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Author | Gioacchino Vino; Angel Sappa | ||||
Title | Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach | Type | Conference Article | ||
Year | 2013 | Publication | 10th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
Volume | 7950 | Issue | Pages | 354-363 | |
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Abstract | This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. | ||||
Address | Póvoa de Varzim; Portugal; June 2013 | ||||
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 | 978-3-642-39093-7 | Medium | |
Area | Expedition | Conference | ICIAR | ||
Notes | ADAS; 600.055 | Approved | no | ||
Call Number | Admin @ si @ ViS2013 | Serial | 2562 | ||
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Author | David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich | ||||
Title | Traffic sign recognition for computer vision project-based learning | Type | Journal Article | ||
Year | 2013 | Publication | IEEE Transactions on Education | Abbreviated Journal | T-EDUC |
Volume | 56 | Issue | 3 | Pages | 364-371 |
Keywords | traffic signs | ||||
Abstract | This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. | ||||
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ISSN | 0018-9359 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; CIC | Approved | no | ||
Call Number | Admin @ si @ GSL2013; ADAS @ adas @ | Serial | 2160 | ||
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Author | David Roche; Debora Gil; Jesus Giraldo | ||||
Title | Multiple active receptor conformation, agonist efficacy and maximum effect of the system: the conformation-based operational model of agonism, | Type | Journal Article | ||
Year | 2013 | Publication | Drug Discovery Today | Abbreviated Journal | DDT |
Volume | 18 | Issue | 7-8 | Pages | 365-371 |
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Abstract | The operational model of agonism assumes that the maximum effect a particular receptor system can achieve (the Em parameter) is fixed. Em estimates are above but close to the asymptotic maximum effects of endogenous agonists. The concept of Em is contradicted by superagonists and those positive allosteric modulators that significantly increase the maximum effect of endogenous agonists. An extension of the operational model is proposed that assumes that the Em parameter does not necessarily have a single value for a receptor system but has multiple values associated to multiple active receptor conformations. The model provides a mechanistic link between active receptor conformation and agonist efficacy, which can be useful for the analysis of agonist response under different receptor scenarios. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Notes | IAM; 600.057; 600.054 | Approved | no | ||
Call Number | IAM @ iam @ RGG2013a | Serial | 2190 | ||
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Author | Katerine Diaz; Francesc J. Ferri; W. Diaz | ||||
Title | Fast Approximated Discriminative Common Vectors using rank-one SVD updates | Type | Conference Article | ||
Year | 2013 | Publication | 20th International Conference On Neural Information Processing | Abbreviated Journal | |
Volume | 8228 | Issue | III | Pages | 368-375 |
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Abstract | An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Address | Daegu; Korea; November 2013 | ||||
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 | 978-3-642-42050-4 | Medium | |
Area | Expedition | Conference | ICONIP | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ DFD2013 | Serial | 2439 | ||
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Author | Isabel Guitart; Jordi Conesa; Luis Villarejo; Agata Lapedriza; David Masip; Antoni Perez; Elena Planas | ||||
Title | Opinion Mining on Educational Resources at the Open University of Catalonia | Type | Conference Article | ||
Year | 2013 | Publication | 3rd International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional approaches. In conjunction with CISIS 2013: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems | Abbreviated Journal | |
Volume | Issue | Pages | 385 - 390 | ||
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Abstract | In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question. | ||||
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ISSN | ISBN | 978-0-7695-4992-7 | Medium | ||
Area | Expedition | Conference | ALICE | ||
Notes | OR;MV | Approved | no | ||
Call Number | GCV2013 | Serial | 2268 | ||
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Author | Sergio Escalera; Jordi Gonzalez; Xavier Baro; Miguel Reyes; Oscar Lopes; Isabelle Guyon; V. Athitsos; Hugo Jair Escalante | ||||
Title | Multi-modal Gesture Recognition Challenge 2013: Dataset and Results | Type | Conference Article | ||
Year | 2013 | Publication | 15th ACM International Conference on Multimodal Interaction | Abbreviated Journal | |
Volume | Issue | Pages | 445-452 | ||
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Abstract | The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable
depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audio and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results were obtained by the first ranked participants. |
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Address | Sidney; Australia; December 2013 | ||||
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 | ISBN | 978-1-4503-2129-7 | Medium | ||
Area | Expedition | Conference | ICMI | ||
Notes | HUPBA; ISE; 600.063;MV | Approved | no | ||
Call Number | Admin @ si @ EGB2013 | Serial | 2373 | ||
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Author | Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez | ||||
Title | Road Geometry Classification by Adaptative Shape Models | Type | Journal Article | ||
