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| Author | Christophe Rigaud; Clement Guerin | ||||
| Title | Localisation contextuelle des personnages de bandes dessinées | Type | Conference Article | ||
| Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
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| Abstract | Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%. | ||||
| Address | Nancy; Francia; March 2014 | ||||
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| Area | Expedition | Conference | CIFED | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RiG2014 | Serial | 2481 | ||
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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Word Spotting and Recognition with Embedded Attributes | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 12 | Pages | 2552 - 2566 |
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| Abstract | This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | DAG; 600.056; 600.045; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ AGF2014a | Serial | 2483 | ||
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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Segmentation-free Word Spotting with Exemplar SVMs | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 12 | Pages | 3967–3978 |
| Keywords | Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression | ||||
| Abstract | 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. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. 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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| Notes | DAG; 600.045; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ AGF2014b | Serial | 2485 | ||
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| Author | Jorge Bernal | ||||
| Title | Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps | Type | Journal Article | ||
| Year | 2014 | Publication | Electronic Letters on Computer Vision and Image Analysis | Abbreviated Journal | ELCVIA |
| Volume | 13 | Issue | 2 | Pages | 9-10 |
| Keywords | Colonoscopy; polyp localization; polyp segmentation; Eye-tracking | ||||
| Abstract | Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. | ||||
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Place of Publication | Editor | Alicia Fornes; Volkmar Frinken | ||
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| Notes | MV | Approved | no | ||
| Call Number | Admin @ si @ Ber2014 | Serial | 2487 | ||
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| Author | Carlo Gatta; Adriana Romero; Joost Van de Weijer | ||||
| Title | Unrolling loopy top-down semantic feedback in convolutional deep networks | Type | Conference Article | ||
| Year | 2014 | Publication | Workshop on Deep Vision: Deep Learning for Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 498-505 | ||
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| Abstract | In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches. | ||||
| Address | Columbus; Ohio; June 2014 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ GRW2014 | Serial | 2490 | ||
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| Author | Dimosthenis Karatzas; Sergi Robles; Lluis Gomez | ||||
| Title | An on-line platform for ground truthing and performance evaluation of text extraction systems | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 242 - 246 | ||
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| Abstract | This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use. |
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| Address | Tours; Francia; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.056; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ KRG2014 | Serial | 2491 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | MSER-based Real-Time Text Detection and Tracking | Type | Conference Article | ||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3110 - 3115 | ||
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| Abstract | 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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| Address | Stockholm; August 2014 | ||||
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| ISSN | 1051-4651 | ISBN | Medium | ||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.056; 601.158; 601.197; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GoK2014a | Serial | 2492 | ||
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| Author | Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual | ||||
| Title | Spatial Discriminant ICA for RS-fMRI characterisation | Type | Conference Article | ||
| Year | 2014 | Publication | 4th International Workshop on Pattern Recognition in Neuroimaging | Abbreviated Journal | |
| Volume | Issue | Pages | 1-4 | ||
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| Abstract | Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. | ||||
| Address | Tübingen; June 2014 | ||||
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| ISSN | ISBN | 978-1-4799-4150-6 | Medium | ||
| Area | Expedition | Conference | PRNI | ||
| Notes | OR;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TBI2014 | Serial | 2493 | ||
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| Author | Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
| Title | Fast Structural Matching for Document Image Retrieval through Spatial Databases | Type | Conference Article | ||
| Year | 2014 | Publication | Document Recognition and Retrieval XXI | Abbreviated Journal | |
| Volume | 9021 | Issue | Pages | ||
| Keywords | Document image retrieval; distance transform; MSER; spatial database | ||||
| Abstract | 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. | ||||
| Address | Amsterdam; September 2014 | ||||
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| Area | Expedition | Conference | SPIE-DRR | ||
| Notes | DAG; 600.056; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GRK2014a | Serial | 2496 | ||
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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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Place of Publication | Editor | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | ||
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| ISSN | 1524-9050 | ISBN | Medium | ||
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| 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 | |
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| 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 | ||
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| 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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