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| Author | Gioacchino Vino; Angel Sappa | ||||
Title  |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach | Type | Conference Article | ||
| Year | 2013 | Publication | 10th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 7950 | Issue | Pages | 354-363 | |
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| Abstract | This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. | ||||
| Address | Póvoa de Varzim; Portugal; June 2013 | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-39093-7 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | ADAS; 600.055 | Approved | no | ||
| Call Number | Admin @ si @ ViS2013 | Serial | 2562 | ||
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| Author | Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski | ||||
| Title |
Revisiting Supervision for Continual Representation Learning | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning. | ||||
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| Notes | xxx | Approved | no | ||
| Call Number | Admin @ si @ MCT2023 | Serial | 4013 | ||
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| Author | Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera | ||||
| Title |
RGB-D Segmentation of Poultry Entrails | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
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| Abstract | Best commercial paper award. | ||||
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| Area | Expedition | Conference | AMDO | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PJM2016 | Serial | 2848 | ||
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| Author | Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera | ||||
| Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey | Type | Journal Article | ||
| Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 171 | Issue | Pages | 118-139 | |
| Keywords | Human motion recognition; RGB-D data; Deep learning; Survey | ||||
| Abstract | Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ WLO2018 | Serial | 3123 | ||
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| Author | Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo | ||||
| Title |
RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
| Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Multispectral Imaging; Free Sensor Model; Neural Network | ||||
| Abstract | This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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| Address | Porto; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | ADAS; MSIAU; 600.118; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ ASS2017 | Serial | 2918 | ||
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| Author | Antonio Lopez | ||||
| Title |
Ridge/Valley-like structures: Creases, separatrices and drainage patterns | Type | Miscellaneous | ||
| Year | 1997 | Publication | Computer vision on–line | Abbreviated Journal | |
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| Address | CVC | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ Lop1997 | Serial | 488 | ||
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| Author | Antonio Lopez; Joan Serrat | ||||
| Title |
Ridge/Valley-like structures: Creases, separatrices and drainage patterns | Type | Report | ||
| Year | 1997 | Publication | CVC Technical Report #21 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LoS1997 | Serial | 524 | ||
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| Author | Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf | ||||
| Title |
Ridgeness for Detecting Lane Markings | Type | Miscellaneous | ||
| Year | 2005 | Publication | 2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | lane markings | ||||
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| Address | Hamburg (Germany) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LSS2005 | Serial | 548 | ||
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| Author | Antonio Lopez; Joan Serrat | ||||
| Title |
Ridges and Valleys in Image Analysis | Type | Report | ||
| Year | 1998 | Publication | CVC Technical Report #22 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LoS1998 | Serial | 533 | ||
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| Author | A. Pujol; Antonio Lopez; Jose Luis Alba; Juan J. Villanueva | ||||
| Title |
Ridges, Valleys and Hausdorff Based Similarity Measures for Face Detection and Matching | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems (PRIS’2001), ICEIS Press, Ana Fred and Anil K. Jain (Eds), pgs.80–90 | Abbreviated Journal | |
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| Address | Setubal (Portugal) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ PLA2001 | Serial | 486 | ||
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| Author | Fadi Dornaika; Angel Sappa | ||||
| Title |
Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models | Type | Journal Article | ||
| Year | 2007 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 28 | Issue | 15 | Pages | 2116-2126 |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ DoS2007c | Serial | 877 | ||
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| Author | Fadi Dornaika; Angel Sappa | ||||
| Title |
Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models | Type | Book Chapter | ||
| Year | 2006 | Publication | 8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684 | Abbreviated Journal | |
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| Address | Antwerp (Belgium) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ DoS2006c | Serial | 689 | ||
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| Author | David Lloret; Antonio Lopez; Joan Serrat | ||||
| Title |
Rigid Registration of CT and MR volumes based on Rothes creases | Type | Miscellaneous | ||
| Year | 1997 | Publication | (SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis. Eds.CVC,pgs.1–6 | Abbreviated Journal | |
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| Address | Barcelona (Spain) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LLS1997 | Serial | 490 | ||
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| Author | Laura Lopez-Fuentes; Claudio Rossi; Harald Skinnemoen | ||||
| Title |
River segmentation for flood monitoring | Type | Conference Article | ||
| Year | 2017 | Publication | Data Science for Emergency Management at Big Data 2017 | Abbreviated Journal | |
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| Abstract | Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation. | ||||
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| Notes | LAMP; 600.084; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LRS2017 | Serial | 3078 | ||
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| Author | Angel Sappa; Rosa Herrero; Fadi Dornaika; David Geronimo; Antonio Lopez | ||||
| Title |
Road Approximation in Euclidean and v-Disparity Space: A Comparative Study | Type | Conference Article | ||
| Year | 2007 | Publication | Computer Aided Systems Theory, | Abbreviated Journal | |
| Volume | 4739 | Issue | Pages | 1105–1112 | |
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| Abstract | This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge. |
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| Address | Las Palmas de Gran Canaria (Spain) | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | EUROCAST | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SHD2007b | Serial | 917 | ||
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