Download Artificial Intelligence Perspectives and Applications: by Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova, PDF

By Radek Silhavy, Roman Senkerik, Zuzana Kominkova Oplatkova, Zdenka Prokopova, Petr Silhavy

This quantity is predicated at the examine papers provided within the 4th desktop technological know-how online Conference.

The quantity synthetic Intelligence views and functions provides new techniques and techniques to real-world difficulties, and specifically, exploratory learn that describes novel methods within the box of synthetic intelligence. specific emphasis is laid on sleek traits in chosen fields of curiosity. New algorithms or tools in numerous fields also are presented.

The laptop technological know-how online convention (CSOC 2015) is meant to supply a global discussion board for discussions at the newest top of the range examine leads to all parts concerning machine Science.

The addressed issues are the theoretical features and purposes of desktop technology, man made Intelligences, Cybernetics, Automation keep watch over concept and software program Engineering.

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Additional resources for Artificial Intelligence Perspectives and Applications: Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015), Vol 1: Artificial Intelligence Perspectives and Applications

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If ≤ | |, then go to Step 12, else then go to Step 20. Step 12. ℎ = 1, = 1. Step 13. If ℎ ≤ | |, then go to Step 14, else then go to Step 17. Step 14. If , = −1, then go to Step 15, else then go to Step 16. > , то = . Step 15. If Step 16. ℎ = ℎ + 1. Go to Step 13. Step 17. If < + 1, то = 1. Step 18. = + 1. 28 O. Dolinina and A. Shvarts Step 19. = + 1. Go to Step 11. Step 20. If = 0, then go to Step 21, else then go to Step 10. Step 21. For each : ( )= . Using calculated values of ( ∶ ∈ ) following sets are formed ( )= , = 1, … (5) with – total number of groups of defuzzyfied variables.

Acknowledgement. This research is funded by Vietnam National University of Ho Chi Minh City (VNU-HCMC) under the project ”Features descriptor under variation condition for real-time face recognition application”, 2014. References 1. : 2D and 3D face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007) 2. : Face recognition with local binary patterns. , Matas, J. ) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004) 3. : Face description with local binary patterns: Application to face recognition.

Gallery of pose ranges Preprocessing Pose estimation ... 15-25 degree Pre-prossed image ... Original image 40-60 degree Database 15 degree 25 degree 40 degree 60 degree ... Predicted pose range: 40-60 degree ... Chosen gallery ... 40 degree ... 60 degree Fig. 6. Choosing gallery for a profile face image ... Pose Estimation Using Local Binary Patterns for Face Recognition LBP-based methods Pre-prossed image Matching 45 Id = 1 Feature vector Chosen gallery Feature set ... Id = 1 Id = 2 Id = n LBP-based Methods ...

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