Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ryan McCoppin"'
Autor:
Mateen M. Rizki, Ryan McCoppin
Publikováno v:
SPIE Proceedings.
This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b9e55c8126920908f00cbae8e1f9ed5
https://doi.org/10.1117/12.2054045
https://doi.org/10.1117/12.2054045
Autor:
Andrew Freeman, Howard N. Rude, Nathan Koester, Ryan McCoppin, L.A. Tamburino, Mateen M. Rizki, Olga Mendoza-Schrock
Publikováno v:
SPIE Proceedings.
This paper describes the process used to collect the Seasonal Weather And Gender (SWAG) dataset; an electro-optical dataset of human subjects that can be used to develop advanced gender classification algorithms. Several novel features characterize t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d4e7d438d1381fd40dd189058207477
https://doi.org/10.1117/12.2018903
https://doi.org/10.1117/12.2018903
Publikováno v:
2012 IEEE National Aerospace and Electronics Conference (NAECON).
In this paper we describe preliminary efforts to extend previous gender classification experiments using feature histograms extracted from 3D point clouds of human subjects. The previous experiments used point clouds drawn from the Civilian American
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f030f995a6165f557f05d51b7dd4efd4
https://doi.org/10.1109/naecon.2012.6531043
https://doi.org/10.1109/naecon.2012.6531043
Publikováno v:
Evolutionary and Bio-Inspired Computation: Theory and Applications VI.
In this paper we extend a previous exploration of histogram features extracted from 3D point cloud images of human subjects for gender discrimination. Feature extraction used a collection of concentric cylinders to define volumes for counting 3D poin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a576cd22ab66e5e653ea71bf37e0a18
https://doi.org/10.1117/12.921880
https://doi.org/10.1117/12.921880