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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
MSER-based Real-Time Text Detection and Tracking |
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Conference Article |
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Year |
2014 |
Publication  |
22nd International Conference on Pattern Recognition |
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3110 - 3115 |
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We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices. |
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Stockholm; August 2014 |
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1051-4651 |
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DAG; 600.056; 601.158; 601.197; 600.077 |
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Admin @ si @ GoK2014a |
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2492 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions |
Type |
Conference Article |
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Year |
2014 |
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22nd International Conference on Pattern Recognition |
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2903 - 2908 |
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Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag
of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships.
Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods. |
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Stockholm; Sweden; August 2014 |
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DAG; 600.056; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ GRK2014b |
Serial |
2497 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance |
Type |
Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3074 - 3079 |
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Keywords |
word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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Abstract |
Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014a |
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2515 |
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Author |
Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Fisher vectors over random density forest for object recognition |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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4328-4333 |
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Stockholm; Sweden; August 2014 |
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LAMP; 600.079 |
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Admin @ si @ BTS2014 |
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2518 |
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Author |
Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Unsupervised scene adaptation for faster multi- scale pedestrian detection |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3534 - 3539 |
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Stockholm; Sweden; August 2014 |
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LAMP; 600.079 |
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Admin @ si @ BLK2014 |
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2519 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
EM-Based Layout Analysis Method for Structured Documents |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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315-320 |
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In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
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1051-4651 |
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DAG; 602.006; 600.061; 600.077 |
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Admin @ si @ CrR2014 |
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2530 |
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Author |
Victor Ponce; Mario Gorga; Xavier Baro; Sergio Escalera |
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Title |
Human Behavior Analysis from Video Data Using Bag-of-Gestures |
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Conference Article |
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Year |
2011 |
Publication |
22nd International Joint Conference on Artificial Intelligence |
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3 |
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2836-2837 |
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Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors. |
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Barcelona |
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978-1-57735-516-8 |
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IJCAI |
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HuPBA;MV |
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no |
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Admin @ si @ PGB2011b |
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1770 |
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Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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Title |
LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
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Conference Article |
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2015 |
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22th IEEE International Conference on Image Processing |
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178 - 181 |
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Quebec; Canada; September 2015 |
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ICIP |
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ADAS; 600.076 |
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Admin @ si @ AST2015 |
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2630 |
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Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl |
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Title |
The EMPATHIC Virtual Coach: a demo |
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Conference Article |
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2021 |
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23rd ACM International Conference on Multimodal Interaction |
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848-851 |
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The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios. |
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Virtual; October 2021 |
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ICMI |
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HUPBA; no proj |
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Admin @ si @ OVB2021 |
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3644 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Efficient Exemplar Word Spotting |
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Conference Article |
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2012 |
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23rd British Machine Vision Conference |
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67.1- 67.11 |
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In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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1-901725-46-4 |
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BMVC |
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DAG |
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DAG @ dag @ AGF2012 |
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1984 |
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Author |
Naila Murray; Luca Marchesotti; Florent Perronnin |
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Title |
Learning to Rank Images using Semantic and Aesthetic Labels |
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Conference Article |
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2012 |
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23rd British Machine Vision Conference |
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110.1-110.10 |
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Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one. |
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Guildford, London |
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1-901725-46-4 |
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BMVC |
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CIC |
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Admin @ si @ MMP2012b |
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2027 |
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Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Context Aware Keypoint Extraction for Robust Image Representation |
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2012 |
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23rd British Machine Vision Conference |
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100.1 - 100.12 |
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BMVC |
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MILAB |
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Admin @ si @ MCG2012a |
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2140 |
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Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Automatic Point-based Facial Trait Judgments Evaluation |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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2715–2720 |
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Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ RMT2010 |
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1282 |
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Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Abstract |
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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3D Scene Priors for Road Detection |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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57–64 |
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road detection |
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Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS @ adas @ AGL2010a |
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1302 |
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