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Aitor Alvarez-Gila, Adrian Galdran, Estibaliz Garrote, & Joost Van de Weijer. (2019). Self-supervised blur detection from synthetically blurred scenes. IMAVIS - Image and Vision Computing, 92, 103804.
Abstract: Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
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Fahad Shahbaz Khan, Muhammad Anwer Rao, Joost Van de Weijer, Michael Felsberg, & J.Laaksonen. (2015). Deep semantic pyramids for human attributes and action recognition. In Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 (Vol. 9127, pp. 341–353). Springer International Publishing.
Abstract: Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.
Keywords: Action recognition; Human attributes; Semantic pyramids
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Carles Sanchez, F. Javier Sanchez, Antoni Rosell, & Debora Gil. (2012). An illumination model of the trachea appearance in videobronchoscopy images. In Image Analysis and Recognition (Vol. 7325, pp. 313–320). LNCS. Springer Berlin Heidelberg.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways. This imaging modality provides realistic images and allows non-invasive minimal intervention procedures. Tracheal procedures are routinary interventions that require assessment of the percentage of obstructed pathway for injury (stenosis) detection. Visual assessment in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error.
This paper introduces an automatic method for the estimation of steneosed trachea percentage reduction in videobronchoscopic images. We look for tracheal rings , whose deformation determines the degree of obstruction. For ring extraction , we present a ring detector based on an illumination and appearance model. This model allows us to parametrise the ring detection. Finally, we can infer optimal estimation parameters for any video resolution.
Keywords: Bronchoscopy, tracheal ring, stenosis assesment, trachea appearance model, segmentation
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Ole Larsen, Petia Radeva, & Enric Marti. (1995). Bounds on the optimal elasticity parameters for a snake. Image Analysis and Processing, , 37–42.
Abstract: This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds.
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Josep Llados, Enric Marti, & Jordi Regincos. (1993). Interpretación de diseños a mano alzada como técnica de entrada a un sistema CAD en un ámbito de arquitectura. In III National Conference on Computer Graphics. Granada.
Abstract: En los últimos años, se ha introducido ámpliamente el uso de los sistemas CAD en dominios relacionados con la arquitectura. Dichos sistemas CAD son muy útiles para el arquitecto en el diseño de planos de plantas de edificios. Sin embargo, la utilización eficiente de un CAD requiere un tiempo de aprendizaje, en especial, en la etapa de creación y edición del diseño. Además, una vez familiarizado con un CAD, el arquitecto debe adaptarse a la simbología que éste le permite que, en algunos casos puede ser poco flexible.Con esta motivación, se propone una técnica alternativa de entrada de documentos en sistemas CAD. Dicha técnica se basa en el diseño del plano sobre papel mediante un dibujo lineal hecho a mano alzada a modo de boceto e introducido mediante scanner. Una vez interpretado este dibujo inicial e introducido en el CAD, el arquitecto sólo deber hacer sobre éste los retoques finales del documento.El sistema de entrada propuesto se compone de dos módulos principales: En primer lugar, la extracción de características (puntos característicos, rectas y arcos) de la imagen obtenida mediante scanner. En dicho módulo se aplican principalmente técnicas de procesamiento de imágenes obteniendo como resultado una representaci¢n del dibujo de entrada basada en grafos de atributos. El objetivo del segundo módulo es el de encontrar y reconocer las entidades integrantes del documento (puertas, mesas, etc.) en base a una biblioteca de símbolos definida en el sistema CAD. La implementación de dicho módulo se basa en técnicas de isomorfismo de grafos.El sistema propone una alternativa que permita, mediante el diseño a mano alzada, la introducción de la informaci¢n m s significativa del plano de forma rápida, sencilla y estandarizada por parte del usuario.
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Enric Marti, J. Rocarias, A. Sanchez, Petia Radeva, Ricardo Toledo, & Jordi Vitria. (2006). Caronte: un gestor documental para asignaturas del EEES.
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Joan Serrat, & Antonio Lopez. (2006). Una experiencia de Enginyeria del Software amb ABP.
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Enric Marti, Debora Gil, & Carme Julia. (2005). Una experiència en PBL per a la docència de Gràfics per Computador.
Abstract: En aquest article es presenta una experiència en ABP feta el curs 2004-05 en Gràfics per Computador 2, assignatura optativa de 3er curs d’Enginyeria Informàtica impartida a l’ETSE. En l’article s’explica l’organització docent abans d’ABP, basada en classes magistrals. Després es mostra l’organització en ABP i es quantifica en ECTS l’esforç de l’alumne en ambdues organitzacions. Essent conscient del diferent interès de l’alumnat per l’assignatura, se’ls hi ofereix dos itineraris: el de classes magistrals i d’ABP. Es mostren alguns resultats dels alumnes d’ABP i també les primeres enquestes realitzades als alumnes. S’exposen les conclusions en el primer any de l’experiència, plantejant temes de discussió. S’ha procurat que la proposta no desbordi l’esforç del professorat. Per això s’ofereix el doble itinerari, per a canalitzar per ABP els alumnes més interessats i permetre a la resta que realitzin el curs amb l’organització clàsica de l’assignatura: classes magistrals de teoria, problemes i pràctiques.
Keywords: Aprenentatge Basat en Projectes; Aprenentatge Basat en Problemes; Problem Based Learning; ECTS; EEES; Computer Graphics; OpenGL.
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Juan J. Villanueva, Jordi Gonzalez, X. Varona, & Xavier Roca. (2002). Aspaces: Action Spaces for Recognition and Synthesis of Human Actions..
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J.A.Perez, Enric Marti, & Juan J.Villanueva. (1992). Interfase de Usuario de Entrada de Datos 3D en un CAD de Cartografía Urbana a partir de Pares Estereoscópicos. In II Congreso Español de Informática Gráfica (pp. 47–60).
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Frederic Sampedro, & Sergio Escalera. (2015). Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation. IETCV - IET Computer Vision, 9(3), 439–446.
Abstract: 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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Huamin Ren, Nattiya Kanhabua, Andreas Mogelmose, Weifeng Liu, Kaustubh Kulkarni, Sergio Escalera, et al. (2018). Back-dropout Transfer Learning for Action Recognition. IETCV - IET Computer Vision, 12(4), 484–491.
Abstract: Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.
Keywords: Learning (artificial intelligence); Pattern Recognition
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Mohamed Ilyes Lakhal, Hakan Çevikalp, Sergio Escalera, & Ferda Ofli. (2018). Recurrent Neural Networks for Remote Sensing Image Classification. IETCV - IET Computer Vision, 12(7), 1040–1045.
Abstract: 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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Meysam Madadi, Sergio Escalera, Xavier Baro, & Jordi Gonzalez. (2022). End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data. IETCV - IET Computer Vision, 16(1), 50–66.
Abstract: Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets.
Keywords: Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation
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Marc Oliu, Ciprian Corneanu, Kamal Nasrollahi, Olegs Nikisins, Sergio Escalera, Yunlian Sun, et al. (2016). Improved RGB-D-T based Face Recognition. BIO - IET Biometrics, 5(4), 297–303.
Abstract: Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.
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