Automatic Classification of Semi Precious Rocks

Irving Alberto Cruz Matías, Constantino Pearl, Andrea Puente

Research output: Contribution to conferencePaper

Abstract

This project pretends to reach a successful model of automatic classification of semi pre-
cious rocks through Machine Learning techniques in TensorFlow and OpenCV image processing algorithms.
Visual characteristics and image processing algorithms were proposed to correctly seg-
ment the objects and identify their key features. After that, different TensorFlow models
(DenseNet, NasNet, etc.) were tested to measure their accuracy and select the best method based on a comparative between performance and precision.
The results from the experiments were assessed and the definite algorithm was construct-
ed. The algorithm runs on an Amazon Web Services instance, which is accessed by a mobile application. Results, scope and project limitations are discussed at the end of this work, as well as future approaches.
Original languageEnglish
Publication statusIn preparation - 2019
Event2020 Winter Conference on Applications of Computer Vision - Snowmass Village, Colorado, United States
Duration: 2 Mar 20205 Mar 2020
http://wacv20.wacv.net/

Conference

Conference2020 Winter Conference on Applications of Computer Vision
Abbreviated titleWACV20
CountryUnited States
CityColorado
Period2/3/205/3/20
Internet address

Fingerprint

Rocks
Image processing
Web services
Learning systems
Experiments

Cite this

Cruz Matías, I. A., Pearl, C., & Puente, A. (2019). Automatic Classification of Semi Precious Rocks. Paper presented at 2020 Winter Conference on Applications of Computer Vision, Colorado, United States.
Cruz Matías, Irving Alberto ; Pearl, Constantino ; Puente, Andrea. / Automatic Classification of Semi Precious Rocks. Paper presented at 2020 Winter Conference on Applications of Computer Vision, Colorado, United States.
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title = "Automatic Classification of Semi Precious Rocks",
abstract = "This project pretends to reach a successful model of automatic classification of semi pre-cious rocks through Machine Learning techniques in TensorFlow and OpenCV image processing algorithms.Visual characteristics and image processing algorithms were proposed to correctly seg-ment the objects and identify their key features. After that, different TensorFlow models(DenseNet, NasNet, etc.) were tested to measure their accuracy and select the best method based on a comparative between performance and precision.The results from the experiments were assessed and the definite algorithm was construct-ed. The algorithm runs on an Amazon Web Services instance, which is accessed by a mobile application. Results, scope and project limitations are discussed at the end of this work, as well as future approaches.",
author = "{Cruz Mat{\'i}as}, {Irving Alberto} and Constantino Pearl and Andrea Puente",
year = "2019",
language = "English",
note = "2020 Winter Conference on Applications of Computer Vision, WACV20 ; Conference date: 02-03-2020 Through 05-03-2020",
url = "http://wacv20.wacv.net/",

}

Cruz Matías, IA, Pearl, C & Puente, A 2019, 'Automatic Classification of Semi Precious Rocks' Paper presented at 2020 Winter Conference on Applications of Computer Vision, Colorado, United States, 2/3/20 - 5/3/20, .

Automatic Classification of Semi Precious Rocks. / Cruz Matías, Irving Alberto; Pearl, Constantino; Puente, Andrea.

2019. Paper presented at 2020 Winter Conference on Applications of Computer Vision, Colorado, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Automatic Classification of Semi Precious Rocks

AU - Cruz Matías, Irving Alberto

AU - Pearl, Constantino

AU - Puente, Andrea

PY - 2019

Y1 - 2019

N2 - This project pretends to reach a successful model of automatic classification of semi pre-cious rocks through Machine Learning techniques in TensorFlow and OpenCV image processing algorithms.Visual characteristics and image processing algorithms were proposed to correctly seg-ment the objects and identify their key features. After that, different TensorFlow models(DenseNet, NasNet, etc.) were tested to measure their accuracy and select the best method based on a comparative between performance and precision.The results from the experiments were assessed and the definite algorithm was construct-ed. The algorithm runs on an Amazon Web Services instance, which is accessed by a mobile application. Results, scope and project limitations are discussed at the end of this work, as well as future approaches.

AB - This project pretends to reach a successful model of automatic classification of semi pre-cious rocks through Machine Learning techniques in TensorFlow and OpenCV image processing algorithms.Visual characteristics and image processing algorithms were proposed to correctly seg-ment the objects and identify their key features. After that, different TensorFlow models(DenseNet, NasNet, etc.) were tested to measure their accuracy and select the best method based on a comparative between performance and precision.The results from the experiments were assessed and the definite algorithm was construct-ed. The algorithm runs on an Amazon Web Services instance, which is accessed by a mobile application. Results, scope and project limitations are discussed at the end of this work, as well as future approaches.

M3 - Paper

ER -

Cruz Matías IA, Pearl C, Puente A. Automatic Classification of Semi Precious Rocks. 2019. Paper presented at 2020 Winter Conference on Applications of Computer Vision, Colorado, United States.