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Mohammed Al Rawi and Ernest Valveny. 2019. Compact and Efficient Multitask Learning in Vision, Language and Speech. IEEE International Conference on Computer Vision Workshops.2933–2942.
Abstract: Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains.
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Anjan Dutta, Umapada Pal and Josep Llados. 2016. Compact Correlated Features for Writer Independent Signature Verification. 23rd International Conference on Pattern Recognition.
Abstract: This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.
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Salim Jouili, Salvatore Tabbone and Ernest Valveny. 2010. Comparing Graph Similarity Measures for Graphical Recognition. Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers. Springer Berlin Heidelberg, 37–48. (LNCS.)
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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S.Jouili, Salvatore Tabbone and Ernest Valveny. 2009. Comparing Graph Similarity Measures for Graphical Recognition. 8th IAPR International Workshop on Graphics Recognition. Springer. (LNCS.)
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Miquel Ferrer, Ernest Valveny and F. Serratosa. 2007. Comparison Between two Spectral-based Methods for Median Graph Computation. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478(2):580–587.
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Antonio Lopez, Atsushi Imiya, Tomas Pajdla and Jose Manuel Alvarez. Computer Vision in Vehicle Technology: Land, Sea & Air.
Abstract: A unified view of the use of computer vision technology for different types of vehicles
Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).
The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed.
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Debora Gil, Jordi Gonzalez and Gemma Sanchez, eds. 2007. Computer Vision: Advances in Research and Development. Bellaterra (Spain), UAB. (2.)
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Josep Llados. 2006. Computer Vision: Progress of Research and Development.
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