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Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera |
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Title |
The AutoML challenge on codalab |
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Conference Article |
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2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MILAB |
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no |
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Admin @ si @ GBC2015b |
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2650 |
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Author |
Debora Gil; David Roche; Agnes Borras; Jesus Giraldo |
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Title |
Terminating Evolutionary Algorithms at their Steady State |
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Journal Article |
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2015 |
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Computational Optimization and Applications |
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COA |
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61 |
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2 |
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489-515 |
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Evolutionary algorithms; Termination condition; Steady state; Differential evolution |
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Assessing the reliability of termination conditions for evolutionary algorithms (EAs) is of prime importance. An erroneous or weak stop criterion can negatively affect both the computational effort and the final result. We introduce a statistical framework for assessing whether a termination condition is able to stop an EA at its steady state, so that its results can not be improved anymore. We use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in decision variable space. Our framework is analyzed across 24 benchmark test functions and two standard termination criteria based on function fitness value in objective function space and EA population decision variable space distribution for the differential evolution (DE) paradigm. Results validate our framework as a powerful tool for determining the capability of a measure for terminating EA and the results also identify the decision variable space distribution as the best-suited for accurately terminating DE in real-world applications. |
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Springer US |
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0926-6003 |
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IAM; 600.044; 605.203; 600.060; 600.075 |
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Admin @ si @ GRB2015 |
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2560 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
Synthetic sequences and ground-truth flow field generation for algorithm validation |
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Journal Article |
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Year |
2015 |
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Multimedia Tools and Applications |
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MTAP |
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74 |
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9 |
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3121-3135 |
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Ground-truth optical flow; Synthetic sequence; Algorithm validation |
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Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. |
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Springer US |
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1380-7501 |
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ADAS; 600.055; 601.215; 600.076 |
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Admin @ si @ OnS2014b |
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2472 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
150 |
Issue |
A |
Pages |
147-154 |
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Keywords |
document image analysis; stochastic context-free grammars; text classication features |
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In this paper we dene a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classication features are used to perform an initial classication of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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DAG; 601.158; 600.077; 600.061 |
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no |
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Admin @ si @ ACS2015 |
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2531 |
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Author |
Manuel Graña; Bogdan Raducanu |
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Title |
Special Issue on Bioinspired and knowledge based techniques and applications |
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Journal Article |
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Year |
2015 |
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Neurocomputing |
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NEUCOM |
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1-3 |
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LAMP; |
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Admin @ si @ GrR2015 |
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2598 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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Year |
2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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ACDC |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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no |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini |
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Title |
Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition |
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Conference Article |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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88-95 |
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Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ INB2015 |
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2654 |
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Author |
Frederic Sampedro; Sergio Escalera |
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Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation |
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Journal Article |
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2015 |
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IET Computer Vision |
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IETCV |
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9 |
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3 |
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439 - 446 |
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In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. |
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1751-9632 |
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HuPBA;MILAB |
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no |
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Admin @ si @ SaE2015 |
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2551 |
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Author |
Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
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Title |
Sparse Radial Sampling LBP for Writer Identification |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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716-720 |
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In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077 |
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Admin @ si @ NBL2015 |
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2692 |
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M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination |
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Conference Article |
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2015 |
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IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 |
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4169 - 4172 |
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This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. |
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Milan; Italy; July 2015 |
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IGARSS |
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LAMP; 600.079;MILAB |
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no |
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Admin @ si @ CRG2015 |
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2724 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
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Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
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2015 |
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2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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503-515 |
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Scene reconstruction; Point cloud; Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Lisboa; Portugal; November 2015 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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Admin @ si @ OSS2015a |
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2662 |
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E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
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Regularized Clustering for Egocentric Video Segmentation |
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2015 |
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Pattern Recognition and Image Analysis |
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327-336 |
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Temporal video segmentation ; Egocentric videos ; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Springer International Publishing |
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LNCS |
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978-3-319-19390-8 |
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MILAB |
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no |
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Admin @ si @TDB2015a |
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2781 |
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Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez |
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Recognizing Actions through Action-specific Person Detection |
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2015 |
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IEEE Transactions on Image Processing |
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TIP |
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24 |
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11 |
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4422-4432 |
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Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations. |
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1057-7149 |
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ADAS; LAMP; 600.076; 600.079 |
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no |
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Call Number |
Admin @ si @ KXR2015 |
Serial |
2668 |
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Author |
Eduardo Tusa; Arash Akbarinia; Raquel Gil Rodriguez; Corina Barbalata |
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Title |
Real-Time Face Detection and Tracking Utilising OpenMP and ROS |
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Conference Article |
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Year |
2015 |
Publication |
3rd Asia-Pacific Conference on Computer Aided System Engineering |
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179 - 184 |
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RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP |
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Abstract |
The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in
low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction. |
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Quito; Ecuador; July 2015 |
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APCASE |
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Notes |
NEUROBIT |
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no |
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Call Number |
Admin @ si @ TAG2015 |
Serial |
2659 |
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Author |
Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
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Title |
R-clustering for egocentric video segmentation |
Type |
Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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327-336 |
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Keywords |
Temporal video segmentation; Egocentric videos; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Santiago de Compostela; España; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19389-2 |
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IbPRIA |
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Notes |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ TDB2015 |
Serial |
2597 |
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