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Author Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio
Title A computational framework for cancer response assessment based on oncological PET-CT scans Type Journal Article
Year 2014 Publication Computers in Biology and Medicine Abbreviated Journal CBM
Volume 55 Issue Pages 92–99
Keywords Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis
Abstract In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.
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Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ SED2014 Serial 2606
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Author Maedeh Aghaei; Petia Radeva
Title Bag-of-Tracklets for Person Tracking in Life-Logging Data Type Conference Article
Year 2014 Publication 17th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume 269 Issue Pages 35-44
Keywords
Abstract By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data.
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Series Editor Series Title Abbreviated Series Title
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ISSN (up) ISBN 978-1-61499-451-0 Medium
Area Expedition Conference CCIA
Notes MILAB Approved no
Call Number Admin @ si @ AgR2015 Serial 2607
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Author Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto
Title Va-ID: A trust-based virtual assessment system Type Conference Article
Year 2014 Publication 6th International Conference on Intelligent Networking and Collaborative Systems Abbreviated Journal
Volume Issue Pages 328 - 335
Keywords
Abstract Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model.
Address Salerna; Italy; September 2014
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Publisher Place of Publication Editor
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ISSN (up) ISBN 978-1-4799-6386-7 Medium
Area Expedition Conference INCOS
Notes OR; HuPBA;MV Approved no
Call Number Admin @ si @ ABB2014 Serial 2620
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Author B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva
Title Learning Deep Features for Scene Recognition using Places Database Type Conference Article
Year 2014 Publication 28th Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages 487-495
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Abstract
Address Montreal; Canada; December 2014
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Area Expedition Conference NIPS
Notes OR;MV Approved no
Call Number Admin @ si @ ZLX2014 Serial 2621
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Author Agata Lapedriza; David Masip; David Sanchez
Title Emotions Classification using Facial Action Units Recognition Type Conference Article
Year 2014 Publication 17th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume 269 Issue Pages 55-64
Keywords
Abstract In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection.
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ISSN (up) ISBN 978-1-61499-451-0 Medium
Area Expedition Conference CCIA
Notes OR;MV Approved no
Call Number Admin @ si @ LMS2014 Serial 2622
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Author Ariel Amato
Title Moving cast shadow detection Type Journal Article
Year 2014 Publication Electronic letters on computer vision and image analysis Abbreviated Journal ELCVIA
Volume 13 Issue 2 Pages 70-71
Keywords
Abstract Motion perception is an amazing innate ability of the creatures on the planet. This adroitness entails a functional advantage that enables species to compete better in the wild. The motion perception ability is usually employed at different levels, allowing from the simplest interaction with the ’physis’ up to the most transcendental survival tasks. Among the five classical perception system , vision is the most widely used in the motion perception field. Millions years of evolution have led to a highly specialized visual system in humans, which is characterized by a tremendous accuracy as well as an extraordinary robustness. Although humans and an immense diversity of species can distinguish moving object with a seeming simplicity, it has proven to be a difficult and non trivial problem from a computational perspective. In the field of Computer Vision, the detection of moving objects is a challenging and fundamental research area. This can be referred to as the ’origin’ of vast and numerous vision-based research sub-areas. Nevertheless, from the bottom to the top of this hierarchical analysis, the foundations still relies on when and where motion has occurred in an image. Pixels corresponding to moving objects in image sequences can be identified by measuring changes in their values. However, a pixel’s value (representing a combination of color and brightness) could also vary due to other factors such as: variation in scene illumination, camera noise and nonlinear sensor responses among others. The challenge lies in detecting if the changes in pixels’ value are caused by a genuine object movement or not. An additional challenging aspect in motion detection is represented by moving cast shadows. The paradox arises because a moving object and its cast shadow share similar motion patterns. However, a moving cast shadow is not a moving object. In fact, a shadow represents a photometric illumination effect caused by the relative position of the object with respect to the light sources. Shadow detection methods are mainly divided in two domains depending on the application field. One normally consists of static images where shadows are casted by static objects, whereas the second one is referred to image sequences where shadows are casted by moving objects. For the first case, shadows can provide additional geometric and semantic cues about shape and position of its casting object as well as the localization of the light source. Although the previous information can be extracted from static images as well as video sequences, the main focus in the second area is usually change detection, scene matching or surveillance. In this context, a shadow can severely affect with the analysis and interpretation of the scene. The work done in the thesis is focused on the second case, thus it addresses the problem of detection and removal of moving cast shadows in video sequences in order to enhance the detection of moving object.
