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Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli |
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Title |
Recurrent Neural Networks for Remote Sensing Image Classification |
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Journal Article |
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Year |
2018 |
Publication |
IET Computer Vision |
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IETCV |
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12 |
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7 |
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1040 - 1045 |
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Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. |
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HUPBA; no proj |
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Admin @ si @ LÇE2018 |
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3119 |
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Author |
Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo |
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Title |
2D-to-3D Facial Expression Transfer |
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2018 |
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24th International Conference on Pattern Recognition |
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2008 - 2013 |
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Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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ICPR |
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MSIAU; 600.086; 600.130; 600.118 |
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no |
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Admin @ si @ RLM2018 |
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3232 |
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Author |
Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari |
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Title |
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context |
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2018 |
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IEEE Winter Applications of Computer Vision Workshops |
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28-36 |
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IEEE Winter Applications of Computer Vision Workshops |
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Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. |
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Lake Tahoe; USA; March 2018 |
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WACVW |
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HUPBA; 602.133 |
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Admin @ si @ JBA2018 |
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3121 |
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Author |
Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
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Title |
Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks |
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Miscellaneous |
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2018 |
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Arxiv |
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arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images.
In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts. |
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ISE; 600.098; 600.119 |
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no |
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Admin @ si @ CRY2018 |
Serial |
3550 |
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Author |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
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Title |
Residual Stacked RNNs for Action Recognition |
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Conference Article |
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Year |
2018 |
Publication |
9th International Workshop on Human Behavior Understanding |
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534-548 |
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Action recognition; Deep residual learning; Two-stream RNN |
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Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. |
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Munich; September 2018 |
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ECCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ LCE2018b |
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3206 |
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Author |
Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez |
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Title |
Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation |
Type |
Journal Article |
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Year |
2018 |
Publication |
Respiration |
Abbreviated Journal |
RES |
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Volume |
96 |
Issue |
6 |
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525-534 |
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Keywords |
Multidetector computed tomography; Bronchoscopy; Continuous positive airway pressure; Image enhancement; Virtual bronchoscopic navigation |
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Abstract
RATIONALE:
Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways.
OBJECTIVES:
To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration.
METHODS:
CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol.
MEASUREMENTS AND MAIN RESULTS:
Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001).
CONCLUSIONS:
CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions. |
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IAM; 600.145 |
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Admin @ si @ DGT2018 |
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3135 |
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Author |
Hugo Prol; Vincent Dumoulin; Luis Herranz |
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Title |
Cross-Modulation Networks for Few-Shot Learning |
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2018 |
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Arxiv |
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A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. |
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LAMP; 600.120 |
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Admin @ si @ PDH2018 |
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3248 |
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Author |
Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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Title |
System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture |
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Patent |
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2018 |
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US9946933B2 |
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US9946933B2 |
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A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. |
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ADAS; 600.118 |
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Admin @ si @ SGV2018 |
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3255 |
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Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
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Looking at People Special Issue |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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2-4 |
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141-143 |
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HUPBA; ISE; 600.119 |
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Admin @ si @ EGJ2018 |
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3093 |
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Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez |
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Polyp Detection Benchmark in Colonoscopy Videos using GTCreator: A Novel Fully Configurable Tool for Easy and Fast Annotation of Image Databases |
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2018 |
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32nd International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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CARS |
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ISE; MV; 600.119 |
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Admin @ si @ BHM2018 |
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3089 |
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Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas |
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Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic |
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2018 |
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Jornades Imatge i Recerca |
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DAG; 600.084; 600.135; 601.338; 600.121; 600.129 |
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Admin @ si @ GRB2018 |
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3173 |
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Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera |
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From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2373-2382 |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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HUPBA |
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Admin @ si @ |
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3116 |
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Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces |
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2018 |
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Technology Innovation Management Review |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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no |
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Call Number |
Admin @ si @ VKV2018a |
Serial |
3153 |
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Author |
Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio |
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Title |
Image-to-image translation for cross-domain disentanglement |
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Conference Article |
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Year |
2018 |
Publication |
32nd Annual Conference on Neural Information Processing Systems |
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Montreal; Canada; December 2018 |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ GWB2018 |
Serial |
3155 |
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Author |
Adrien Gaidon; Antonio Lopez; Florent Perronnin |
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Title |
The Reasonable Effectiveness of Synthetic Visual Data |
Type |
Journal Article |
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Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
126 |
Issue |
9 |
Pages |
899–901 |
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Notes |
ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ GLP2018 |
Serial |
3180 |
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| Permanent link to this record |
