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Author | Mario Rojas; David Masip; Jordi Vitria | ||||
Title | Automatic Detection of Facial Feature Points via HOGs and Geometric Prior Models | Type | Conference Article | ||
Year | 2011 | Publication | 5th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 6669 | Issue | Pages | 371-378 | |
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Abstract | Most applications dealing with problems involving the face require a robust estimation of the facial salient points. Nevertheless, this estimation is not usually an automated preprocessing step in applications dealing with facial expression recognition. In this paper we present a simple method to detect facial salient points in the face. It is based on a prior Point Distribution Model and a robust object descriptor. The model learns the distribution of the points from the training data, as well as the amount of variation in location each point exhibits. Using this model, we reduce the search areas to look for each point. In addition, we also exploit the global consistency of the points constellation, increasing the detection accuracy. The method was tested on two separate data sets and the results, in some cases, outperform the state of the art. | ||||
Address | Las Palmas de Gran Canaria. Spain | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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ISSN | 0302-9743 | ISBN | 978-3-642-21256-7 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ RMV2011a | Serial | 1731 | ||
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Author | Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca | ||||
Title | Toward Real-Time Pedestrian Detection Based on a Deformable Template Model | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 1 | Pages | 355-364 |
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Abstract | Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. | ||||
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ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE; 601.213; 600.078 | Approved | no | ||
Call Number | PGH2014 | Serial | 2350 | ||
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Author | Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera | ||||
Title | E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 10 | Issue | Pages | 7489-7503 | |
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Abstract | More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches. |
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Notes | IAM; 600.139; 600.118; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GHE2022 | Serial | 3721 | ||
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Author | Shida Beigpour | ||||
Title | Illumination and object reflectance modeling | Type | Book Whole | ||
Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information. | ||||
Address | Barcelona | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ernest Valveny | |
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Bei2013 | Serial | 2267 | ||
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Author | Akhil Gurram; Antonio Lopez | ||||
Title | On the Metrics for Evaluating Monocular Depth Estimation | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ GuL2023 | Serial | 3867 | ||
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Author | Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez | ||||
Title | Distributed Learning and Inference with Compressed Images | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 30 | Issue | Pages | 3069 - 3083 | |
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Abstract | Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. | ||||
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Notes | LAMP; ADAS; 600.120; 600.118 | Approved | no | ||
Call Number | Admin @ si @ KEH2021 | Serial | 3543 | ||
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Author | Emanuel Indermühle; Volkmar Frinken; Horst Bunke | ||||
Title | Mode Detection in Online Handwritten Documents using BLSTM Neural Networks | Type | Conference Article | ||
Year | 2012 | Publication | 13th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 302-307 | ||
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Abstract | Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data. | ||||
Address | Bari, italy | ||||
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ISSN | ISBN | 978-1-4673-2262-1 | Medium | ||
Area | Expedition | Conference | ICFHR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ IFB2012 | Serial | 2056 | ||
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Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
Title | Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume | Issue | Pages | 181 - 185 | ||
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Abstract | Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. | ||||
Address | Tours; France; April 2014 | ||||
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ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 601.223; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RCO2014a | Serial | 2545 | ||
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Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
Title | Normalisation et validation d'images de documents capturées en mobilité | Type | Conference Article | ||
Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
Volume | Issue | Pages | 109-124 | ||
Keywords | mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction | ||||
Abstract | Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
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Address | Nancy; France; March 2014 | ||||
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Area | Expedition | Conference | CIFED | ||
Notes | DAG; 601.223; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RCO2014b | Serial | 2546 | ||
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Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
Title | Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses | Type | Conference Article | ||
Year | 2015 | Publication | 16th IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 452-460 | ||
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Abstract | Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis. | ||||
Address | Santiago de Chile; December 2015 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | LAMP; 600.068; 600.072; | Approved | no | ||
