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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic pdf  openurl
  Title Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles Type Journal Article
  Year 2016 Publication Journal of Automation and Control Engineering (JOACE) Abbreviated Journal  
  Volume Vol. 4 Issue Pages (down) pp. 430-436  
  Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System  
  Abstract Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.  
  Address  
  Corporate Author Thesis  
  Publisher 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  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 51  
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Author Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. pdf  openurl
  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 Issue Pages (down) 417-426  
  Keywords  
  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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  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 103  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  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 (down) 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.  
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  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 92  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title Vegetation Index Estimation from Monospectral Images Type Conference Article
  Year 2018 Publication 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science Abbreviated Journal  
  Volume 10882 Issue Pages (down) 353-362  
  Keywords  
  Abstract This paper proposes a novel approach to estimate Normalized

Difference Vegetation Index (NDVI) from just the red channel of

a RGB image. The NDVI index is defined as the ratio of the difference

of the red and infrared radiances over their sum. In other words, information

from the red channel of a RGB image and the corresponding

infrared spectral band are required for its computation. In the current

work the NDVI index is estimated just from the red channel by training a

Conditional Generative Adversarial Network (CGAN). The architecture

proposed for the generative network consists of a single level structure,

which combines at the final layer results from convolutional operations

together with the given red channel with Gaussian noise to enhance

details, resulting in a sharp NDVI image. Then, the discriminative model

estimates the probability that the NDVI generated index came from the

training dataset, rather than the index automatically generated. Experimental

results with a large set of real images are provided showing that

a Conditional GAN single level model represents an acceptable approach

to estimate NDVI index.
 
  Address  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 82  
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Author Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. pdf  url
openurl 
  Title Thermal Image Super-Resolution Challenge Results- PBVS 2022. Type Conference Article
  Year 2022 Publication Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. Abbreviated Journal CONFERENCE  
  Volume 2022-June Issue Pages (down) 349-357  
  Keywords  
  Abstract This paper presents results from the third Thermal Image

Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.

The challenge uses the same thermal image dataset as the

first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was

kept aside for testing. The evaluation tasks were to measure

the PSNR and SSIM between the SR image and the ground

truth (HR thermal noisy image downsampled by four), and

also to measure the PSNR and SSIM between the SR image

and the semi-registered HR image (acquired with another

camera). The results outperformed those from last year’s

challenge, improving both evaluation metrics. This year,

almost 100 teams participants registered for the challenge,

showing the community’s interest in this hot topic.
 
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 175  
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Author Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos pdf  url
openurl 
  Title A predictive model for human activity recognition by observing actions and context Type Conference Article
  Year 2015 Publication ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 Abbreviated Journal  
  Volume Issue Pages (down) 323 - 333  
  Keywords Edge width, Image blu,r Defocus map, Edge model  
  Abstract This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 43  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  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 (down) 287-297  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 66  
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Author Miguel Realpe; Boris X. Vintimilla; L. Vlacic pdf  openurl
  Title Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. Type Conference Article
  Year 2015 Publication IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. Abbreviated Journal  
  Volume Issue Pages (down) 253-258  
  Keywords  
  Abstract Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 37  
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Author Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  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 (down) 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.  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 93  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Infrared Image Colorization based on a Triplet DCGAN Architecture. Type Conference Article
  Year 2017 Publication 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) Abbreviated Journal  
  Volume 2017-July Issue Pages (down) 212-217  
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  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 62  
Permanent link to this record
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