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Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |
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Title |
Special Issue on Autonomous Driving an Driver Assistance Systems |
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Journal Article |
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2017 |
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In Robotics and Autonomous Systems Journal |
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Vol. 91 |
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pp. 208-209 |
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gtsi @ user @ |
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65 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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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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gtsi @ user @ |
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89 |
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Author |
Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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Journal Article |
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2017 |
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In Sensors Journal |
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Vol. 17 |
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pp. 873 |
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gtsi @ user @ |
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64 |
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Author |
M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé |
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains |
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2015 |
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Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 |
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2488 - 2495 |
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Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model |
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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. |
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IEEE |
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Hamburg, Germany |
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English |
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English |
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2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
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cidis @ cidis @ |
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41 |
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Author |
Angel D. Sappa. |
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Title |
ICT Applications for Smart Cities |
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2022 |
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Intelligent Systems Reference Library |
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BOOK |
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224 |
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cidis @ cidis @ |
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198 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
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Conference Article |
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2018 |
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International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
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721 |
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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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gtsi @ user @ |
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84 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Title |
Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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Journal Article |
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Year |
2017 |
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Lecture Notes in Computer Science |
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Vol. 10125 LNCS |
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pp. 467-475 |
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gtsi @ user @ |
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63 |
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Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
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Title |
Tiny and Efficient Model for the Edge Detection Generalization |
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Conference Article |
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2023 |
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Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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1356 - 1365 |
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cidis @ cidis @ |
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229 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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cidis @ cidis @ |
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181 |
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Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla |
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Title |
Human Pose Estimation through A Novel Multi-View Scheme |
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Conference Article |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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5 |
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855-862 |
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Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach |
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This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations. |
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yes |
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cidis @ cidis @ |
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169 |
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