| |
Records |
Links |
|
Author |
G. Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella |
|
| |
Title |
Hierarchical CRF with product label spaces for parts-based Models |
Type |
Conference Article |
| |
Year |
2011 |
Publication |
IEEE Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. |
|
| |
Address  |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
FG |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RBT2011 |
Serial |
1862 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Marquez;Debora Gil;Aura Hernandez-Sabate |
|
| |
Title |
A Complete Confidence Framework for Optical Flow |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
|
|
| |
Volume |
7584 |
Issue |
2 |
Pages |
124-133 |
|
| |
Keywords |
Optical flow, confidence measures, sparsification plots, error prediction plots |
|
| |
Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
Florence, Italy, October 7-13, 2012 |
Editor |
Andrea Fusiello, Vittorio Murino ,Rita Cucchiara |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-3-642-33867-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ECCVW |
|
| |
Notes |
IAM;ADAS; |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ MGH2012b |
Serial |
1991 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
|
| |
Title |
Pedestrian Candidates Generation using Monocular Cues |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
7-12 |
|
| |
Keywords |
pedestrian detection |
|
| |
Abstract |
Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE Xplore |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
IV |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
|
| |
Title |
Evaluation of Similarity Functions in Multimodal Stereo |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
7324 |
Issue |
I |
Pages |
320-329 |
|
| |
Keywords |
Aveiro, Portugal |
|
| |
Abstract |
This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head. |
|
| |
Address |
|
|
| |
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 |
978-3-642-31294-6 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICIAR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
BLS2012a |
Serial |
2014 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Oliveira; Angel Sappa; V. Santos |
|
| |
Title |
Color Correction using 3D Gaussian Mixture Models |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
7324 |
Issue |
I |
Pages |
97-106 |
|
| |
Keywords |
|
|
| |
Abstract |
The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
|
| |
Address |
|
|
| |
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 |
10.1007/978-3-642-31295-3_12 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICIAR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OSS2012a |
Serial |
2015 |
|
| 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. |
|
| |
Address |
|
|
| |
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 |
978-3-642-33867-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ECCVW |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ALG2012; ADAS @ adas |
Serial |
2187 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
|
| |
Title |
DA-DPM Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
ICCV Workshop on Reconstruction meets Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection |
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCVW-RR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ XRV2013 |
Serial |
2569 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
|
| |
Title |
Evaluating Color Representation for Online Road Detection |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
594-595 |
|
| |
Keywords |
|
|
| |
Abstract |
Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired
using an on-board camera in different real-driving situations. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVVT:E2M |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ AGL2013 |
Serial |
2794 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
|
| |
Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
5th International Conference Games and Learning Alliance |
Abbreviated Journal |
|
|
| |
Volume |
10056 |
Issue |
|
Pages |
50-59 |
|
| |
Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
|
| |
Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
GALA |
|
| |
Notes |
ADAS;IAM; |
Approved |
no |
|
| |
Call Number |
HAC2016 |
Serial |
2864 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
|
| |
Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Deep Learning; Medical Imaging |
|
| |
Abstract |
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CARS |
|
| |
Notes |
ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ VBS2017a |
Serial |
2880 |
|
| Permanent link to this record |
