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Records |
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Author |
Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Segmentation as Selective Search for Object Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1879-1886 |
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Abstract |
For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge. |
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Barcelona |
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ISSN |
1550-5499 |
ISBN |
978-1-4577-1101-5 |
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Conference |
ICCV |
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ISE |
Approved |
no |
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Call Number |
Admin @ si @ SUG2011 |
Serial |
1780 |
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Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca |
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Title |
A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1776-1783 |
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Abstract |
Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. |
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Barcelona |
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1550-5499 |
ISBN |
978-1-4577-1101-5 |
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ICCV |
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ISE |
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no |
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Call Number |
Admin @ si @ CHM2011 |
Serial |
1811 |
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Author |
Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
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Title |
Detection and Removal of Chromatic Moving Shadows in Surveillance Scenarios |
Type |
Conference Article |
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Year |
2009 |
Publication |
12th International Conference on Computer Vision |
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1499 - 1506 |
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Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions. |
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Kyoto, Japan |
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ISSN |
1550-5499 |
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978-1-4244-4420-5 |
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ICCV |
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no |
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Call Number |
ISE @ ise @ HHM2009 |
Serial |
1213 |
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Permanent link to this record |
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Author |
Petia Radeva; Joan Serrat; Enric Marti |
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Title |
A snake for model-based segmentation |
Type |
Conference Article |
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Year |
1995 |
Publication |
Proc. Conf. Fifth Int Computer Vision |
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Pages |
816-821 |
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Keywords |
snakes; elastic matching; model-based segmenta tion |
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Abstract |
Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signicant improvement. |
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MILAB;ADAS;IAM |
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no |
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Call Number |
IAM @ iam @ RSM1995 |
Serial |
1634 |
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Author |
Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez |
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Title |
You are Fired! Nonverbal Role Analysis in Competitive Meetings |
Type |
Conference Article |
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Year |
2009 |
Publication |
IEEE International Conference on Audio, Speech and Signal Processing |
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1949–1952 |
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This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. |
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Taipei, Taiwan |
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1520-6149 |
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978-1-4244-2353-8 |
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ICASSP |
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Notes |
OR;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ RVG2009 |
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1154 |
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Permanent link to this record |
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Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title |
Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
Type |
Conference Article |
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Year |
2012 |
Publication |
High Performance Computing and Simulation, International Conference on |
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182-187 |
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This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation. |
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Address |
Madrid |
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IEEE Xplore |
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978-1-4673-2359-8 |
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HPCS |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ ISH2012a |
Serial |
2038 |
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Permanent link to this record |
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Author |
Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Active labeling: Application to wireless endoscopy analysis |
Type |
Conference Article |
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Year |
2012 |
Publication |
High Performance Computing and Simulation, International Conference on |
Abbreviated Journal |
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Issue |
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Pages |
174-181 |
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Abstract |
Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”. |
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978-1-4673-2359-8 |
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HPCS |
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Notes |
MILAB; OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ RDS2012 |
Serial |
2152 |
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Permanent link to this record |
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Author |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Title |
Image vignetting reduction via a maximization of fuzzy entropy |
Type |
Conference Article |
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Year |
2017 |
Publication |
IEEE International Conference on Fuzzy Systems |
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In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ LMG2017 |
Serial |
2972 |
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Permanent link to this record |
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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 |
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Title |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification |
Type |
Conference Article |
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Year |
2018 |
Publication |
8th International Workshop on Human Behavior Understanding |
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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. |
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Address |
Xian; China; May 2018 |
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FGW |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ HKI2018 |
Serial |
3118 |
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Permanent link to this record |
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Author |
Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi |
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Title |
Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures |
Type |
Conference Article |
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Year |
2018 |
Publication |
13th IEEE Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
250 - 257 |
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Keywords |
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Abstract |
Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. |
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Xian; China; May 2018 |
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FG |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ HBN2018 |
Serial |
3117 |
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Permanent link to this record |
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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Occlusion Aware Hand Pose Recovery from Sequences of Depth Images |
Type |
Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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Abstract |
State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs. |
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Notes |
HUPBA; ISE; 602.143; 600.098; 600.119 |
Approved |
no |
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Call Number |
Admin @ si @ MEC2017 |
Serial |
2970 |
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Permanent link to this record |
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Author |
Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe |
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Title |
Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database |
Type |
Conference Article |
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Year |
2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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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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Washington;USA; May 2017 |
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Admin @ si @ ATE2017 |
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3013 |
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Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera |
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Exploiting feature representations through similarity learning and ranking aggregation for person re-identification |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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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. |
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Washington; DC; USA; May 2017 |
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HUPBA; 602.143 |
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Admin @ si @ JBE2017 |
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2923 |
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Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari |
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Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. |
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Washington; DC; USA; May 2017 |
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HUPBA; no menciona |
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Admin @ si @ LRL2017 |
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2925 |
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Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari |
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Title |
Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists. |
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Washington; DC; USA; May 2017 |
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HUPBA; no menciona |
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no |
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Admin @ si @ LJG2017 |
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2924 |
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