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| Author | Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven | ||||
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End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks | Type | Miscellaneous | ||
| Year | 2017 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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| Notes | HuPBA; ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ GGM2017 | Serial | 2932 | ||
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| Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez | ||||
| Title |
Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 34 | Issue | 5 | Pages | 827-837 |
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| Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | ||||
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| Notes | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ RVB2017 | Serial | 2892 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition | Abbreviated Journal | |
| Volume | 10310 | Issue | Pages | 107-117 | |
| Keywords | Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching | ||||
| Abstract | Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. | ||||
| Address | Anacapri; Italy; May 2017 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | Pasquale Foggia; Cheng-Lin Liu; Mario Vento | |
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| Area | Expedition | Conference | GbRPR | ||
| Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RLF2017a | Serial | 2951 | ||
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| Author | Bojana Gajic; Eduard Vazquez; Ramon Baldrich | ||||
| Title |
Evaluation of Deep Image Descriptors for Texture Retrieval | Type | Conference Article | ||
| Year | 2017 | Publication | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) | Abbreviated Journal | |
| Volume | Issue | Pages | 251-257 | ||
| Keywords | Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation | ||||
| Abstract | The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures. |
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| Address | Porto, Portugal; 27 February – 1 March 2017 | ||||
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| Area | Expedition | Conference | VISIGRAPP | ||
| Notes | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3710 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title |
Evaluation of Texture Descriptors for Validation of Counterfeit Documents | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1237-1242 | ||
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| Abstract | This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance. | ||||
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| ISSN | 2379-2140 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 601.269; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRL2017 | Serial | 3092 | ||
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| Author | Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza | ||||
| Title |
Evolving weighting schemes for the Bag of Visual Words | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 925–939 |
| Keywords | Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision | ||||
| Abstract | The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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| Publisher | Place of Publication | Editor | Springer | ||
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| Notes | HUPBA;MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPE2017 | Serial | 2743 | ||
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| Author | Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera | ||||
| Title |
Exploiting feature representations through similarity learning and ranking aggregation for person re-identification | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset. | ||||
| Address | Washington; DC; USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; 602.143 | Approved | no | ||
| Call Number | Admin @ si @ JBE2017 | Serial | 2923 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title |
Flowchart Recognition in Patent Information Retrieval | Type | Book Chapter | ||
| Year | 2017 | Publication | Current Challenges in Patent Information Retrieval | Abbreviated Journal | |
| Volume | 37 | Issue | Pages | 351-368 | |
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | M. Lupu; K. Mayer; N. Kando; A.J. Trippe | |
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RuL2017 | Serial | 2896 | ||
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| Author | Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros | ||||
| Title |
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example | Type | Book Chapter | ||
| Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
| Volume | Issue | 13 | Pages | 243-258 | |
| Keywords | Domain Adaptation | ||||
| Abstract | Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. | ||||
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| Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
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| Notes | ADAS; 600.085; 601.223; 600.076; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ LXG2017 | Serial | 2872 | ||
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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 | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
| Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines | ||||
| Abstract | We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. | ||||
| Address | Faro; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JRL2017a | Serial | 2953 | ||
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| Author | Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes | ||||
| Title |
Graph-based deep learning for graphics classification | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 29-30 | ||
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| Abstract | Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems |
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RDL2017b | Serial | 3058 | ||
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| Author | Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados | ||||
| Title |
Handwriting Recognition by Attribute embedding and Recurrent Neural Networks | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1038-1043 | ||
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| Abstract | Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.097; 601.225; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ TDF2017 | Serial | 3055 | ||
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| Author | Cristina Sanchez Montes; F. Javier Sanchez; Cristina Rodriguez de Miguel; Henry Cordova; Jorge Bernal; Maria Lopez Ceron; Josep Llach; Gloria Fernandez Esparrach | ||||
| Title |
Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results | Type | Conference Article | ||
| Year | 2017 | Publication | 25th United European Gastroenterology Week | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | polyps; histology; computer vision | ||||
| Abstract | during colonoscopy, clinicians perform visual inspection of the polyps to predict histology. Kudo’s pit pattern classification is one of the most commonly used for optical diagnosis. These surface patterns present a contrast with respect to their neighboring regions and they can be considered as bright regions in the image that can attract the attention of computational methods. | ||||
| Address | Barcelona; October 2017 | ||||
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| Area | Expedition | Conference | ESGE | ||
| Notes | MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SSR2017 | Serial | 2979 | ||
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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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