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| Author | Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GRK2017 | Serial | 2999 | ||
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| Author | E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara | ||||
| Title | Benchmarking Keypoint Filtering Approaches for Document Image Matching | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial not only to processing speed but also to accuracy. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RCR2017 | Serial | 3000 | ||
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| Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo | ||||
| Title | Automatic Static/Variable Content Separation in Administrative Document Images | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ ART2017 | Serial | 3001 | ||
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| Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
| Title | Learning to Learn from Web Data through Deep Semantic Embeddings | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 11134 | Issue | Pages | 514-529 | |
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| Abstract | In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. | ||||
| Address | Munich; Alemanya; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 600.129; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GGG2018a | Serial | 3175 | ||
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| Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny | ||||
| Title | Don't only Feel Read: Using Scene text to understand advertisements | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. | ||||
| Address | Salt Lake City; Utah; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DGV2018 | Serial | 3551 | ||
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| Author | Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo | ||||
| Title | Reading Text in the Wild from Compressed Images | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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| Abstract | Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV - EPIC | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBS2017 | Serial | 3006 | ||
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| Author | Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai | ||||
| Title | WordFences: Text Localization and Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
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| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ PAE2017 | Serial | 3007 | ||
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| Author | Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats | ||||
| Title | Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription | Type | Journal | ||
| Year | 2017 | Publication | Journal of Medical Engineering & Technology | Abbreviated Journal | JMET |
| Volume | 41 | Issue | 6 | Pages | 486-497 |
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| Abstract | INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question. OBJECTIVES: To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination. METHODS: Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows. RESULTS: A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation). CONCLUSIONS: Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPB2017 | Serial | 3010 | ||
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| Author | Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Audio-Visual Emotion Recognition in Video Clips | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 10 | Issue | 1 | Pages | 60-75 |
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| Abstract | This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. | ||||
| Address | 1 Jan.-March 2019 | ||||
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| Notes | HUPBA; 602.143; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ NMN2017 | Serial | 3011 | ||
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| Author | Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People: A Review of Events and Resources | Type | Conference Article | ||
| Year | 2017 | Publication | 30th International Joint Conference on Neural Networks | Abbreviated Journal | |
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| Abstract | This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. | ||||
| Address | Anchorage; Alaska; USA; May 2017 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA; 602.143 | Approved | no | ||
| Call Number | Admin @ si @ EBE2017 | Serial | 3012 | ||
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| Author | Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe | ||||
| Title | Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks. |
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| Address | Washington;USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ ATE2017 | Serial | 3013 | ||
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| Author | Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera | ||||
| Title | Error-Correcting Factorization | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 40 | Issue | Pages | 2388-2401 | |
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| Abstract | Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BPT2018 | Serial | 3015 | ||
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| Author | Meysam Madadi | ||||
| Title | Human Segmentation, Pose Estimation and Applications | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Automatic analyzing humans in photographs or videos has great potential applications in computer vision, including medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task.
In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. nonrigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures. |
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| Address | October 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Jordi Gonzalez | |
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| ISSN | ISBN | 978-84-945373-3-2 | Medium | ||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ Mad2017 | Serial | 3017 | ||
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| Author | Onur Ferhat | ||||
| Title | Analysis of Head-Pose Invariant, Natural Light Gaze Estimation Methods | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Eye tracker devices have traditionally been only used inside laboratories, requiring trained professionals and elaborate setup mechanisms. However, in the recent years the scientific work on easier–to–use eye trackers which require no special hardware—other than the omnipresent front facing cameras in computers, tablets, and mobiles—is aiming at making this technology common–place. These types of trackers have several extra challenges that make the problem harder, such as low resolution images provided by a regular webcam, the changing ambient lighting conditions, personal appearance differences, changes in head pose, and so on. Recent research in the field has focused on all these challenges in order to provide better gaze estimation performances in a real world setup.
In this work, we aim at tackling the gaze tracking problem in a single camera setup. We first analyze all the previous work in the field, identifying the strengths and weaknesses of each tried idea. We start our work on the gaze tracker with an appearance–based gaze estimation method, which is the simplest idea that creates a direct mapping between a rectangular image patch extracted around the eye in a camera image, and the gaze point (or gaze direction). Here, we do an extensive analysis of the factors that affect the performance of this tracker in several experimental setups, in order to address these problems in future works. In the second part of our work, we propose a feature–based gaze estimation method, which encodes the eye region image into a compact representation. We argue that this type of representation is better suited to dealing with head pose and lighting condition changes, as it both reduces the dimensionality of the input (i.e. eye image) and breaks the direct connection between image pixel intensities and the gaze estimation. Lastly, we use a face alignment algorithm to have robust face pose estimation, using a 3D model customized to the subject using the tracker. We combine this with a convolutional neural network trained on a large dataset of images to build a face pose invariant gaze tracker. |
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| Address | September 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Fernando Vilariño | |
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| ISSN | ISBN | 978-84-945373-5-6 | Medium | ||
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| Notes | MV | Approved | no | ||
| Call Number | Admin @ si @ Fer2017 | Serial | 3018 | ||
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| Author | Arash Akbarinia | ||||
| Title | Computational Model of Visual Perception: From Colour to Form | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The original idea of this project was to study the role of colour in the challenging task of object recognition. We started by extending previous research on colour naming showing that it is feasible to capture colour terms through parsimonious ellipsoids. Although, the results of our model exceeded state-of-the-art in two benchmark datasets, we realised that the two phenomena of metameric lights and colour constancy must be addressed prior to any further colour processing. Our investigation of metameric pairs reached the conclusion that they are infrequent in real world scenarios. Contrary to that, the illumination of a scene often changes dramatically. We addressed this issue by proposing a colour constancy model inspired by the dynamical centre-surround adaptation of neurons in the visual cortex. This was implemented through two overlapping asymmetric Gaussians whose variances and heights are adjusted according to the local contrast of pixels. We complemented this model with a generic contrast-variant pooling mechanism that inversely connect the percentage of pooled signal to the local contrast of a region. The results of our experiments on four benchmark datasets were indeed promising: the proposed model, although simple, outperformed even learning-based approaches in many cases. Encouraged by the success of our contrast-variant surround modulation, we extended this approach to detect boundaries of objects. We proposed an edge detection model based on the first derivative of the Gaussian kernel. We incorporated four types of surround: full, far, iso- and orthogonal-orientation. Furthermore, we accounted for the pooling mechanism at higher cortical areas and the shape feedback sent to lower areas. Our results in three benchmark datasets showed significant improvement over non-learning algorithms.
To summarise, we demonstrated that biologically-inspired models offer promising solutions to computer vision problems, such as, colour naming, colour constancy and edge detection. We believe that the greatest contribution of this Ph.D dissertation is modelling the concept of dynamic surround modulation that shows the significance of contrast-variant surround integration. The models proposed here are grounded on only a portion of what we know about the human visual system. Therefore, it is only natural to complement them accordingly in future works. |
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| Address | October 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | C. Alejandro Parraga | |
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| ISSN | ISBN | 978-84-945373-4-9 | Medium | ||
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| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ Akb2017 | Serial | 3019 | ||
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