|
Records |
Links |
|
Author |
Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation |
Type |
Conference Article |
|
Year |
2017 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Semantic Segmentation |
|
|
Abstract |
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
|
|
Address |
Honolulu; USA; July 2017 |
|
|
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 |
CVPRW |
|
|
Notes |
MILAB; ADAS; 600.076; 600.085; 601.281 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ JDV2016 |
Serial |
2866 |
|
Permanent link to this record |
|
|
|
|
Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
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 ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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. |
|
|
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 |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
Type |
Conference Article |
|
Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
Abbreviated Journal |
|
|
|
Volume |
417 |
Issue |
|
Pages |
503-515 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Scene reconstruction; Point cloud; Autonomous vehicles |
|
|
Abstract |
In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
|
|
Address |
Lisboa; Portugal; November 2015 |
|
|
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 |
ROBOT |
|
|
Notes |
ADAS; 600.076; 600.086 |
Approved |
no |
|
|
Call Number |
Admin @ si @ OSS2015a |
Serial |
2662 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Shadow Resistant Road Segmentation from a Mobile Monocular System |
Type |
Conference Article |
|
Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
|
|
|
Address |
Gerona (Spain) |
|
|
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 |
|
|
|
Notes |
ADAS;CIC |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ ALB2007 |
Serial |
943 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Illuminant Invariant Model-Based Road Segmentation |
Type |
Conference Article |
|
Year |
2008 |
Publication |
IEEE Intelligent Vehicles Symposium, |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1155–1180 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
|
|
|
Address |
Eindhoven (The Netherlands) |
|
|
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 |
|
|
|
Notes |
ADAS;CIC |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ ALB2008 |
Serial |
1045 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Novel Index for Objective Evaluation of Road Detection Algorithms |
Type |
Conference Article |
|
Year |
2008 |
Publication |
Intelligent Transportation Systems. 11th International IEEE Conference on, |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
815–820 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
|
|
|
Address |
Beijing (Xina) |
|
|
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 |
ITSC |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ AlL2008 |
Serial |
1074 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
Type |
Conference Article |
|
Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
565–572 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
|
|
Address |
Miami (USA) |
|
|
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 |
1063-6919 |
ISBN |
978-1-4244-3992-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPR |
|
|
Notes |
ADAS;ISE |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ AGL2009 |
Serial |
1169 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
|
Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
57–64 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
|
|
Address |
San Francisco; CA; USA; June 2010 |
|
|
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 |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
CVPR |
|
|
Notes |
ADAS;ISE |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ AGL2010a |
Serial |
1302 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Geographic Information for vision-based Road Detection |
Type |
Conference Article |
|
Year |
2010 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
621–626 |
|
|
Keywords ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
road detection |
|
|
Abstract |
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach. |
|
|
Address |
San Diego; CA; USA |
|
|
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 |
IV |
|
|
Notes |
ADAS;ISE |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ ALG2010 |
Serial |
1428 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
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 ![sorted by Keywords field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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 |
|
|
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 |