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Notes ISE Approved no
Call Number Admin @ si @ Ama2014 Serial 2870
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Author L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink
Title A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting Type Journal
Year 2014 Publication Manuscript Cultures Abbreviated Journal
Volume 7 Issue Pages 47-58
Keywords
Abstract With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline.
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Notes DAG; 600.061; 600.077 Approved no
Call Number Admin @ si @ Serial 3190
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired Type Journal Article
Year 2014 Publication Computer Abbreviated Journal COMP
Volume 47 Issue 4 Pages 52-58
Keywords
Abstract Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.
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Series Editor Series Title Abbreviated Series Title
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ISSN (up) 0018-9162 ISBN Medium
Area Expedition Conference
Notes LAMP; Approved no
Call Number Admin @ si @ TSR2014a Serial 2317
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Author Marçal Rusiñol; Josep Llados
Title Boosting the Handwritten Word Spotting Experience by Including the User in the Loop Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 3 Pages 1063–1072
Keywords Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling
Abstract In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list.
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ISSN (up) 0031-3203 ISBN Medium
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Notes DAG; 600.045; 600.061; 600.077 Approved no
Call Number Admin @ si @ RuL2013 Serial 2343
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Author David Roche; Debora Gil; Jesus Giraldo
Title Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? Type Book Chapter
Year 2014 Publication G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology Abbreviated Journal
Volume 796 Issue 3 Pages 159-181
Keywords β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model
Abstract Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists.
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Publisher Springer Netherlands Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 0065-2598 ISBN 978-94-007-7422-3 Medium
Area Expedition Conference
Notes IAM; 600.075 Approved no
Call Number IAM @ iam @ RGG2014 Serial 2197
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Author Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik
Title Asymmetric Distances for Binary Embeddings Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 1 Pages 33-47
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Abstract In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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ISSN (up) 0162-8828 ISBN Medium
Area Expedition Conference
Notes DAG; 600.045; 605.203; 600.077 Approved no
Call Number Admin @ si @ GPG2014 Serial 2272
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez
Title Domain Adaptation of Deformable Part-Based Models Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 12 Pages 2367-2380
Keywords Domain Adaptation; Pedestrian Detection
Abstract The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors.
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ISSN (up) 0162-8828 ISBN Medium
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Notes ADAS; 600.057; 600.054; 601.217; 600.076 Approved no
Call Number ADAS @ adas @ XRV2014b Serial 2436
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Author David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo
Title Virtual and Real World Adaptation for Pedestrian Detection Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 4 Pages 797-809
Keywords Domain Adaptation; Pedestrian Detection
Abstract Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.
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ISSN (up) 0162-8828 ISBN Medium
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Notes ADAS; 600.057; 600.054; 600.076 Approved no
Call Number ADAS @ adas @ VML2014 Serial 2275
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Author Carlo Gatta; Francesco Ciompi
Title Stacked Sequential Scale-Space Taylor Context Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 8 Pages 1694-1700
Keywords
Abstract We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
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Series Editor Series Title Abbreviated Series Title
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ISSN (up) 0162-8828 ISBN Medium
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Notes LAMP; MILAB; 601.160; 600.079 Approved no
Call Number Admin @ si @ GaC2014 Serial 2466
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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
Keywords
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 (up) 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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