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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title Robust Head Gestures Recognition for Assistive Technology Type Book Chapter
Year 2014 Publication Pattern Recognition Abbreviated Journal
Volume 8495 Issue Pages 152-161
Keywords
Abstract This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
Address
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-07490-0 Medium
Area Expedition Conference
Notes LAMP; Approved no
Call Number Admin @ si @ TSR2014b Serial 2505
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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
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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
Keywords
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
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
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
Keywords
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.
Address Nottingham; UK; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
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 Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg
Title Painting-91: A Large Scale Database for Computational Painting Categorization Type Journal Article
Year 2014 Publication Machine Vision and Applications Abbreviated Journal MVAP
Volume 25 Issue 6 Pages 1385-1397
Keywords
Abstract Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0932-8092 ISBN Medium
Area Expedition Conference
Notes CIC; LAMP; 600.074; 600.079 Approved no
Call Number Admin @ si @ KBW2014 Serial 2510
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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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
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
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference WHISPERS
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RGC2014 Serial 2513
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Author Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera
Title Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial Type Journal Article
Year 2011 Publication ReVisión Abbreviated Journal
Volume 4 Issue 1 Pages
Keywords
Abstract La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1989-1199 ISBN Medium
Area Expedition Conference
Notes HuPBA; MILAB;MV Approved no
Call Number Admin @ si @ PGB2011d Serial 2514
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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
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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
Keywords
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
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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
Keywords
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
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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
Permanent link to this record
 
 
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
Keywords
Abstract
Address Stockholm; Sweden; August 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
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
Keywords
Abstract
Address Stockholm; Sweden; August 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes LAMP; 600.079 Approved no
Call Number Admin @ si @ BLK2014 Serial 2519
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Author Svebor Karaman; Andrew Bagdanov; Lea Landucci; Gianpaolo D'Amico; Andrea Ferracani; Daniele Pezzatini; Alberto del Bimbo
Title Personalized multimedia content delivery on an interactive table by passive observation of museum visitors Type Journal Article
Year 2016 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 75 Issue 7 Pages 3787-3811
Keywords Computer vision; Video surveillance; Cultural heritage; Multimedia museum; Personalization; Natural interaction; Passive profiling
Abstract The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello).
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes LAMP; 601.240; 600.079 Approved no
Call Number Admin @ si @ KBL2016 Serial 2520
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Author Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo
Title From re-identification to identity inference: Labeling consistency by local similarity constraints Type Book Chapter
Year 2014 Publication Person Re-Identification Abbreviated Journal
Volume 2 Issue Pages 287-307
Keywords re-identification; Identity inference; Conditional random fields; Video surveillance
Abstract In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2191-6586 ISBN 978-1-4471-6295-7 Medium
Area Expedition Conference
Notes LAMP; 600.079 Approved no
Call Number Admin @ si @KLB2014b Serial 2521
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