| Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–15] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez | ||||
| Title | Biometric Recognition Based on Line Shape Descriptors | Type | Book Chapter | ||
| Year | 2006 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 3926 | Issue | Pages | 346–357, | |
| Keywords | |||||
Abstract  |
Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Link | Place of Publication | Editor | ||
| 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 | DAG @ dag @ CSL2006 | Serial | 685 | ||
| Permanent link to this record | |||||
| Author | Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | An Overview of Symbol Recognition | Type | Book Chapter | ||
| Year | 2014 | Publication | Handbook of Document Image Processing and Recognition | Abbreviated Journal | |
| Volume | D | Issue | Pages | 523-551 | |
| Keywords | Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting | ||||
| Abstract |
According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer London | Place of Publication | Editor | D. Doermann; K. Tombre | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-0-85729-858-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ TaT2014 | Serial | 2489 | ||
| 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 | ||
| Keywords | |||||
| 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. | ||||
| Address | |||||
| 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 @ TSH2022b | Serial | 3805 | ||
| Permanent link to this record | |||||
| Author | Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny | ||||
| Title | Notation-invariant patch-based wall detector in architectural floor plans | Type | Book Chapter | ||
| Year | 2013 | Publication | Graphics Recognition. New Trends and Challenges | Abbreviated Journal | |
| Volume | 7423 | Issue | Pages | 79--88 | |
| Keywords | |||||
| Abstract |
Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. | ||||
| 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 | 978-3-642-36823-3 | Medium | |
| Area | Expedition | Conference | |||
| Notes | DAG; 600.045; 600.056; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ HMS2013 | Serial | 2322 | ||
| 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 | ||
| Keywords | |||||
| 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. | ||||
| Address | |||||
| 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 | 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 | |
| Keywords | |||||
| 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 | Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig | ||||
| Title | Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis | Type | Book Chapter | ||
| Year | 2015 | Publication | Artificial Intelligence Research and Development | Abbreviated Journal | |
| Volume | 277 | Issue | Pages | 247 - 256 | |
| Keywords | |||||
| Abstract |
Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | IOS Press | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Frontiers in Artificial Intelligence and Applications | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @TVR2015 | Serial | 2780 | ||
| Permanent link to this record | |||||
| 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 | |
| Keywords | |||||
| 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 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Oriol Rodriguez-Leor; Petia Radeva; Aura Hernandez-Sabate | ||||
| Title | Assessing Artery Motion Compensation in IVUS | Type | Book Chapter | ||
| Year | 2007 | Publication | Computer Analysis Of Images And Patterns | Abbreviated Journal | LNCS |
| Volume | 4673 | Issue | Pages | 213-220 | |
| Keywords | validation standards; quality measures; IVUS motion compensation; conservation laws; Fourier development | ||||
| Abstract |
Cardiac dynamics suppression is a main issue for visual improvement and computation of tissue mechanical properties in IntraVascular UltraSound (IVUS). Although in recent times several motion compensation techniques have arisen, there is a lack of objective evaluation of motion reduction in in vivo pullbacks. We consider that the assessment protocol deserves special attention for the sake of a clinical applicability as reliable as possible. Our work focuses on defining a quality measure and a validation protocol assessing IVUS motion compensation. On the grounds of continuum mechanics laws we introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; while results in in vivo pullbacks show its reliability in clinical cases. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springerlink | Place of Publication | Heidelberg | Editor | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-540-74271-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM;MILAB | Approved | no | ||
| Call Number | IAM @ iam @ GRR2007 | Serial | 1540 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Deep learning-based vegetation index estimation | Type | Book Chapter | ||
| Year | 2021 | Publication | Generative Adversarial Networks for Image-to-Image Translation | Abbreviated Journal | |
| Volume | Issue | Pages | 205-234 | ||
| Keywords | |||||
| Abstract |
Chapter 9 | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier | Place of Publication | Editor | A.Solanki; A.Nayyar; M.Naved | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2021a | Serial | 3578 | ||
| Permanent link to this record | |||||
| 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 | ||
| Keywords | |||||
| 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. |
||||
| Address | |||||
| 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 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal | ||||
| Title | 3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos | Type | Book Chapter | ||
| Year | 2015 | Publication | Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
| Volume | 9515 | Issue | Pages | 140-152 | |
| Keywords | Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds | ||||
| Abstract |
Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response. Spatio-temporal stability is achieved by extracting 3D watershed regions on the temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection. |
||||
| Address | |||||
| 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 | ISBN | Medium | |||
| Area | Expedition | Conference | CARE | ||
| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ GSF2015 | Serial | 2733 | ||
| Permanent link to this record | |||||
| Author | Pedro Herruzo; Marc Bolaños; Petia Radeva | ||||
| Title | Can a CNN Recognize Catalan Diet? | Type | Book Chapter | ||
| Year | 2016 | Publication | AIP Conference Proceedings | Abbreviated Journal | |
| Volume | 1773 | Issue | Pages | ||
| Keywords | |||||
| Abstract |
CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
||||
| Address | |||||
| 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 | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ HBR2016 | Serial | 2837 | ||
| Permanent link to this record | |||||
| Author | Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva | ||||
| Title | Towards Egocentric Person Re-identification and Social Pattern Analysis. | Type | Book Chapter | ||
| Year | 2019 | Publication | Frontiers in Artificial Intelligence and Applications | Abbreviated Journal | |
| Volume | 310 | Issue | Pages | 203 - 211 | |
| Keywords | |||||
| Abstract |
CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation. |
||||
| Address | |||||
| 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 | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TCP2019 | Serial | 3377 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Marti Soler; Stephane Ayache; Umut Guçlu; Jun Wan; Meysam Madadi; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People: Inpainting and Denoising Challenges | Type | Book Chapter | ||
| Year | 2019 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
| Volume | Issue | Pages | 23-44 | ||
| Keywords | |||||
| Abstract |
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined. | ||||
| Address | |||||
| 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 | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ESA2019 | Serial | 3327 | ||
| Permanent link to this record | |||||