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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-class Binary Object Categorization using Blurred Shape Models |
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Conference Article |
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Year |
2007 |
Publication |
Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern |
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4756 |
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773–782 |
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978-3-540-76724-4 |
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CIARP |
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MILAB; DAG;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EFP2007 |
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911 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
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Conference Article |
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Year |
2009 |
Publication |
15th International Conference on Image Analysis and Processing |
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Volume |
5716 |
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Pages |
1005–1014 |
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Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Salerno, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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no |
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BCNPCL @ bcnpcl @ EFP2009c |
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1186 |
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Author |
Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes |
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Book Chapter |
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Year |
2011 |
Publication |
Innovations in Intelligent Image Analysis |
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Volume |
339 |
Issue |
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Pages |
7-29 |
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A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images. |
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Springer Berlin Heidelberg |
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Berlin |
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H. Kawasnicka; L.Jain |
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1860-949X |
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978-3-642-17933-4 |
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MILAB;HuPBA |
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no |
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Admin @ si @ ETP2011 |
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1746 |
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Author |
Francesco Ciompi |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound |
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2012 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
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In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Petia Radeva;Oriol Pujol |
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MILAB |
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no |
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Call Number |
Admin @ si @ Cio2012 |
Serial |
2146 |
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Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Class Multi-Scale Stacked Sequential Learning |
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Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
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Volume |
6713 |
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197-206 |
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Napoles, Italy |
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Springer |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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MCS |
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HuPBA;MILAB |
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no |
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Admin @ si @ PEP2011b |
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1772 |
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Author |
Juanjo Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-class structural damage segmentation using fully convolutional networks |
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Journal Article |
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Year |
2019 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
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Volume |
112 |
Issue |
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Pages |
103121 |
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Keywords |
Bridge damage detection; Deep learning; Semantic segmentation |
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Abstract |
Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%. |
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HuPBA; no proj |
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no |
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Admin @ si @ RKL2019 |
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3315 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams |
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Journal Article |
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Year |
2016 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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149 |
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146-156 |
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Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. |
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MILAB; |
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no |
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Admin @ si @ ADR2016b |
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2742 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos |
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Miscellaneous |
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2015 |
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Arxiv |
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Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness. |
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MILAB |
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no |
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Admin @ si @ ADR2015b |
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2713 |
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Author |
Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Illuminant Estimation with Conditional Random Fields |
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Journal Article |
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2014 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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23 |
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1 |
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83-95 |
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color constancy; CRF; multi-illuminant |
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Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. |
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1057-7149 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ BRW2014 |
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2451 |
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Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Image Super-Resolution for Thermal Images |
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Conference Article |
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2022 |
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17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) |
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4 |
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635-642 |
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Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block |
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This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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Online; Feb 6-8, 2022 |
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VISAPP |
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MSIAU; 601.349 |
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Admin @ si @ RSV2022a |
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3690 |
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Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Modal Aerial View Image Challenge: Translation From Synthetic Aperture Radar to Electro-Optical Domain Results-PBVS 2023 |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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515-523 |
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This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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MSIAU |
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no |
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Admin @ si @ LNS2023a |
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3913 |
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Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022 |
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Conference Article |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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350-358 |
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This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
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New Orleans; USA; June 2022 |
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Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
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Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023 |
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2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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412-421 |
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This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement. |
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Vancouver; Canada; June 2023 |
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Admin @ si @ LNS2023b |
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3915 |
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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
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2018 |
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Entropy |
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ENTROPY |
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20 |
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11 |
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809 |
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hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
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HUPBA; no proj |
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Admin @ si @ RKE2018 |
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Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen |
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Multi-modal Deep Learning Approach for Flood Detection |
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2017 |
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MediaEval Benchmarking Initiative for Multimedia Evaluation |
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In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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Dublin; Ireland; September 2017 |
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MediaEval |
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LAMP; 600.084; 600.109; 600.120 |
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Admin @ si @ LWB2017a |
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2974 |
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