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Author Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa
Title Melamine faced panels defect classification beyond the visible spectrum. Type Journal Article
Year 2018 Publication In Sensors 2018 Abbreviated Journal
Volume Vol. 11 Issue Issue 11 Pages
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Abstract In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond

the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
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Call Number gtsi @ user @ Serial 89
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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
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Call Number gtsi @ user @ Serial 64
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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.
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Publisher IEEE Place of Publication Editor
Language English Summary Language English Original Title
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Area Expedition Conference 2014 International Conference on Computer Vision Theory and Applications (VISAPP)
Notes Approved no
Call Number cidis @ cidis @ Serial 27
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Author Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa
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.
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Call Number cidis @ cidis @ Serial 28
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Author Mildred Cruz; Cristhian A. Aguilera; Boris X. Vintimilla; Ricardo Toledo; Ángel D. Sappa
Title Cross-spectral image registration and fusion: an evaluation study Type Conference Article
Year 2015 Publication 2nd International Conference on Machine Vision and Machine Learning Abbreviated Journal
Volume 331 Issue Pages
Keywords multispectral imaging; image registration; data fusion; infrared and visible spectra
Abstract This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
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Publisher Computer Vision Center Place of Publication Barcelona, Spain Editor
Language English Summary Language English Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 35
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Author Cristhian A. Aguilera; Angel D. Sappa; R. Toledo
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.
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Publisher IEEE Place of Publication Quebec City, QC, Canada Editor
Language English Summary Language English Original Title
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Area Expedition Conference 2015 IEEE International Conference on Image Processing (ICIP)
Notes Approved no
Call Number cidis @ cidis @ Serial 40
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Author Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa
Title A visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
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Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 44
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Author Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla
Title Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study Type Journal Article
Year 2016 Publication Sensors Journal Abbreviated Journal
Volume Vol. 16 Issue Pages pp. 1-15
Keywords image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
Abstract This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR).
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Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 47
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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 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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Call Number cidis @ cidis @ Serial 48
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Author Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo
Title Monocular visual odometry: a cross-spectral image fusion based approach Type Journal Article
Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal
Volume Vol. 86 Issue Pages pp. 26-36
Keywords Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion
Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
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Language Enlgish Summary Language English Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 54
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