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| Author | R. Valenti; Theo Gevers | ||||
| Title | Accurate Eye Center Location through Invariant Isocentric Patterns | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 34 | Issue | 9 | Pages | 1785-1798 |
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| Abstract | Impact factor 2010: 5.308
Impact factor 2011/12?: 5.96 Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery. |
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| ISSN | 0162-8828 | ISBN | Medium | ||
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ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ VaG 2012a | Serial | 1849 | ||
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| Author | R. Valenti; Theo Gevers | ||||
| Title | Combining Head Pose and Eye Location Information for Gaze Estimation | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 2 | Pages | 802-815 |
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| Abstract | Impact factor 2010: 2.92
Impact factor 2011/12?: 3.32 Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15° by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2° and 5°) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ VaG 2012b | Serial | 1851 | ||
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| Author | Arjan Gijsenij; R. Lu; Theo Gevers; De Xu | ||||
| Title | Color Constancy for Multiple Light Source | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 2 | Pages | 697-707 |
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| Abstract | Impact factor 2010: 2.92
Impact factor 2011/2012?: 3.32 Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic difference between these two illuminants is more than 1° , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%). Otherwise, when the chromatic difference is less than 1° and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods. |
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ GLG2012a | Serial | 1852 | ||
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| Author | Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers | ||||
| Title | A Statistical Method for 2D Facial Landmarking | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 2 | Pages | 844-858 |
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| Abstract | IF = 3.32
Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE). |
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ DSG 2012 | Serial | 1853 | ||
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| Author | Ivo Everts; Jan van Gemert; Theo Gevers | ||||
| Title | Per-patch Descriptor Selection using Surface and Scene Properties | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 7577 | Issue | VI | Pages | 172-186 |
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| Abstract | Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool. | ||||
| Address | Florence, Italy | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33782-6 | Medium | |
| Area | Expedition | Conference | ECCV | ||
| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ EGG2012 | Serial | 2023 | ||
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| Author | Hamdi Dibeklioglu; Theo Gevers; Albert Ali Salah | ||||
| Title | Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 7574 | Issue | III | Pages | 525-538 |
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| Abstract | Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous/posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics. | ||||
| Address | Florence, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33711-6 | Medium | |
| Area | Expedition | Conference | ECCV | ||
| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ DGS2012 | Serial | 2024 | ||
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| Author | Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek | ||||
| Title | Color in Computer Vision: Fundamentals and Applications | Type | Book Whole | ||
| Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Publisher | The Wiley-IS&T Series in Imaging Science and Technology | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
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| Notes |
ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ GGG2012a | Serial | 2068 | ||
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| Author | Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers | ||||
| Title | Improving HOG with Image Segmentation: Application to Human Detection | Type | Conference Article | ||
| Year | 2012 | Publication | 11th International Conference on Advanced Concepts for Intelligent Vision Systems | Abbreviated Journal | |
| Volume | 7517 | Issue | Pages | 178-189 | |
| Keywords | Segmentation; Pedestrian Detection | ||||
| Abstract | In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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| Address | Brno, Czech Republic | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | J. Blanc-Talon et al. | |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33139-8 | Medium | |
| Area | Expedition | Conference | ACIVS | ||
| Notes |
ADAS;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ SLV2012 | Serial | 1980 | ||
| Permanent link to this record | |||||
| Author | Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez | ||||
| Title | Road Scene Segmentation from a Single Image | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 7578 | Issue | VII | Pages | 376-389 |
| Keywords | road detection | ||||
| Abstract | Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images. From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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| Address | Florence, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33785-7 | Medium | |
| Area | Expedition | Conference | ECCV | ||
| Notes |
ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ AGL2012; ADAS @ adas @ agl2012a | Serial | 2022 | ||
| Permanent link to this record | |||||
| Author | Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez | ||||
| Title | Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision – Workshops and Demonstrations | Abbreviated Journal | |
| Volume | 7584 | Issue | Pages | 586-595 | |
| Keywords | road detection | ||||
| Abstract | Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo. |
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33867-0 | Medium | |
| Area | Expedition | Conference | ECCVW | ||
| Notes |
ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ ALG2012; ADAS @ adas | Serial | 2187 | ||
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| Author | Xu Hu | ||||
| Title | Real-Time Part Based Models for Object Detection | Type | Report | ||
| Year | 2012 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 171 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
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ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ Hu2012 | Serial | 2415 | ||
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| Author | Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo | ||||
| Title | A Novel SIFT-Like-Based Approach for FIR-VS Images Registration | Type | Conference Article | ||
| Year | 2012 | Publication | 11th Quantitative InfraRed Thermography | Abbreviated Journal | |
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| Address | Naples, Italy | ||||
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| Area | Expedition | Conference | QIRT | ||
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ADAS; TV | Approved | no | ||
| Call Number | Admin @ si @ ABS2012 | Serial | 2017 | ||
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| Author | Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo | ||||
| Title | Feature Selection Based on Reinforcement Learning for Object Recognition | Type | Conference Article | ||
| Year | 2012 | Publication | Adaptive Learning Agents Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 33-39 | ||
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| Address | Valencia | ||||
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| Area | Expedition | Conference | ALA | ||
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ADAS; RV | Approved | no | ||
| Call Number | Admin @ si @ PSL2012 | Serial | 2018 | ||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez | ||||
| Title | Color Attributes for Object Detection | Type | Conference Article | ||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3306-3313 | ||
| Keywords | pedestrian detection | ||||
| Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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| Address | Providence; Rhode Island; USA; | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes |
ADAS; CIC; | Approved | no | ||
| Call Number | Admin @ si @ KRW2012 | Serial | 1935 | ||
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| Author | R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. | ||||
| Title | On-line Semantic Perception Using Uncertainty | Type | Conference Article | ||
| Year | 2012 | Publication | International Conference on Intelligent Robots and Systems | Abbreviated Journal | IROS |
| Volume | Issue | Pages | 4185-4191 | ||
| Keywords | Semantic Segmentation | ||||
| Abstract | Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance | ||||
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| Area | Expedition | Conference | IROS | ||
| Notes |
ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ NRR2012 | Serial | 2378 | ||
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