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Author | Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
Title | Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2903 - 2908 | ||
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Abstract | 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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Address | Stockholm; Sweden; August 2014 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.056; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ GRK2014b | Serial | 2497 | ||
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Author | Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez | ||||
Title | Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters | Type | Journal Article | ||
Year | 2014 | Publication | Expert Systems With Applications | Abbreviated Journal | EXSY |
Volume | 41 | Issue | 16 | Pages | 7281–7290 |
Keywords | Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks | ||||
Abstract | Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour. | ||||
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Notes | ADAS; 600.055; 600.057; 600.076 | Approved | no | ||
Call Number | Admin @ si @ LPA2014 | Serial | 2500 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras | ||||
Title | Combining Priors, Appearance and Context for Road Detection | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 3 | Pages | 1168-1178 |
Keywords | Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout | ||||
Abstract | Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
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Publisher | Place of Publication | Editor | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | ||
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ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076;ISE | Approved | no | ||
Call Number | Admin @ si @ ALG2014 | Serial | 2501 | ||
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Author | Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño | ||||
Title | Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos | Type | Conference Article | ||
Year | 2014 | Publication | CARE workshop | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection | ||||
Abstract | 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. | ||||
Address | Boston; USA; September 2014 | ||||
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Area | Expedition | Conference | CARE | ||
Notes | MV; DAG; 600.060; 600.047; 600.077;SIAI | Approved | no | ||
Call Number | Admin @ si @ NBF2014 | Serial | 2504 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta | ||||
Title | Semantic Pyramids for Gender and Action Recognition | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 23 | Issue | 8 | Pages | 3633-3645 |
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Abstract | Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC; LAMP; 601.160; 600.074; 600.079;MILAB | Approved | no | ||
Call Number | Admin @ si @ KWR2014 | Serial | 2507 | ||
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Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras | ||||
Title | The Photometry of Intrinsic Images | Type | Conference Article | ||
Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1494-1501 | ||
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Abstract | 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. | ||||
Address | Columbus; Ohio; USA; June 2014 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | CIC; 600.052; 600.051; 600.074 | Approved | no | ||
Call Number | Admin @ si @ SPB2014 | Serial | 2506 | ||
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Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer | ||||
Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | ||
Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1090 - 1097 | ||
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Abstract | 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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Address | Nottingham; UK; September 2014 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
Call Number | Admin @ si @ DKF2014 | Serial | 2509 | ||
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Author | C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger | ||||
Title | Limitations of visual gamma corrections in LCD displays | Type | Journal Article | ||
Year | 2014 | Publication | Displays | Abbreviated Journal | Dis |
Volume | 35 | Issue | 5 | Pages | 227–239 |
Keywords | Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration | ||||
Abstract | A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. | ||||
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Notes | CIC; DAG; 600.052; 600.077; 600.074 | Approved | no | ||
Call Number | Admin @ si @ PRK2014 | Serial | 2511 | ||
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Author | Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
Title | Unsupervised Deep Feature Extraction Of Hyperspectral Images | Type | Conference Article | ||
Year | 2014 | Publication | 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification | ||||
Abstract | 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. | ||||
Address | Lausanne; Switzerland; June 2014 | ||||
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Area | Expedition | Conference | WHISPERS | ||
Notes | MILAB; LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ RGC2014 | Serial | 2513 | ||
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Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
Title | A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3074 - 3079 | ||
Keywords | word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance | ||||
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. | ||||
Address | Stockholm; Sweden; August 2014 | ||||
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ISSN | 1051-4651 | ISBN | Medium | ||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ WEG2014a | Serial | 2515 | ||
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Author | Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre | ||||
Title | A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts | Type | Conference Article | ||
Year | 2014 | Publication | Digital Access to Textual Cultural Heritage Conference | Abbreviated Journal | |
Volume | Issue | Pages | 103-108 | ||
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Abstract | 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. | ||||
Address | Madrid; May 2014 | ||||
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ISSN | ISBN | 978-1-4503-2588-2 | Medium | ||
Area | Expedition | Conference | DATeCH | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ FLM2014 | Serial | 2516 | ||
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Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
Title | A Novel Learning-free Word Spotting Approach Based on Graph Representation | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume | Issue | Pages | 207-211 | ||
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Abstract | 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. | ||||
Address | Tours; France; April 2014 | ||||
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ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ WEG2014b | Serial | 2517 | ||
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Author | Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Fisher vectors over random density forest for object recognition | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 4328-4333 | ||
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Address | Stockholm; Sweden; August 2014 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BTS2014 | Serial | 2518 | ||
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Author | Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Unsupervised scene adaptation for faster multi- scale pedestrian detection | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3534 - 3539 | ||
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Address | Stockholm; Sweden; August 2014 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BLK2014 | Serial | 2519 | ||
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Author | Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Leveraging local neighborhood topology for large scale person re-identification | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 12 | Pages | 3767–3778 |
Keywords | Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 | ||||
Abstract | In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. | ||||
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Notes | LAMP; 601.240; 600.079 | Approved | no | ||
Call Number | Admin @ si @ KLB2014a | Serial | 2522 | ||
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