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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Dynamic Lexicon Generation for Natural Scene Images |
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Conference Article |
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2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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395-410 |
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scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN |
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Abstract |
Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge benet from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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DAG; 600.084 |
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Admin @ si @ PGR2016 |
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2825 |
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Author |
Dan Norton; Fernando Vilariño; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Memory Field – Creative Engagement in Digital Collections |
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2015 |
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Internet Librarian International Conference |
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“Memory Fields” is a trans-disciplinary project aiming at the (re)valorisation of digital collections.Its main deliverable is an interface for a dual screen installation, used to access and mix the public library digital collections. The collections being used in this case are a collection of digitised posters from the Spanish Civil War, belonging to the Arxiu General de Catalunya, and a collection of field recordings made by Dan Norton. The system generates visualisations, and the images and sounds are mixed together using narrative primitives of video dj. Users contribute to the digital collections by adding personal memories and observations. The comments and recollections appear as flowers growing in a “memory field” and memories remain public in a Twitter feed (@Memoryfields). |
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London; UK; October 2015 |
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ILI |
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MV;SIAI |
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Admin @ si @NVF2015 |
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2796 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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The Library Living Lab |
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2015 |
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Open Living Lab Days |
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Istanbul; Turkey; August 2015 |
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OLLD |
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MV; DAG;SIAI |
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Admin @ si @ViK2015 |
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2797 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres |
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2015 |
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The White Book on Public Library Network from Diputació de Barcelona |
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MV; DAG;SIAI |
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Admin @ si @VKC2015 |
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2798 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Computer Vision and Performing Arts |
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Conference Article |
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2015 |
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Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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KAMS |
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MV;SIAI |
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Admin @ si @Vil2015 |
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2799 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Memory Fields: DJs in the Library |
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Conference Article |
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2015 |
Publication |
21 st Symposium of Electronic Arts |
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Vancouver; Canada; August 2015 |
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ISEA |
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;SIAI |
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Admin @ si @VNF2015 |
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2800 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
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Conference Article |
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2016 |
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4S/EASST Conference |
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Barcelona; Spain; September 2016 |
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EASST |
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MV; 600.097;SIAI |
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Admin @ si @VNF2016 |
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2801 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Giving Value to digital collections in the Public Library |
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Conference Article |
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2016 |
Publication |
Librarian 2020 |
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Brussels; Belgium; October 2016 |
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LIB |
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MV; 600.097;SIAI |
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Admin @ si @Vil2016a |
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2802 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Living Lab approach for Citizen Science in Libraries |
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Conference Article |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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Admin @ si @ViK2016 |
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2804 |
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Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation |
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2016 |
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International Workshop on Poetry: Archives, Poetries and Receptions |
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Barcelona; Spain; October 2016 |
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POETRY |
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MV; 600.097;SIAI |
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Admin @ si @Vil2016b |
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2805 |
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Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Slanted Stixels: Representing San Francisco's Steepest Streets} |
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Conference Article |
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2017 |
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28th British Machine Vision Conference |
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In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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London; uk; September 2017 |
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BMVC |
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ADAS; 600.118 |
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ADAS @ adas @ HSE2017a |
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2945 |
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Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
LIUM-CVC Submissions for WMT17 Multimodal Translation Task |
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2017 |
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2nd Conference on Machine Translation |
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This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. |
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WMT |
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LAMP; 600.106; 600.120 |
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Admin @ si @ CAB2017 |
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3035 |
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Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Behavior Analysis; Personality Traits; First Impressions |
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This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MV; 600.063 |
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Admin @ si @ PCP2016 |
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2828 |
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Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
![download PDF file pdf](img/file_PDF.gif)
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Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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CCIA |
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OR;MV; |
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Admin @ si @ GMS2016 |
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2816 |
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Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
![download PDF file pdf](img/file_PDF.gif)
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Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) |
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2016 |
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3rd International Conference on Maritime Technology and Engineering |
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Abstract |
Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available. |
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Lisboa; Portugal; July 2016 |
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MARTECH |
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OR;MV; |
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no |
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Admin @ si @ GMS2016b |
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2817 |
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