| |
Records |
Links |
|
Author |
Angel D. Sappa, Spencer Low, Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich |
|
| |
Title |
Multi-modal Aerial View Image Challenge: Sensor Domain Translation |
Type |
Conference Article |
| |
Year |
2024 |
Publication |
Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
235 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Suarez & Angel D. Sappa |
|
| |
Title |
Haze-Free Imaging through Haze-Aware Transformer Adaptations |
Type |
Conference Article |
| |
Year |
2024 |
Publication |
In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
236 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Suarez, Angel D. Sappa |
|
| |
Title |
A Generative Model for Guided Thermal Image Super-Resolution |
Type |
Conference Article |
| |
Year |
2024 |
Publication |
In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
240 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira |
|
| |
Title |
Incremental Texture Mapping for Autonomous Driving |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Robotics and Autonomous Systems Journal |
Abbreviated Journal |
|
|
| |
Volume |
Vol. 84 |
Issue |
|
Pages |
pp. 113-128 |
|
| |
Keywords |
Scene reconstruction, Autonomous driving, Texture mapping |
|
| |
Abstract |
Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
|
| |
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 |
50 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
|
| |
Title |
Image patch similarity through a meta-learning metric based approach |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
511-517 |
|
| |
Keywords |
|
|
| |
Abstract |
Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results
have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results. |
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
115 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |
|
| |
Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Sensors 2020 |
Abbreviated Journal |
|
|
| |
Volume |
Vol. 2020-June |
Issue |
11 |
Pages |
pp. 1-13 |
|
| |
Keywords |
stereo matching; deep learning; embedded GPU |
|
| |
Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
|
| |
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 |
14248220 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
132 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
Issue |
|
Pages |
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 |
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
103 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé |
|
| |
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 |
|
Expedition |
|
Conference |
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
41 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
|
| |
Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
Abbreviated Journal |
|
|
| |
Volume |
417 |
Issue |
|
Pages |
503-515 |
|
| |
Keywords |
Scene reconstruction, Point cloud, Autonomous vehicles |
|
| |
Abstract |
In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer International Publishing Switzerland 2016 |
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 |
Second Iberian Robotics Conference |
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
45 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
Keywords |
|
|
| |
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. |
|
| |
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
89 |
|
| Permanent link to this record |
