| DC Field | Value | Language |
|---|
| dc.contributor.author | Rybchak, Z. | - |
| dc.contributor.author | Basystiuk, O. | - |
| dc.date.accessioned | 2018-02-12T13:14:17Z | - |
| dc.date.available | 2018-02-12T13:14:17Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.citation | Rybchak Z. Analysis of computer vision and image analysis technics / Z. Rybchak, O. Basystiuk // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. – P. 79–84. – Bibliography: 21 titles. | uk_UA |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/39424 | - |
| dc.description.abstract | Computer vision and image recognition are one of the most popular theme nowadays. Moreover, this technology developing really fast, so filed of usage increased. The main
aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in computer vision and image recognition. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Commission of Motorization and Energetics in Agriculture | uk_UA |
| dc.subject | computer vision | uk_UA |
| dc.subject | image recognition | uk_UA |
| dc.subject | object recognition | uk_UA |
| dc.subject | machine learning | uk_UA |
| dc.subject | computer with high-level understanding | uk_UA |
| dc.subject | digital images processing | uk_UA |
| dc.subject | scene reconstruction | uk_UA |
| dc.title | Analysis of computer vision and image analysis technics | uk_UA |
| dc.type | Article | uk_UA |
| dc.contributor.affiliation | Lviv Polytechnic National University | uk_UA |
| dc.coverage.country | PL | uk_UA |
| dc.format.pages | 79-84 | - |
| dc.relation.referencesen | 1. Richard Szeliski. 2011. Computer Vision: Algorithms and Applications. – United Kingdom: Springer London, 812 p. 2. Richard Szeliski. 2014. Concise Computer Vision: An Introduction into Theory and Algorithms. – United Kingdom: Springer London, 429 p. 3. Brytik V., Grebinnik O., Kobziev V. 2016. Research the possibilities of different filters and their application to image recognition problems. – Poland: ECONTECHMOD. An international quarterly journal, Vol. 5, No. 4, рр. 21–27. 4. Ethem Alpaydin. 2010. Introduction to Machine Learning. London: The MIT Press, 584p. 5. Satya Mallick. 2016. Image Recognition and Object Detection. Available online at: http://www. learnopencv.com/image-recognition-and-objectdetection- part1/ 6. Ken Weiner. 2016. Why image recognition is about to transform business. Available online at: https://techcrunch.com/2016/04/30/why-imagerecognition- is-about-to-transform-business/ 7. John C. Russ, F. Brent Neal. 2015. The Image Processing Handbook. United States of America: Florida CRC Press, 1035 p. 8. Venmathi E. Ganesh, N. Kumaratharan. 2016. Kirsch Compass Kernel Edge Detection Algorithm for Micro Calcification Clusters in Mammograms. Middle-East Journal of Scientific Research, 24 (4), рр. 1530–1535. 9. Brytik V., Zhilina E., 2014. Investigation possibilities of various filters which used in pattern recognition problems Bionica Intellecta, 2(83), рр. 88–95. 10. Semenets V., Natalukha Yu., O. Taranukha, Tokarev V., 2014. About One Method of Mathematical Modelling of Human Vision Functions. ECONTECHMOD. An international quarterly journal, Vol. 3, No. 3, рр. 51–59. 11. Nick McClure. 2017. TensorFlow Machine Learning Cookbook. Packt Publishing, 370 p. 12. Tensorflow. Image Recognition. Available online at: https://www.tensorflow.org/tutorials/image_recog nition 13. Michael Nielsen. 2017. Using neural nets to recognize handwritten digits. Available online at: http://neuralnetworksanddeeplearning.com/chap1.html 14. Michael Nielsen. 2017. How the backpropagation algorithm works. Available online at: http://neuralnetworksanddeeplearning.com/chap2.html 15. Michael Nielsen. 2017. Improving the way neural networks learn. Available online at:http://neuralnetworksanddeeplearning.com/chap3.html 16. Michael Nielsen. 2017. Why are deep neural networks hard to train? Available online at: http://neuralnetworksanddeeplearning.com/chap5.html 17. The British Machine Vision Association and Society for Pattern Recognition. 2017. What is computer vision? Available online at: http://www.bmva.org/visionoverview 18. Gary Bradski, Adrian Kaehler. 2016. Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library. O'ReillyMedia, 1024 p. 19. Parker J. R. 2011. Algorithms for Image Processing and Computer Vision. Wiley, 504 p. 20. Simon J. D. Prince. 2014. Computer Vision: Models, Learning, and Inference. Cambridge University Press, 505 p. 21. Giovanni Maria Farinella, Sebastiano Battiato, Roberto Cipolla. 2015. Advanced Topics in Computer Vision. Springer Science & Business Media, 433 p. Lviv | uk_UA |
| dc.citation.journalTitle | Econtechmod | - |
| dc.citation.volume | Volum 6, number 2 | - |
| dc.coverage.placename | Lublin ; Rzeszow | uk_UA |
| Appears in Collections: | Econtechmod. – 2017. – Vol. 6, No. 2
|
