PSO) [29, 30]. The results showed that ABC can produce optimal solutions and thus is more effective than other methods in several optimization problems.

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Applications of Evolutionary Computation in Image Processing and Pattern Recognition by Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros

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