Call Number | Admin @ si @ TRM2015 | Serial | 2722 | ||
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Author | C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia | ||||
Title | Modelling symmetry perception with banks of quadrature convolutional Gabor kernels | Type | Conference Article | ||
Year | 2019 | Publication | 42nd edition of the European Conference on Visual Perception | Abbreviated Journal | |
Volume | Issue | Pages | 224-224 | ||
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Abstract | Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation. | ||||
Address | Leuven; Belgium; August 2019 | ||||
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Area | Expedition | Conference | ECVP | ||
Notes | NEUROBIT; 600.128 | Approved | no | ||
Call Number | Admin @ si @ POA2019 | Serial | 3371 | ||
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Author | Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez | ||||
Title | A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection | Type | Journal Article | ||
Year | 2023 | Publication | Electronics | Abbreviated Journal | ELEC |
Volume | 12 | Issue | 18 | Pages | 3947 |
Keywords | micro-expression spotting; sliding window; key frame extraction | ||||
Abstract | Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ FAH2023 | Serial | 3864 | ||
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Author | Idoia Ruiz | ||||
Title | Deep Metric Learning for re-identification, tracking and hierarchical novelty detection | Type | Book Whole | ||
Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Metric learning refers to the problem in machine learning of learning a distance or similarity measurement to compare data. In particular, deep metric learning involves learning a representation, also referred to as embedding, such that in the embedding space data samples can be compared based on the distance, directly providing a similarity measure. This step is necessary to perform several tasks in computer vision. It allows to perform the classification of images, regions or pixels, re-identification, out-of-distribution detection, object tracking in image sequences and any other task that requires computing a similarity score for their solution. This thesis addresses three specific problems that share this common requirement. The first one is person re-identification. Essentially, it is an image retrieval task that aims at finding instances of the same person according to a similarity measure. We first compare in terms of accuracy and efficiency, classical metric learning to basic deep learning based methods for this problem. In this context, we also study network distillation as a strategy to optimize the trade-off between accuracy and speed at inference time. The second problem we contribute to is novelty detection in image classification. It consists in detecting samples of novel classes, i.e. never seen during training. However, standard novelty detection does not provide any information about the novel samples besides they are unknown. Aiming at more informative outputs, we take advantage from the hierarchical taxonomies that are intrinsic to the classes. We propose a metric learning based approach that leverages the hierarchical relationships among classes during training, being able to predict the parent class for a novel sample in such hierarchical taxonomy. Our third contribution is in multi-object tracking and segmentation. This joint task comprises classification, detection, instance segmentation and tracking. Tracking can be formulated as a retrieval problem to be addressed with metric learning approaches. We tackle the existing difficulty in academic research that is the lack of annotated benchmarks for this task. To this matter, we introduce the problem of weakly supervised multi-object tracking and segmentation, facing the challenge of not having available ground truth for instance segmentation. We propose a synergistic training strategy that benefits from the knowledge of the supervised tasks that are being learnt simultaneously. | ||||
Address | July, 2022 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Joan Serrat | ||
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ISSN | ISBN | 978-84-124793-4-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Rui2022 | Serial | 3717 | ||
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Author | Lu Yu; Vacit Oguz Yazici; Xialei Liu; Joost Van de Weijer; Yongmei Cheng; Arnau Ramisa | ||||
Title | Learning Metrics from Teachers: Compact Networks for Image Embedding | Type | Conference Article | ||
Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2907-2916 | ||
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Abstract | Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the
communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semisupervised learning and cross quality distillation. |
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Address | Long beach; California; june 2019 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | LAMP; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YYL2019 | Serial | 3281 | ||
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Author | Carles Onielfa; Carles Casacuberta; Sergio Escalera | ||||
Title | Influence in Social Networks Through Visual Analysis of Image Memes | Type | Conference Article | ||
Year | 2022 | Publication | Artificial Intelligence Research and Development | Abbreviated Journal | |
Volume | 356 | Issue | Pages | 71-80 | |
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Abstract | Memes evolve and mutate through their diffusion in social media. They have the potential to propagate ideas and, by extension, products. Many studies have focused on memes, but none so far, to our knowledge, on the users that post them, their relationships, and the reach of their influence. In this article, we define a meme influence graph together with suitable metrics to visualize and quantify influence between users who post memes, and we also describe a process to implement our definitions using a new approach to meme detection based on text-to-image area ratio and contrast. After applying our method to a set of users of the social media platform Instagram, we conclude that our metrics add information to already existing user characteristics. | ||||
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Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ OCE2022 | Serial | 3799 | ||
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