Year | 2013 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 14 | Issue | 1 | Pages | 459-468 |
Keywords | road detection | ||||
Abstract | Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGD2013;; ADAS @ adas @ | Serial | 2269 | ||
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Author | Adriana Romero; Carlo Gatta | ||||
Title | Do We Really Need All These Neurons? | Type | Conference Article | ||
Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 7887 | Issue | Pages | 460--467 | |
Keywords | Retricted Boltzmann Machine; hidden units; unsupervised learning; classification | ||||
Abstract | Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch. | ||||
Address | Madeira; Portugal; June 2013 | ||||
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 | 978-3-642-38627-5 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | MILAB; 600.046 | Approved | no | ||
Call Number | Admin @ si @ RoG2013 | Serial | 2311 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Multi-script Text Extraction from Natural Scenes | Type | Conference Article | ||
Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 467-471 | ||
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Abstract | Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages. | ||||
Address | Washington; USA; August 2013 | ||||
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 | 1520-5363 | ISBN | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.056; 601.158; 601.197 | Approved | no | ||
Call Number | Admin @ si @ GoK2013 | Serial | 2310 | ||
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Author | Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu | ||||
Title | Facial expression recognition using tracked facial actions: Classifier performance analysis | Type | Journal Article | ||
Year | 2013 | Publication | Engineering Applications of Artificial Intelligence | Abbreviated Journal | EAAI |
Volume | 26 | Issue | 1 | Pages | 467-477 |
Keywords | Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction | ||||
Abstract | In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | OR; 600.046;MV | Approved | no | ||
Call Number | Admin @ si @ DMR2013 | Serial | 2185 | ||
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Author | Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa | ||||
Title | Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection | Type | Conference Article | ||
Year | 2013 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
Volume | Issue | Pages | 467 - 472 | ||
Keywords | Pedestrian Detection; Virtual World; Part based | ||||
Abstract | State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). | ||||
Address | Gold Coast; Australia; June 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE | 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 | 978-1-4673-2754-1 | Medium | |
Area | Expedition | Conference | IV | ||
Notes | ADAS; 600.054; 600.057 | Approved | no | ||
Call Number | XVL2013; ADAS @ adas @ xvl2013a | Serial | 2214 | ||
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Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario | Type | Conference Article | ||
Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 8048 | Issue | Pages | 483-490 | |
Keywords | Optical flow; regularization; Driver Assistance Systems; Performance Evaluation | ||||
Abstract | Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI). | ||||
Address | York; UK; August 2013 | ||||
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 | 978-3-642-40245-6 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | ADAS; 600.055; 601.215 | Approved | no | ||
Call Number | Admin @ si @ OnS2013b | Serial | 2244 | ||
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Author | Victor Ponce; Sergio Escalera; Xavier Baro | ||||
Title | Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings | Type | Conference Article | ||
Year | 2013 | Publication | 15th ACM International Conference on Multimodal Interaction | Abbreviated Journal | |
Volume | Issue | Pages | 495-502 | ||
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Abstract | In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions. | ||||
Address | Sidney; Australia; December 2013 | ||||
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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 | ISBN | 978-1-4503-2129-7 | Medium | ||
Area | Expedition | Conference | ICMI | ||
Notes | HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ PEB2013 | Serial | 2488 | ||
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Author | Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen | ||||
Title | Improving HMM-Based Keyword Spotting with Character Language Models | Type | Conference Article | ||
Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 506-510 | ||
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Abstract | Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance. | ||||
Address | Washington; USA; August 2013 | ||||
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 | 1520-5363 | ISBN | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.045; 605.203 | Approved | no | ||
Call Number | Admin @ si @ FFB2013 | Serial | 2295 | ||
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Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados | ||||
Title | Integrating Visual and Textual Cues for Query-by-String Word Spotting | Type | Conference Article | ||
Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 511 - 515 | ||
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Abstract | In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. | ||||
Address | Washington; USA; August 2013 | ||||
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 | 1520-5363 | ISBN | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG; ADAS; 600.045; 600.055; 600.061 | Approved | no | ||
Call Number | Admin @ si @ ART2013 | Serial | 2224 | ||
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