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Author |
Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez |
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Title |
Vision-based road detection via on-line video registration |
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Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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Pages |
1135–1140 |
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Keywords |
video alignment; road detection |
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Abstract |
TB6.2
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a traffic-free reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminant-invariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ITSC |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ DAS2010 |
Serial |
1424 |
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Author |
Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Camera Egomotion Estimation in the ADAS Context |
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Conference Article |
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Year |
2010 |
Publication |
13th International IEEE Annual Conference on Intelligent Transportation Systems |
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Pages |
1415–1420 |
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Abstract |
Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ADAS |
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ADAS @ adas @ CPL2010 |
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1425 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Single Snapshot 3D Head Pose Initialization for Tracking in Human Robot Interaction Scenario |
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Conference Article |
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2010 |
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1st International Workshop on Computer Vision for Human-Robot Interaction |
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32–39 |
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1st International Workshop on Computer Vision for Human-Robot Interaction, in conjunction with IEEE CVPR 2010 |
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This paper presents an automatic 3D head pose initialization scheme for a real-time face tracker with application to human-robot interaction. It has two main contributions. First, we propose an automatic 3D head pose and person specific face shape estimation, based on a 3D deformable model. The proposed approach serves to initialize our realtime 3D face tracker. What makes this contribution very attractive is that the initialization step can cope with faces
under arbitrary pose, so it is not limited only to near-frontal views. Second, the previous framework is used to develop an application in which the orientation of an AIBO’s camera can be controlled through the imitation of user’s head pose.
In our scenario, this application is used to build panoramic images from overlapping snapshots. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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CVPRW |
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OR;MV |
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Call Number |
BCNPCL @ bcnpcl @ DoR2010a |
Serial |
1309 |
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Author |
Michal Drozdzal; Laura Igual; Petia Radeva; Jordi Vitria; Carolina Malagelada; Fernando Azpiroz |
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Title |
Aligning Endoluminal Scene Sequences in Wireless Capsule Endoscopy |
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Conference Article |
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Year |
2010 |
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IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis |
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117–124 |
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Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences will help to find regions of similar intestinal activity and in this way will provide a valuable information on intestinal motility problems. This work, for first time, addresses the problem of aligning endoluminal sequences taking into account motion and structure of the intestine. To describe motility in the sequence, we propose different descriptors based on the Sift Flow algorithm, namely: (1) Histograms of Sift Flow Directions to describe the flow course, (2) Sift Descriptors to represent image intestine structure and (3) Sift Flow Magnitude to quantify intestine deformation. We show that the merge of all three descriptors provides robust information on sequence description in terms of motility. Moreover, we develop a novel methodology to rank the intestinal sequences based on the expert feedback about relevance of the results. The experimental results show that the selected descriptors are useful in the alignment and similarity description and the proposed method allows the analysis of the WCE. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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Conference |
MMBIA |
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Notes |
OR;MILAB;MV |
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Call Number |
BCNPCL @ bcnpcl @ DIR2010 |
Serial |
1316 |
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Author |
Antonio Hernandez; Miguel Reyes; Sergio Escalera; Petia Radeva |
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Title |
Spatio-Temporal GrabCut human segmentation for face and pose recovery |
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Conference Article |
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Year |
2010 |
Publication |
IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
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33–40 |
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In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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AMFG |
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MILAB;HUPBA |
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Call Number |
BCNPCL @ bcnpcl @ HRE2010 |
Serial |
1362 |
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