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Author Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo
Title Learning crossspectral similarity measures with deep convolutional neural networks Type Conference Article
Year 2016 Publication IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Abbreviated Journal
Volume (down) Issue Pages 267-275
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Abstract The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.
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Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 48
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Author Monica Villavicencio; Alain Abran
Title Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs Type Conference Article
Year 2011 Publication Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement Abbreviated Journal
Volume (down) Issue Pages 239-244
Keywords measurement; software engineering; higher education
Abstract In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement.
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Publisher IEEE Place of Publication Editor
Language English Summary Language English Original Title
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Call Number gtsi @ user @ Serial 68
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Author Angely Oyola; Dennis G. Romero; Boris X. Vintimilla
Title A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER) Type Conference Article
Year 2017 Publication The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) Abbreviated Journal
Volume (down) Issue Pages 131-135
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Call Number cidis @ cidis @ Serial 55
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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 (down) Issue Pages 1-5
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Call Number cidis @ cidis @ Serial 57
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Author Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa
Title A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers Type Conference Article
Year 2017 Publication 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) Abbreviated Journal
Volume (down) Issue Pages 1-6
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Call Number cidis @ cidis @ Serial 60
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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 (down) Issue Pages 224-228
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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.
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Call Number gtsi @ user @ Serial 93
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Author Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa
Title Detection of utility poles from noisy Point Cloud Data in Urban environments. Type Conference Article
Year 2018 Publication Artificial Intelligence and Cloud Computing Conference (AICCC 2018) Abbreviated Journal
Volume (down) Issue Pages 53-57
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Abstract In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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Call Number gtsi @ user @ Serial 94
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Author Marjorie Chalen; Boris X. Vintimilla
Title Towards Action Prediction Applying Deep Learning Type Journal Article
Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal
Volume (down) Issue Pages pp. 1-3
Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.
Abstract Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
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Call Number cidis @ cidis @ Serial 129
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Author Armin Mehri; Angel D. Sappa
Title Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples Type Conference Article
Year 2019 Publication Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States Abbreviated Journal
Volume (down) Issue Pages 971-979
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Abstract This paper presents a novel approach for colorizing

near infrared (NIR) images. The approach is based on

image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored

networks that require less computation times, converge

faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation

metrics—and qualitatively evaluated showing considerable

improvements with respect to the state of the art
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Call Number gtsi @ user @ Serial 105
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Author Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla.
Title Thermal Image SuperResolution through Deep Convolutional Neural Network. Type Conference Article
Year 2019 Publication 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá Abbreviated Journal
Volume (down) Issue Pages 417-426
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Abstract Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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Call Number gtsi @ user @ Serial 103
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