CIDIS - Publicaciones -- Query Results

	CIDIS - Publicaciones Home \| Show All \| Simple Search \| Advanced Search	Login Quick Search: Field: contains: ...
	11–20 of 73 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

<< 1 2 3 4 5 6 7 8 >>

List View

Citations

Details

	Records
	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				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	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
Permanent link to this record



	Author	Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo
	Title	Cross-Spectral Local Descriptors via Quadruplet Network			Type	Journal Article
	Year	2017	Publication	In Sensors Journal	Abbreviated Journal
	Volume	Vol. 17	Issue		Pages	pp. 873
	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	64
Permanent link to this record



	Author	N. Onkarappa; Cristhian A. Aguilera; B. X. Vintimilla; Angel D. Sappa
	Title	Cross-spectral Stereo Correspondence using Dense Flow Fields			Type	Conference Article
	Year	2014	Publication	Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014	Abbreviated Journal
	Volume	3	Issue		Pages	613 - 617
	Keywords	Cross-spectral Stereo Correspondence, Dense Optical Flow, Infrared and Visible Spectrum
	Abstract	This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	2014 International Conference on Computer Vision Theory and Applications (VISAPP)
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	27
Permanent link to this record



	Author	Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla
	Title	Cycle generative adversarial network: towards a low-cost vegetation index estimation			Type	Conference Article
	Year	2021	Publication	IEEE International Conference on Image Processing (ICIP 2021)	Abbreviated Journal
	Volume	2021-September	Issue		Pages	2783-2787
	Keywords	CyclicGAN, NDVI, near infrared spectra, instance normalization.
	Abstract	This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
	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	164
Permanent link to this record



	Author	Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa
	Title	Deep learning based camera pose estimation in multi-view environment.			Type	Conference Article
	Year	2018	Publication	14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018)	Abbreviated Journal
	Volume		Issue		Pages	224-228
	Keywords
	Abstract	This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
	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	93
Permanent link to this record



	Author	Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa.
	Title	Deep Learning based Corn Kernel Classification.			Type	Conference Article
	Year	2020	Publication	The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020)	Abbreviated Journal
	Volume	2020-June	Issue	9150684	Pages	294-302
	Keywords
	Abstract	This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	21607508	ISBN	978-172819360-1	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	124
Permanent link to this record



	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Deep Learning based Single Image Dehazing			Type	Conference Article
	Year	2018	Publication	14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images.
	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	83
Permanent link to this record



	Author	Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla
	Title	Deep learning-based vegetation index estimation			Type	Book Chapter
	Year	2021	Publication	Generative Adversarial Networks for Image-to-Image Translation Book.	Abbreviated Journal
	Volume	Chapter 9	Issue	Issue 2	Pages	205-232
	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	137
Permanent link to this record



	Author	Xavier Soria; Edgar Riba; Angel D. Sappa
	Title	Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection			Type	Conference Article
	Year	2020	Publication	2020 IEEE Winter Conference on Applications of Computer Vision (WACV)	Abbreviated Journal
	Volume		Issue	9093290	Pages	1912-1921
	Keywords
	Abstract	This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.
	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	978-172816553-0	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	126
Permanent link to this record