Applications of Computational Geometry and Computer Vision

Recent advances in machine learning research promise to bring us closer to the original goals of artificial intelligence. Spurred by recent innovations in low-cost, specialized hardware and incremental refinements in machine learning algorithms, machine learning is revolutionizing entire industries. Perhaps the biggest beneficiary of this progress has been the field of computer vision. Within the domains of computational geometry and computer vision are two problems: Finding large, interesting holes in high dimensional data, and locating and automatically classifying facial features from images. State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles are introduced. The problem of finding holes is then linked to the problem of extracting features from images and deep learning methods such as convolutional neural networks. The performance of the hole-finding algorithm is measured using multiple standard machine learning benchmarks as well as a 39 dimensional dataset, thus demonstrating the utility of the method for a wide range of data.

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2016

Author dc:creator

• Lemley, Joseph

Contributors dc:contributor

• Razvan Andonie
• Boris Kovalerchuk
• Donald Davendra

• Artificial Intelligence
• Algorithms
• Robotics
• Artificial Intelligence and Robotics
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/383

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-1377