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Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
RoadText-1K: Text Detection and Recognition Dataset for Driving Videos |
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Conference Article |
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Year |
2020 |
Publication |
IEEE International Conference on Robotics and Automation |
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Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k |
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Paris; Francia; ??? |
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ICRA |
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Notes |
DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ RMG2020 |
Serial |
3400 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Title |
Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph |
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Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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346–353 |
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Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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DAG |
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DAG @ dag @ FVS2009c |
Serial |
1174 |
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Author |
Albert Gordo; Ernest Valveny |
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Title |
The diagonal split: A pre-segmentation step for page layout analysis & classification |
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Conference Article |
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2009 |
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4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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290–297 |
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Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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DAG |
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DAG @ dag @ Gov2009b |
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1176 |
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Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
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Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
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Conference Article |
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Year |
2018 |
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14th Asian Conference on Computer Vision |
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In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
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Perth; Australia; December 2018 |
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ACCV |
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DAG; 600.097; 600.121; 600.129 |
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Call Number |
Admin @ si @ DDG2018a |
Serial |
3151 |
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Author |
Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Title |
Monitoring neuromotricity on-line: a cloud computing approach |
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Conference Article |
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Year |
2015 |
Publication |
17th Conference of the International Graphonomics Society IGS2015 |
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The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain. |
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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ LRF2015 |
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2617 |
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Author |
J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel |
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Title |
Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space |
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Conference Article |
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2015 |
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Workshop Proceedings on the 11th International Conference on Intelligent Environments |
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19 |
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373-380 |
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Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually. |
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Praga; Chzech Republic; July 2015 |
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IE |
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DAG; 600.077 |
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no |
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Admin @ si @ KNP2015 |
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2694 |
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Author |
Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny |
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Title |
Leveraging category-level labels for instance-level image retrieval |
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Conference Article |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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3045-3052 |
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In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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DAG |
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Admin @ si @ GRP2012 |
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2050 |
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Author |
Albert Gordo; Florent Perronnin |
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Asymmetric Distances for Binary Embeddings |
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2011 |
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IEEE Conference on Computer Vision and Pattern Recognition |
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729 - 736 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
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Providence, RI |
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978-1-4577-0394-2 |
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CVPR |
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Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
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1817 |
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Author |
Ernest Valveny; B. Lamiroy |
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Automatic Generation of Browsable Technical Documents. |
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Miscellaneous |
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2002 |
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Proceedings of the Sixteenth International Conference on Pattern Recognition ICPR 2002: 188–191. |
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Quebec, Canada |
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DAG @ dag @ VaL2002 |
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301 |
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Marçal Rusiñol; Lluis Pere de las Heras; Joan Mas; Oriol Ramos Terrades; Dimosthenis Karatzas; Anjan Dutta; Gemma Sanchez; Josep Llados |
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CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012 |
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2012 |
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Conference and Labs of the Evaluation Forum |
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Roma |
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CLEF |
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Admin @ si @ RHM2012 |
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2072 |
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