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Author
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin
Title
Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models
Type
Conference Article
Year
2024
Publication
Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS)
Abbreviated Journal
Volume
Issue
Pages
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
Approved
no
Call Number
cidis @ cidis @
Serial
245
Permanent link to this record
Author
Patricia L. Suarez
;
Angel D. Sappa
;
Boris X. Vintimilla
Title
Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture
Type
Conference Article
Year
2017
Publication
19th International Conference on Image Analysis and Processing.
Abbreviated Journal
Volume
Issue
Pages
287-297
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
Approved
no
Call Number
gtsi @ user @
Serial
66
Permanent link to this record
Author
Patricia L. Suarez
;
Angel D. Sappa
;
Boris X. Vintimilla
Title
Cross-spectral image dehaze through a dense stacked conditional GAN based approach.
Type
Conference Article
Year
2018
Publication
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018)
Abbreviated Journal
Volume
Issue
Pages
358-364
Keywords
Abstract
This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
Address
Corporate Author
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Publisher
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Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
Approved
no
Call Number
gtsi @ user @
Serial
92
Permanent link to this record
Author
Patricia L. Suarez
;
Angel D. Sappa
;
Boris X. Vintimilla
Title
Cross-spectral Image Patch Similarity using Convolutional Neural Network
Type
Conference Article
Year
2017
Publication
2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM)
Abbreviated Journal
Volume
Issue
Pages
1-5
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
Approved
no
Call Number
cidis @ cidis @
Serial
57
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