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Author | Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez | ||||
Title | Road Scene Segmentation from a Single Image | Type | Conference Article | ||
Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 7578 | Issue | VII | Pages ![]() |
376-389 |
Keywords | road detection | ||||
Abstract | Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images. From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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Address | Florence, Italy | ||||
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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 | 978-3-642-33785-7 | Medium | |
Area | Expedition | Conference | ECCV | ||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGL2012; ADAS @ adas @ agl2012a | Serial | 2022 | ||
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Author | Albert Gordo; Jaume Gibert; Ernest Valveny; Marçal Rusiñol | ||||
Title | A Kernel-based Approach to Document Retrieval | Type | Conference Article | ||
Year | 2010 | Publication | 9th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
377–384 | ||
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Abstract | In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval. | ||||
Address | Boston; USA; | ||||
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Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-60558-773-8 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ GGV2010 | Serial | 1431 | ||
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Author | Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari | ||||
Title | Automatic Recognition of Facial Displays of Unfelt Emotions | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 12 | Issue | 2 | Pages ![]() |
377 - 390 |
Keywords | |||||
Abstract | Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase. | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ KCO2021 | Serial | 3658 | ||
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Author | David Berga; Xavier Otazu | ||||
Title | A neurodynamic model of saliency prediction in v1 | Type | Journal Article | ||
Year | 2022 | Publication | Neural Computation | Abbreviated Journal | NEURALCOMPUT |
Volume | 34 | Issue | 2 | Pages ![]() |
378-414 |
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Abstract | Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex. | ||||
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Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
Call Number | Admin @ si @ BeO2022 | Serial | 3696 | ||
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Author | F.X. Perez; F. Javier Sanchez; Xavier Binefa; Xavier Roca; Jordi Vitria; Juan J. Villanueva | ||||
Title | A mathematical morphology-based system for IC´s inspection and analysis. | Type | Conference Article | ||
Year | 1993 | Publication | Institute of Physics Conferences Series | Abbreviated Journal | |
Volume | 135 | Issue | 10 | Pages ![]() |
381–384 |
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Address | Bristol | ||||
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Publisher | Institute of Physics | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Notes | MV;OR;ISE | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ PSB1993; IAM @ iam @ PSB1993 | Serial | 150 | ||
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Author | Jaume Amores | ||||
Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
Year | 2015 | Publication | Knowledge and Information Systems | Abbreviated Journal | KAIS |
Volume | 42 | Issue | 2 | Pages ![]() |
381-407 |
Keywords | Multi-instance learning; Codebook; Bag of words | ||||
Abstract | While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. | ||||
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Publisher | Springer London | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0219-1377 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Amo2015 | Serial | 2383 | ||
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Author | Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12822 | Issue | Pages ![]() |
381–395 | |
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Abstract | In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | 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 | ICDAR | ||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ RMG2021 | Serial | 3572 | ||
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Author | Eduard Vazquez; Ramon Baldrich | ||||
Title | Colour Image Segmentation in Presence of Shadows | Type | Conference Article | ||
Year | 2008 | Publication | 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
383–387 | ||
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Address | Terrassa (Spain) | ||||
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Area | Expedition | Conference | CGIV08 | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ VaB2008 | Serial | 966 | ||
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Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
383-387 | ||
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Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
Address | Virtual; October 2020 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICRM | ||
Notes | Approved | no | |||
Call Number | Admin @ si @ CGR2020 | Serial | 3529 | ||
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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 | Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
Title | Growing Algorithm for Intersection Detection (GRAID) in branching patterns | Type | Journal Article | ||
Year | 2015 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 26 | Issue | 2 | Pages ![]() |
387-400 |
Keywords | Bifurcation ; Crossroad; Intersection ;Retina ; Vessel | ||||
Abstract | Analysis of branching structures represents a very important task in fields such as medical diagnosis, road detection or biometrics. Detecting intersection landmarks Becomes crucial when capturing the structure of a branching pattern. We present a very simple geometrical model to describe intersections in branching structures based on two conditions: Bounded Tangency condition (BT) and Shortest Branch (SB) condition. The proposed model precisely sets a geometrical characterization of intersections and allows us to introduce a new unsupervised operator for intersection extraction. We propose an implementation that handles the consequences of digital domain operation that,unlike existing approaches, is not restricted to a particular scale and does not require the computation of the thinned pattern. The new proposal, as well as other existing approaches in the bibliography, are evaluated in a common framework for the first time. The performance analysis is based on two manually segmented image data sets: DRIVE retinal image database and COLON-VESSEL data set, a newly created data set of vascular content in colonoscopy frames. We have created an intersection landmark ground truth for each data set besides comparing our method in the only existing ground truth. Quantitative results confirm that we are able to outperform state-of-the-art performancelevels with the advantage that neither training nor parameter tuning is needed. | ||||
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Notes | ;SIAI | Approved | no | ||
Call Number | Admin @ si @MBS2015 | Serial | 2777 | ||
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Author | X. Binefa; F. Javier Sanchez; F.X. Perez; Xavier Roca; Jordi Vitria; Juan J. Villanueva | ||||
Title | Using defocus in optical inspection of integrated circuits | Type | Conference Article | ||
Year | 1993 | Publication | Institute of Physics Conferences Series | Abbreviated Journal | |
Volume | 135 | Issue | 10 | Pages ![]() |
389-392 |
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Address | Bristol | ||||
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Publisher | Institute of Physics | Place of Publication | Editor | ||
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Notes | MV;OR;ISE | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BSP1993; IAM @ iam @ BSP1993 | Serial | 151 | ||
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Author | Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester | ||||
Title | Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities | Type | Journal Article | ||
Year | 2017 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
Volume | 35 | Issue | Pages ![]() |
390-402 | |
Keywords | Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points | ||||
Abstract | Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a condent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | IAM; 600.060; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GVB2017 | Serial | 2775 | ||
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Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Vision | Abbreviated Journal | JV |
Volume | 17 | Issue | 10 | Pages ![]() |
390-390 |
Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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Address | Florida, USA; May 2017 | ||||
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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Author | Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim | ||||
Title | Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 | Type | Conference Article | ||
Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
390-398 | ||
Keywords | Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers | ||||
Abstract | This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. | ||||
Address | New Orleans; USA; June 2022 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; no menciona | Approved | no | ||
Call Number | Admin @ si @ RMV2022 | Serial | 3774 | ||
Permanent link to this record |