From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition

• Autorzy: Rościszewski P.

Identyfikatory

DOI

10.7494/csci.2017.18.4.2119

• Języki publikacji: EN

Abstrakty

EN

Recently gathered image datasets and new capabilities of high performance computing systems allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels, instead of specific features. The principle of operation of deep artificial neural networks is more and more resembling of what we believe to be happening in the human visual cortex. In this paper we build up an understanding of convolutional neural networks through investigating supervised machine learning methods suchas K-Nearest Neighbors, linear classifiers and fully connected neural networks. We provide examples and accuracy results based on our implementation aimed for the problem of hand pose recognition.

Słowa kluczowe

EN

machine learning; artificial neural networks; computer vision

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2017
• Tom: Vol. 18 (4)
• Strony: 341--356
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr., tab.

Twórcy

autor

Rościszewski P.

• Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

Bibliografia

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