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| Author | Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados | ||||
| Title | Hierarchical graph representation for symbol spotting in graphical document images | Type | Conference Article | ||
| Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
| Volume | 7626 | Issue | Pages | 529-538 | |
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Abstract  |
Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset. | ||||
| Address | Miyajima-Itsukushima, Hiroshima | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
| Area | Expedition | Conference | SSPR&SPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BDJ2012 | Serial | 2126 | ||
| Permanent link to this record | |||||
| Author | Marco Pedersoli | ||||
| Title | Hierarchical Multiresolution Models for fast Object Detection | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract |
The ability to automatically detect and recognize objects in unconstrained images is becoming more and more critical: from security systems and autonomous robots, to smart phones and augmented reality, intelligent devices need to understand the meaning of images as a composition of semantic objects. This Thesis tackles the problem of fast object detection based on template models. Detection consists of searching for an object in an image by evaluating the similarity between a template model and an image region at each possible location and scale. In this work, we show that using a template model representation based on a multiple resolution hierarchy is an optimal choice that can lead to excellent detection accuracy and fast computation. We implement two different approaches that make use of a hierarchy of multiresolution models: a multiresolution cascade and a coarse-to-fine search. Also, we extend the coarse-to-fine search by introducing a deformable part-based model that achieves state-of-the-art results together with a very reduced computational cost. Finally, we specialize our approach to the challenging task of pedestrian detection from moving vehicles and show that the overall quality of the system outperforms previous works in terms of speed and accuracy. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Ped2012 | Serial | 2203 | ||
| Permanent link to this record | |||||
| Author | Susana Alvarez; Maria Vanrell | ||||
| Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 12 | Pages | 4312-4325 |
| Keywords | |||||
| Abstract |
The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ AlV2012a | Serial | 2130 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke | ||||
| Title | Writer Identification in Old Handwritten Music Scores | Type | Book Chapter | ||
| Year | 2012 | Publication | Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology | Abbreviated Journal | |
| Volume | Issue | Pages | 27-63 | ||
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| Abstract |
The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%. | ||||
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| Publisher | IGI-Global | Place of Publication | Editor | Copnstantin Papaodysseus | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ FLS2012 | Serial | 1828 | ||
| Permanent link to this record | |||||
| Author | Albert Gordo; Florent Perronnin; Ernest Valveny | ||||
| Title | Document classification using multiple views | Type | Conference Article | ||
| Year | 2012 | Publication | 10th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 33-37 | ||
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| Abstract |
The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task. | ||||
| Address | Australia | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE Computer Society Washington | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-0-7695-4661-2 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GPV2012 | Serial | 2049 | ||
| Permanent link to this record | |||||
| Author | Miguel Oliveira; Angel Sappa; V. Santos | ||||
| Title | Color Correction using 3D Gaussian Mixture Models | Type | Conference Article | ||
| Year | 2012 | Publication | 9th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 7324 | Issue | I | Pages | 97-106 |
| Keywords | |||||
| Abstract |
The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 10.1007/978-3-642-31295-3_12 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ OSS2012a | Serial | 2015 | ||
| Permanent link to this record | |||||
| Author | Miguel Oliveira; Angel Sappa; V. Santos | ||||
| Title | Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
| Volume | Issue | Pages | 299-303 | ||
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| Abstract |
The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. | ||||
| Address | Alcalá de Henares | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1931-0587 | ISBN | 978-1-4673-2119-8 | Medium | |
| Area | Expedition | Conference | IV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ OSS2012b | Serial | 2021 | ||
| Permanent link to this record | |||||
| Author | Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez | ||||
| Title | Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 96 | Issue | 1 | Pages | 83-102 |
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| Abstract |
The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi- nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21. |
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE;CIC;ADAS | Approved | no | ||
| Call Number | Admin @ si @ BGW2012 | Serial | 1718 | ||
| Permanent link to this record | |||||
| Author | David Roche; Debora Gil; Jesus Giraldo | ||||
| Title | Assessing agonist efficacy in an uncertain Em world | Type | Conference Article | ||
| Year | 2012 | Publication | 40th Keystone Symposia on mollecular and celular biology | Abbreviated Journal | |
| Volume | Issue | Pages | 79 | ||
| Keywords | |||||
| Abstract |
The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed. |
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| Address | Fairmont Banff Springs, Banff, Alberta, Canada | ||||
| Corporate Author | Keystone Symposia | Thesis | |||
| Publisher | Keystone Symposia | Place of Publication | Editor | A. Christopoulus and M. Bouvier | |
| Language | english | Summary Language | english | Original Title | |
| Series Editor | Keystone Symposia | Series Title | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | KSMCB | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ RGG2012 | Serial | 1855 | ||
| Permanent link to this record | |||||
| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | Efficient pairwise classification using Local Cross Off strategy | Type | Conference Article | ||
| Year | 2012 | Publication | 25th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | 7310 | Issue | Pages | 25-36 | |
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| Abstract |
The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. | ||||
| Address | Toronto, Ontario | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-30352-4 | Medium | |
| Area | Expedition | Conference | AI | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2012c | Serial | 2044 | ||
| Permanent link to this record | |||||
| Author | Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance | Type | Book Chapter | ||
| Year | 2012 | Publication | Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence | Abbreviated Journal | |
| Volume | 384 | Issue | 3 | Pages | 87-95 |
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| Abstract |
The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-24033-1 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ BFR2012 | Serial | 2062 | ||
| Permanent link to this record | |||||
| Author | Noha Elfiky | ||||
| Title | Compact, Adaptive and Discriminative Spatial Pyramids for Improved Object and Scene Classification | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract |
The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these large-scale datasets efficiently. Nowadays the Bag-of-Words (BoW) is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation, which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation, still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Elf2012 | Serial | 2202 | ||
| Permanent link to this record | |||||
| Author | Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell | ||||
| Title | Spectral sharpening by spherical sampling | Type | Journal Article | ||
| Year | 2012 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 29 | Issue | 7 | Pages | 1199-1210 |
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| Abstract |
There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1084-7529 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ FVS2012 | Serial | 2000 | ||
| Permanent link to this record | |||||
| Author | Wenjuan Gong; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca | ||||
| Title | A New Image Dataset on Human Interactions | Type | Conference Article | ||
| Year | 2012 | Publication | 7th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 7378 | Issue | Pages | 204-209 | |
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| Abstract |
This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images. | ||||
| Address | Mallorca | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-31566-4 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ GGT2012 | Serial | 2030 | ||
| Permanent link to this record | |||||
| Author | Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez | ||||
| Title | Moving object detection from mobile platforms using stereo data registration | Type | Book Chapter | ||
| Year | 2012 | Publication | Computational Intelligence paradigms in advanced pattern classification | Abbreviated Journal | |
| Volume | 386 | Issue | Pages | 25-37 | |
| Keywords | pedestrian detection | ||||
| Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Marek R. Ogiela; Lakhmi C. Jain | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-24048-5 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ SGD2012 | Serial | 2061 | ||
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