| Records |
| Author |
Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification |
Type |
Conference Article |
| Year |
2018 |
Publication |
8th International Workshop on Human Behavior Understanding |
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Issue |
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Pages |
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| Abstract |
Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. |
| Address |
Xian; China; May 2018 |
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Conference |
FGW |
| Notes |
HUPBA; no proj |
Approved |
no |
Call Number  |
Admin @ si @ HKI2018 |
Serial |
3118 |
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| Author |
A. M. Here; B. C. Lopez; Debora Gil; J. J. Camarero; Jordi Martinez-Vilalta |
| Title |
A new software to analyse wood anatomical features in conifer species |
Type |
Conference Article |
| Year |
2013 |
Publication |
International Symposium on Wood Structure in Plant Biology and Ecology |
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International Symposium on Wood Structure in Plant Biology and Ecology |
| Address |
Naples; Italy; March 2013 |
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WSE |
| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ HLG2013 |
Serial |
2303 |
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| Author |
Luis Herranz; Weiqing Min; Shuqiang Jiang |
| Title |
Food recognition and recipe analysis: integrating visual content, context and external knowledge |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions. |
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| Notes |
LAMP; 600.120 |
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no |
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Admin @ si @ HMJ2018 |
Serial |
3250 |
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| Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
| Title |
Wall Patch-Based Segmentation in Architectural Floorplans |
Type |
Conference Article |
| Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1270-1274 |
| Keywords |
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| Abstract |
Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates. |
| Address |
Beiging, China |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
978-0-7695-4520-2 |
Medium |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ HMS2011a |
Serial |
1792 |
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| Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
| Title |
Descriptor-based Svm Wall Detector |
Type |
Conference Article |
| Year |
2011 |
Publication |
9th International Workshop on Graphic Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach. |
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Conference |
GREC |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ HMS2011b |
Serial |
1819 |
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| Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
| Title |
Notation-invariant patch-based wall detector in architectural floor plans |
Type |
Book Chapter |
| Year |
2013 |
Publication |
Graphics Recognition. New Trends and Challenges |
Abbreviated Journal |
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| Volume |
7423 |
Issue |
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Pages |
79--88 |
| Keywords |
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| Abstract |
Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-36823-3 |
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Conference |
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| Notes |
DAG; 600.045; 600.056; 605.203 |
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no |
| Call Number |
Admin @ si @ HMS2013 |
Serial |
2322 |
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| Author |
Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; J. Mauri; Petia Radeva |
| Title |
Stent Strut Detection by Classifying a Wide Set of IVUS Features |
Type |
Conference Article |
| Year |
2012 |
Publication |
Computed Assisted Stenting Workshop |
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Nice, France |
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STENT |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ HPC2012 |
Serial |
2169 |
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| Author |
Antonio Hernandez; Carlos Primo; Sergio Escalera |
| Title |
Automatic user interaction correction via Multi-label Graph cuts |
Type |
Conference Article |
| Year |
2011 |
Publication |
In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1276-1281 |
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| Abstract |
Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm. |
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ISBN |
978-1-4673-0062-9 |
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Conference |
HICV |
| Notes |
MILAB; HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ HPE2011 |
Serial |
1892 |
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| Author |
Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane |
| Title |
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
Type |
Miscellaneous |
| Year |
2024 |
Publication |
Arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. |
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HUPBA |
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no |
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Admin @ si @ HPF2024 |
Serial |
4021 |
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| Author |
Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
| Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
48 |
Issue |
3 |
Pages |
709-719 |
| Keywords |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
| Abstract |
Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
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| Notes |
ISE; 600.063; 600.078 |
Approved |
no |
| Call Number |
Admin @ si @ HPG2015 |
Serial |
2589 |
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| Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
| Title |
Use case visual Bag-of-Words techniques for camera based identity document classification |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Issue |
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Pages |
721 - 725 |
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| Abstract |
Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077; 600.061; |
Approved |
no |
| Call Number |
Admin @ si @ HRL2015a |
Serial |
2726 |
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| Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
| Title |
Attributed Graph Grammar for floor plan analysis |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
726 - 730 |
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| Abstract |
In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077; 600.061 |
Approved |
no |
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Admin @ si @ HRL2015b |
Serial |
2727 |
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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 |
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| Volume |
9657 |
Issue |
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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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LNCS |
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Conference |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ HRL2017 |
Serial |
3086 |
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| Author |
Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Review on Emotion Recognition Databases |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Human-Robot Interaction: Theory and Application |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
emotion; computer vision; databases |
| Abstract |
Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. |
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ISBN |
978-1-78923-316-2 |
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| Notes |
HUPBA; 602.133 |
Approved |
no |
| Call Number |
Admin @ si @ HRL2018 |
Serial |
3212 |
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| Author |
Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera |
| Title |
GrabCut-Based Human Segmentation in Video Sequences |
Type |
Journal Article |
| Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
12 |
Issue |
11 |
Pages |
15376-15393 |
| Keywords |
segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field |
| Abstract |
In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology. |
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HuPBA;MILAB |
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Admin @ si @ HRP2012 |
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2147 |
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