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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases |
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Conference Article |
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Year |
2014 |
Publication |
Document Recognition and Retrieval XXI |
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9021 |
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Keywords |
Document image retrieval; distance transform; MSER; spatial database |
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The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. |
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Amsterdam; September 2014 |
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SPIE-DRR |
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DAG; 600.056; 600.061; 600.077 |
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Admin @ si @ GRK2014a |
Serial |
2496 |
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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 |
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Conference Article |
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Year |
2014 |
Publication |
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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Admin @ si @ GRK2014b |
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2497 |
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Author |
Jorge Bernal; Debora Gil; Carles Sanchez; F. Javier Sanchez |
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Title |
Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis |
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Conference Article |
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2014 |
Publication |
1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy |
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8899 |
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1-10 |
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Image Segmentation; Polyps, Colonoscopy; Valley Information; Energy Maps |
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In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation. |
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Boston; USA; September 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-13409-3 |
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CARE |
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MV; IAM; 600.044; 600.047; 600.060; 600.075 |
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Admin @ si @ BGS2014b |
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2503 |
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Author |
Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño |
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Title |
Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos |
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Conference Article |
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2014 |
Publication |
CARE workshop |
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Keywords |
Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection |
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We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels. |
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Boston; USA; September 2014 |
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MV; DAG; 600.060; 600.047; 600.077;SIAI |
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Admin @ si @ NBF2014 |
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2504 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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Title |
The Photometry of Intrinsic Images |
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Conference Article |
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Year |
2014 |
Publication |
27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
Serial |
2506 |
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Author |
M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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Title |
Adaptive color attributes for real-time visual tracking |
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Conference Article |
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Year |
2014 |
Publication |
27th IEEE Conference on Computer Vision and Pattern Recognition |
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1090 - 1097 |
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Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second. |
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Nottingham; UK; September 2014 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ DKF2014 |
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2509 |
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Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Unsupervised Deep Feature Extraction Of Hyperspectral Images |
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Conference Article |
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2014 |
Publication |
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification |
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This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. |
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Lausanne; Switzerland; June 2014 |
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WHISPERS |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RGC2014 |
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2513 |
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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 |
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Conference Article |
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2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3074 - 3079 |
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word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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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 |
Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre |
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Title |
A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts |
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Conference Article |
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2014 |
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Digital Access to Textual Cultural Heritage Conference |
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103-108 |
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In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts. |
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Madrid; May 2014 |
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978-1-4503-2588-2 |
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DATeCH |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ FLM2014 |
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2516 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Novel Learning-free Word Spotting Approach Based on Graph Representation |
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Conference Article |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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207-211 |
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Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of 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. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014b |
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2517 |
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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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2014 |
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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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Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo |
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Unsupervised scene adaptation for faster multi- scale pedestrian detection |
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2014 |
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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 |
Antonio Hernandez; Stan Sclaroff; Sergio Escalera |
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Title |
Contextual rescoring for Human Pose Estimation |
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Conference Article |
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2014 |
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25th British Machine Vision Conference |
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A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images
while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches. |
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Nottingham; UK; September 2013 |
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BMVC |
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HuPBA;MILAB |
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HSE2014 |
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Sergio Escalera; Xavier Baro; Jordi Gonzalez; Miguel Angel Bautista; Meysam Madadi; Miguel Reyes; Victor Ponce; Hugo Jair Escalante; Jaime Shotton; Isabelle Guyon |
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ChaLearn Looking at People Challenge 2014: Dataset and Results |
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2014 |
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ECCV Workshop on ChaLearn Looking at People |
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8925 |
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459-473 |
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Human Pose Recovery; Behavior Analysis; Action and in- teractions; Multi-modal gestures; recognition |
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This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively. |
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HuPBA; ISE; 600.063;MV |
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Admin @ si @ EBG2014 |
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2529 |
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Francisco Cruz; Oriol Ramos Terrades |
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EM-Based Layout Analysis Method for Structured Documents |
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2014 |
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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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ICPR |
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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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