From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition

Paweł Rościszewski

doi:10.7494/csci.2017.18.4.2119

Authors

Paweł Rościszewski Gdańsk University of Technology

DOI:

https://doi.org/10.7494/csci.2017.18.4.2119

Keywords:

machine learning, artificial neural networks, computer vision

Abstract

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.

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Author Biography

Paweł Rościszewski, Gdańsk University of Technology

Department of Electronics, Telecommunications and Informatics, PhD student

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