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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  url
openurl 
  Title Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images Type Conference Article
  Year 2018 Publication International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing Abbreviated Journal  
  Volume 721 Issue Pages  
  Keywords  
  Abstract This paper proposes a novel approach to use cross-spectral

images to achieve a better performance with the proposed Adaptive Harris

corner detector comparing its obtained results with those achieved

with images of the visible spectra. The images of urban, field, old-building

and country category were used for the experiments, given the variety of

the textures present in these images, with which the complexity of the

proposal is much more challenging for its verification. It is a new scope,

which means improving the detection of characteristic points using crossspectral

images (NIR, G, B) and applying pruning techniques, the combination

of channels for this fusion is the one that generates the largest

variance based on the intensity of the merged pixels, therefore, it is that

which maximizes the entropy in the resulting Cross-spectral images.

Harris is one of the most widely used corner detection algorithm, so

any improvement in its efficiency is an important contribution in the

field of computer vision. The experiments conclude that the inclusion of

a (NIR) channel in the image as a result of the combination of the spectra,

greatly improves the corner detection due to better entropy of the

resulting image after the fusion, Therefore the fusion process applied to

the images improves the results obtained in subsequent processes such as

identification of objects or patterns, classification and/or segmentation.
 
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  ISSN ISBN Medium  
  Area (up) Expedition Conference  
  Notes 1 Approved no  
  Call Number gtsi @ user @ Serial 84  
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Author Xavier Soria; Angel D. Sappa openurl 
  Title Improving Edge Detection in RGB Images by Adding NIR Channel. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 266-273  
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  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 (up) Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 95  
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Author Xavier Soria; Angel D. Sappa; Riad Hammoud pdf  openurl
  Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. Type Journal Article
  Year 2018 Publication Abbreviated Journal  
  Volume Vol. 18 Issue Issue 7 Pages  
  Keywords  
  Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.  
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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 (up) Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 96  
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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 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  
  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 (up) Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 92  
Permanent link to this record
 

 
Author A. Amato; F. Lumbreras; Angel D. Sappa pdf  url
openurl 
  Title A general-purpose crowdsourcing platform for mobile devices 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 211-215  
  Keywords Crowdsourcing Platform, Mobile Crowdsourcing  
  Abstract This paper presents details of a general purpose micro-taskon-demand platform based on the crowdsourcing philosophy. This platformwas specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquityand iii) embedded sensors.The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks.Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task- solver).Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way.Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications.Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Lisbon, Portugal Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area (up) Expedition Conference Computer Vision Theory and Applications (VISAPP), 2014 International Conference on  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 25  
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Author P. Ricaurte; C. Chilán; C. A. Aguilera-Carrasco; B. X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  Title Performance Evaluation of Feature Point Descriptors in the Infrared Domain Type Conference Article
  Year 2014 Publication Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 Abbreviated Journal  
  Volume 1 Issue Pages 545 -550  
  Keywords Infrared Imaging, Feature Point Descriptors  
  Abstract This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.  
  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 (up) Expedition Conference 2014 International Conference on Computer Vision Theory and Applications (VISAPP)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 26  
Permanent link to this record
 

 
Author N. Onkarappa; Cristhian A. Aguilera; B. X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  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 (up) 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 Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Journal Abbreviated Journal  
  Volume Vol. 14 Issue Pages pp. 3690-3701  
  Keywords cross-spectral imaging; feature point descriptors  
  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
  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 (up) Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 28  
Permanent link to this record
 

 
Author Cristhian A. Aguilera; Angel D. Sappa; R. Toledo pdf  url
openurl 
  Title LGHD: A feature descriptor for matching across non-linear intensity variations Type Conference Article
  Year 2015 Publication IEEE International Conference on, Quebec City, QC, 2015 Abbreviated Journal  
  Volume Issue Pages 178 - 181  
  Keywords Feature descriptor, multi-modal, multispectral, NIR, LWIR  
  Abstract This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Quebec City, QC, Canada Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area (up) Expedition Conference 2015 IEEE International Conference on Image Processing (ICIP)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 40  
Permanent link to this record
 

 
Author M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé pdf  url
openurl 
  Title Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains Type Conference Article
  Year 2015 Publication Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 Abbreviated Journal  
  Volume Issue Pages 2488 - 2495  
  Keywords Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model  
  Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Hamburg, Germany Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area (up) Expedition Conference 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 41  
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