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| Author | Adarsh Tiwari; Sanket Biswas; Josep Llados | ||||
| Title | Can Pre-trained Language Models Help in Understanding Handwritten Symbols? | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14193 | Issue | Pages | 199–211 | |
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| Abstract | The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. | ||||
Address  |
San Jose; CA; USA; August 2023 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TBL2023 | Serial | 3908 | ||
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| Author | Mickael Coustaty; Alicia Fornes | ||||
| Title | Document Analysis and Recognition – ICDAR 2023 Workshops | Type | Book Whole | ||
| Year | 2023 | Publication | Document Analysis and Recognition – ICDAR 2023 Workshops | Abbreviated Journal | |
| Volume | 14194 | Issue | 2 | Pages | |
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| Address |
San Jose; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ CoF2023 | Serial | 3852 | ||
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| Author | H. Emrah Tasli; Cevahir Çigla; Theo Gevers; A. Aydin Alatan | ||||
| Title | Super pixel extraction via convexity induced boundary adaptation | Type | Conference Article | ||
| Year | 2013 | Publication | 14th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
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| Abstract | This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed. | ||||
| Address |
San Jose; USA; July 2013 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1945-7871 | ISBN | Medium | ||
| Area | Expedition | Conference | ICME | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ TÇG2013 | Serial | 2367 | ||
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| Author | Laura Igual; Santiago Segui; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| Title | Sparse Bayesian Feature Selection Applied to Intestinal Motility Analysis | Type | Conference Article | ||
| Year | 2007 | Publication | XVI Congreso Argentino de Bioingenieria | Abbreviated Journal | |
| Volume | Issue | Pages | 467–470 | ||
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| Address |
San Juan (Argentina) | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | SABI | ||
| Notes | MILAB;OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ISV2007b | Serial | 896 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computational modelingof visual attention: What do we know from physiology and psychophysics? | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Latest computer vision architectures use a chain of feedforward computations, mainly optimizing artificial neural networks for very specific tasks. Although their impressive performance (i.e. in saliency) using real image datasets, these models do not follow several biological principles of the human visual system (e.g. feedback and horizontal connections in cortex) and are unable to predict several visual tasks simultaneously. In this study we present biologically plausible computations from the early stages of the human visual system (i.e. retina and lateral geniculate nucleus) and lateral connections in V1. Despite the simplicity of these processes and without any type of training or optimization, simulations of firing-rate dynamics of V1 are able to predict bottom-up visual attention at distinct contexts (shown previously as well to predict visual discomfort, brightness and chromatic induction). We also show functional top-down selection mechanisms as feedback inhibition projections (i.e. prefrontal cortex for search/task-based attention and parietal area for inhibition of return). Distinct saliency model predictions are tested with eye tracking datasets in free-viewing and visual search tasks, using real images and synthetically-generated patterns. Results on predicting saliency and scanpaths show that artificial models do not outperform biologically-inspired ones (specifically for datasets that lack of common endogenous biases found in eye tracking experimentation), as well as, do not correctly predict contrast sensitivities in pop-out stimulus patterns. This work remarks the importance of considering biological principles of the visual system for building models that reproduce this (and any other) visual effects. | ||||
| Address |
San Lorenzo El Escorial; July 2019 | ||||
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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 | CIP | ||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BeO2019b | Serial | 3374 | ||
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| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo | ||||
| Title | Measuring bottom-up visual attention in eye tracking experimentation with synthetic images | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. | ||||
| Address |
San Lorenzo El Escorial; July 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIP | ||
| Notes | NEUROBIT; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019c | Serial | 3375 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
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| Address |
San Sebastian; Spain; May 2017 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ECMSM | ||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
| Permanent link to this record | |||||
| Author | Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point that minimizes the vacuum force needed to guarantee a grasp. Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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| Address |
San Sebastian; Spain; May 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ECMSM | ||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VIS2017 | Serial | 2917 | ||
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| Author | A. Martinez; Jordi Vitria; S. Sampayo | ||||
| Title | Atlas: a Hexapod driven by a Neural Network. | Type | Miscellaneous | ||
| Year | 1995 | Publication | Abbreviated Journal | ||
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| Address |
Sant Feliu de Guixols, Spain. | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MVS1995 | Serial | 125 | ||
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| Author | Debora Gil; Agnes Borras; Sergio Vera; Miguel Angel Gonzalez Ballester | ||||
| Title | A Validation Benchmark for Assessment of Medial Surface Quality for Medical Applications | Type | Conference Article | ||
| Year | 2013 | Publication | 9th International Conference on Computer Vision Systems | Abbreviated Journal | |
| Volume | 7963 | Issue | Pages | 334-343 | |
| Keywords | Medial Surfaces; Shape Representation; Medical Applications; Performance Evaluation | ||||
| Abstract | Confident use of medial surfaces in medical decision support systems requires evaluating their quality for detecting pathological deformations and describing anatomical volumes. Validation in the medical imaging field is a challenging task mainly due to the difficulties for getting consensual ground truth. In this paper we propose a validation benchmark for assessing medial surfaces in the context of medical applications. Our benchmark includes a home-made database of synthetic medial surfaces and volumes and specific scores for evaluating surface accuracy, its stability against volume deformations and its capabilities for accurate reconstruction of anatomical volumes. | ||||
| Address |
Sant Petersburg; Russia; July 2013 | ||||
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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 | 978-3-642-39401-0 | Medium | |
| Area | Expedition | Conference | ICVS | ||
| Notes | IAM; 600.044; 600.060 | Approved | no | ||
| Call Number | Admin @ si @ GBV2013 | Serial | 2300 | ||
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| Author | Mario Rojas; David Masip; Jordi Vitria | ||||
| Title | Predicting Dominance Judgements Automatically: A Machine Learning Approach. | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE International Workshop on Social Behavior Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 939-944 | ||
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| Abstract | The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task. | ||||
| Address |
Santa Barbara, CA | ||||
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| ISSN | ISBN | 978-1-4244-9140-7 | Medium | ||
| Area | Expedition | Conference | SBA | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RMV2011b | Serial | 1760 | ||
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| Author | Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella | ||||
| Title | Hierarchical CRF with product label spaces for parts-based Models | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 657-664 | ||
| Keywords | Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features | ||||
| Abstract | Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. | ||||
| Address |
Santa Barbara, CA, USA, 2011 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RBT2011 | Serial | 1862 | ||
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| Author | Antonio Lopez; David Lloret; Joan Serrat | ||||
| Title | Creaseness measures for CT and MR image registration. | Type | Miscellaneous | ||
| Year | 1998 | Publication | CVPR’98 , IEEE Computer Society, pgs.694–699 | Abbreviated Journal | |
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| Abstract | Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy | ||||
| Address |
Santa Barbara, USA. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LLS1998a | Serial | 11 | ||
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| Author | Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure | ||||
| Title | GPU-accelerated real-time stixel computation | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1054-1062 | ||
| Keywords | Autonomous Driving; GPU; Stixel | ||||
| Abstract | The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energyefficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card. | ||||
| Address |
Santa Rosa; CA; USA; March 2017 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ HEV2017b | Serial | 2812 | ||
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| Author | Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen | ||||
| Title | Bandwidth Limited Object Recognition in High Resolution Imagery | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Winter conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. | ||||
| Address |
Santa Rosa; CA; USA; March 2017 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACV | ||
| Notes | LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LBW2017 | Serial | 2973 | ||
| Permanent link to this record | |||||