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 precious 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 precious 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",
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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 precious 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 precious 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.