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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Title |
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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Conference Article |
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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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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Title |
Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) |
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Conference Article |
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Year |
2016 |
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3rd International Conference on Maritime Technology and Engineering |
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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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Admin @ si @ GMS2016b |
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2817 |
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Author |
Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
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Title |
Learning local feature descriptors with triplets and shallow convolutional neural networks |
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2016 |
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27th British Machine Vision Conference |
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It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets. |
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York; UK; September 2016 |
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BMVC |
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ADAS; 600.086 |
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Admin @ si @ BRP2016 |
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2818 |
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Author |
Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci |
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Title |
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting |
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2016 |
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Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting |
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Elsevier |
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9780128110188 |
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MILAB |
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no |
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Admin @ si @ BZZ2016 |
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2821 |
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Author |
Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva |
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Title |
Learning the Lumen Border using a Convolutional Neural Networks classifier |
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Conference Article |
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2016 |
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19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop |
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IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows. |
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Athens; Greece; October 2016 |
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MICCAIW |
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MILAB; |
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Admin @ si @ MBB2016 |
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2822 |
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Author |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Improving Text Proposals for Scene Images with Fully Convolutional Networks |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art. |
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Cancun; Mexico; December 2016 |
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ICPRW |
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DAG; LAMP; 600.084 |
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Admin @ si @ BGN2016 |
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2823 |
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Author |
Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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Title |
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision |
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697-716 |
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Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos. |
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Amsterdam; The Netherlands; October 2016 |
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LNCS |
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ECCV |
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ADAS; 600.076; 600.085 |
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Admin @ si @ SGV2016 |
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2824 |
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Hugo Jair Escalante; Victor Ponce; Jun Wan; Michael A. Riegler; Baiyu Chen; Albert Clapes; Sergio Escalera; Isabelle Guyon; Xavier Baro; Pal Halvorsen; Henning Muller; Martha Larson |
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Title |
ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview |
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2016 |
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23rd International Conference on Pattern Recognition |
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This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks. |
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Cancun; Mexico; December 2016 |
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HuPBA; 602.143;MV |
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Admin @ si @ EPW2016 |
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2827 |
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Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
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Title |
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
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We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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Admin @ si @ CEG2016 |
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2829 |
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Marc Bolaños; Petia Radeva |
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Simultaneous Food Localization and Recognition |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
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Cancun; Mexico; December 2016 |
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ICPR |
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MILAB; no proj |
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Admin @ si @ BoR2016 |
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2834 |
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Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams |
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2016 |
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23rd International Conference on Pattern Recognition |
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Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. |
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Cancun; Mexico; December 2016 |
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MILAB |
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no |
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Admin @ si @ ADR2016d |
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2835 |
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Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Fusion of Classifier Predictions for Audio-Visual Emotion Recognition |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are
computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence
outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches. |
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Cancun; Mexico; December 2016 |
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ICPRW |
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HuPBA;MILAB; |
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Admin @ si @ NMN2016 |
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2839 |
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Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
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SASE: RGB-Depth Database for Human Head Pose Estimation |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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Admin @ si @ LEA2016a |
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2840 |
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Author |
Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari |
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Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring |
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Conference Article |
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2016 |
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9th Conference on Articulated Motion and Deformable Objects |
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Palma de Mallorca; Spain; July 2016 |
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AMDO |
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HuPBA;MILAB |
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Admin @ si @ RUE2016 |
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2846 |
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Author |
Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera |
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Title |
Spatiotemporal Facial Super-Pixels for Pain Detection |
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Conference Article |
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2016 |
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9th Conference on Articulated Motion and Deformable Objects |
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Facial images; Super-pixels; Spatiotemporal filters; Pain detection |
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Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
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Palma de Mallorca; Spain; July 2016 |
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HUPBA;MILAB |
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Admin @ si @ LNM2016 |
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2847 |
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