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Author | Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie | ||||
Title | Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Abstract | We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field. | ||||
Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ WEA2017 | Serial | 3066 | ||
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Author | Yagmur Gucluturk; Umut Guclu; Marc Perez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Carlos Andujar; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera | ||||
Title | Visualizing Apparent Personality Analysis with Deep Residual Networks | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
Volume | Issue | Pages | 3101-3109 | ||
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Abstract | Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called “looking
at people” sub-field. Considering “apparent” personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining model predictions with their explanations. |
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Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; 6002.143 | Approved | no | ||
Call Number | Admin @ si @ GGP2017 | Serial | 3067 | ||
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Author | Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera | ||||
Title | Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ ABR2017 | Serial | 3068 | ||
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Author | Albert Clapes; Tinne Tuytelaars; Sergio Escalera | ||||
Title | Darwintrees for action recognition | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ CTE2017 | Serial | 3069 | ||
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Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
Title | Learning structural loss parameters on graph embedding applied on symbolic graphs | Type | Conference Article | ||
Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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Abstract | We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. | ||||
Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ JRL2017b | Serial | 3073 | ||
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Author | Xavier Soria; Angel Sappa; Arash Akbarinia | ||||
Title | Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities | Type | Conference Article | ||
Year | 2017 | Publication | 7th International Conference on Image Processing Theory, Tools & Applications | Abbreviated Journal | |
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Keywords | Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset | ||||
Abstract | Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. | ||||
Address | Montreal; Canada; November 2017 | ||||
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Area | Expedition | Conference | IPTA | ||
Notes | NEUROBIT; MSIAU; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SSA2017 | Serial | 3074 | ||
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Author | Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas | ||||
Title | ICDAR2017 Robust Reading Challenge on COCO-Text | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GSG2017 | Serial | 3076 | ||
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Author | Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | ICDAR2017 Robust Reading Challenge on Omnidirectional Video | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Abstract | Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community. | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.084; 600.121 | Approved | no | ||
Call Number | Admin @ si @ IMT2017 | Serial | 3077 | ||
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Author | Laura Lopez-Fuentes; Claudio Rossi; Harald Skinnemoen | ||||
Title | River segmentation for flood monitoring | Type | Conference Article | ||
Year | 2017 | Publication | Data Science for Emergency Management at Big Data 2017 | Abbreviated Journal | |
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Abstract | Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation. | ||||
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Notes | LAMP; 600.084; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LRS2017 | Serial | 3078 | ||
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Author | Suman Ghosh; Ernest Valveny | ||||
Title | R-PHOC: Segmentation-Free Word Spotting using CNN | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Keywords | Convolutional neural network; Image segmentation; Artificial neural network; Nearest neighbor search | ||||
Abstract | arXiv:1707.01294
This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our network takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting. |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GhV2017a | Serial | 3079 | ||
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Author | Suman Ghosh; Ernest Valveny | ||||
Title | Visual attention models for scene text recognition | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Abstract | arXiv:1706.01487
In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition. |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GhV2017b | Serial | 3080 | ||
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Author | Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis | ||||
Title | Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification | Type | Conference Article | ||
Year | 2017 | Publication | 3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition | Abbreviated Journal | |
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Address | Thessaloniki; Greece; October 2017 | ||||
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Area | Expedition | Conference | IMEKOFOODS | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GDM2017 | Serial | 3081 | ||
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Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
Title | e-Counterfeit: a mobile-server platform for document counterfeit detection | Type | Conference Article | ||
Year | 2017 | Publication | 14th IAPR International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Abstract | This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms. | ||||
Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.061; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRL2018 | Serial | 3084 | ||
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Author | Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados | ||||
Title | Ontology-Based Understanding of Architectural Drawings | Type | Book Chapter | ||
Year | 2017 | Publication | International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 9657 | Issue | Pages | 75-85 | |
Keywords | Graphics recognition; Floor plan analysi; Domain ontology | ||||
Abstract | In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ HRL2017 | Serial | 3086 | ||
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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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