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| Author | C. Alejandro Parraga | ||||
| Title | Perceptual Psychophysics | Type | Book Chapter | ||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN  |
978-3-527-41264-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | ||
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| Author | Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio | ||||
| Title | Introduction to NIPS 2017 Competition Track | Type | Book Chapter | ||
| Year | 2018 | Publication | The NIPS ’17 Competition: Building Intelligent Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 1-23 | ||
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| Abstract | Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
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| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | Sergio Escalera; Markus Weimer | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-319-94042-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ EWB2018 | Serial | 3200 | ||
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| Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Towards the Alignment of Handwritten Music Scores | Type | Book Chapter | ||
| Year | 2017 | Publication | International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 9657 | Issue | Pages | 103-116 | |
| Keywords | Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment | ||||
| Abstract | It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | Bart Lamiroy; R Dueire Lins | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-319-52158-9 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.097; 602.006; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RFL2017 | Serial | 2955 | ||
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| Author | Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil | ||||
| Title | Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging | Type | Book Chapter | ||
| Year | 2015 | Publication | Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
| Volume | 9534 | Issue | Pages | 69-79 | |
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| Abstract | Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. | ||||
| Address | Munich; Germany; January 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | 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 |
978-3-319-28711-9 | Medium | |
| Area | Expedition | Conference | STACOM | ||
| Notes | ADAS; IAM; 600.075; 600.076; 600.060; 601.145 | Approved | no | ||
| Call Number | Admin @ si @ KHM2015 | Serial | 2734 | ||
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| Author | E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
| Title | Regularized Clustering for Egocentric Video Segmentation | Type | Book Chapter | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 327-336 | ||
| Keywords | Temporal video segmentation ; Egocentric videos ; Clustering | ||||
| Abstract | In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-319-19390-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @TDB2015a | Serial | 2781 | ||
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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 | |
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| 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. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | 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 |
978-3-319-07490-0 | Medium | |
| Area | Expedition | Conference | |||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ TSR2014b | Serial | 2505 | ||
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| Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca | ||||
| Title | Human Body Pose Estimation in Multi-view Environments | Type | Book Chapter | ||
| Year | 2022 | Publication | ICT Applications for Smart Cities. Intelligent Systems Reference Library | Abbreviated Journal | |
| Volume | 224 | Issue | Pages | 79-99 | |
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| Abstract | This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches. | ||||
| Address | September 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | ISRL | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-031-06306-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ CSV2022b | Serial | 3810 | ||
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| Author | Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera | ||||
| Title | Video Analytics in Urban Environments: Challenges and Approaches | Type | Book Chapter | ||
| Year | 2022 | Publication | ICT Applications for Smart Cities | Abbreviated Journal | |
| Volume | 224 | Issue | Pages | 101-121 | |
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| Abstract | This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field. | ||||
| Address | September 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | ISRL | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-031-06306-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ VSC2022 | Serial | 3811 | ||
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| Author | Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu | ||||
| Title | Waste Classification with Small Datasets and Limited Resources | Type | Book Chapter | ||
| Year | 2022 | Publication | ICT Applications for Smart Cities. Intelligent Systems Reference Library | Abbreviated Journal | |
| Volume | 224 | Issue | Pages | 185-203 | |
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| Abstract | Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. | ||||
| Address | September 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | ISRL | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-031-06306-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3813 | ||
| Permanent link to this record | |||||
| Author | Michael Teutsch; Angel Sappa; Riad I. Hammoud | ||||
| Title | Cross-Spectral Image Processing | Type | Book Chapter | ||
| Year | 2022 | Publication | Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 23-34 | ||
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| Abstract | Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. | ||||
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| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-031-00698-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ TSH2022b | Serial | 3805 | ||
| Permanent link to this record | |||||
| Author | Michael Teutsch; Angel Sappa; Riad I. Hammoud | ||||
| Title | Detection, Classification, and Tracking | Type | Book Chapter | ||
| Year | 2022 | Publication | Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 35-58 | ||
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| Abstract | Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-031-00698-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ TSH2022c | Serial | 3806 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
| Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
| Volume | 11009 | Issue | Pages | 81-95 | |
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
| Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
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| Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-3-030-02283-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
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| Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | emotion; computer vision; databases | ||||
| Abstract | Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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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 |
978-1-78923-316-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HUPBA; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
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| Author | Fadi Dornaika; Bogdan Raducanu | ||||
| Title | Subtle Facial Expression Recognition in Still Images and Videos | Type | Book Chapter | ||
| Year | 2011 | Publication | Advances in Face Image Analysis: Techniques and Technologies | Abbreviated Journal | |
| Volume | Issue | 14 | Pages | 259-277 | |
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| Abstract | This chapter addresses the recognition of basic facial expressions. It has three main contributions. First, the authors introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. they represent the learned facial actions associated with different facial expressions by time series. Two dynamic recognition schemes are proposed: (1) the first is based on conditional predictive models and on an analysis-synthesis scheme, and (2) the second is based on examples allowing straightforward use of machine learning approaches. Second, the authors propose an efficient recognition scheme based on the detection of keyframes in videos. Third, the authors compare the dynamic scheme with a static one based on analyzing individual snapshots and show that in general the former performs better than the latter. The authors then provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). | ||||
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| Publisher | IGI-Global | Place of Publication | New York, USA | Editor | Yu-Jin Zhang |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-1-6152-0991-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ DoR2011 | Serial | 1751 | ||
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| Author | Fernando Vilariño; Debora Gil; Petia Radeva | ||||
| Title | A Novel FLDA Formulation for Numerical Stability Analysis | Type | Book Chapter | ||
| Year | 2004 | Publication | Recent Advances in Artificial Intelligence Research and Development | Abbreviated Journal | |
| Volume | 113 | Issue | Pages | 77-84 | |
| Keywords | Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision | ||||
| Abstract | Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision. | ||||
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| Publisher | IOS Press | Place of Publication | Editor | J. Vitrià, P. Radeva and I. Aguiló | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN |
978-1-58603-466-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MV;IAM;MILAB;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ VGR2004 | Serial | 1663 | ||
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