toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
Author Naveen Onkarappa; Angel Sappa edit  doi
openurl 
  Title A Novel Space Variant Image Representation Type Journal Article
  Year 2013 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 47 Issue 1-2 Pages 48-59  
  Keywords Space-variant representation; Log-polar mapping; Onboard vision applications  
  Abstract Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences.  
  Address  
  Corporate Author Thesis  
  Publisher (up) Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0924-9907 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 605.203; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013a Serial 2243  
Permanent link to this record
 

 
Author Naveen Onkarappa; Angel Sappa edit  doi
openurl 
  Title Synthetic sequences and ground-truth flow field generation for algorithm validation Type Journal Article
  Year 2015 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 74 Issue 9 Pages 3121-3135  
  Keywords Ground-truth optical flow; Synthetic sequence; Algorithm validation  
  Abstract Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.  
  Address  
  Corporate Author Thesis  
  Publisher (up) Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 601.215; 600.076 Approved no  
  Call Number Admin @ si @ OnS2014b Serial 2472  
Permanent link to this record
 

 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
  Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 119 Issue 2 Pages 159-178  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
 
  Address  
  Corporate Author Thesis  
  Publisher (up) Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number Admin @ si @ XRV2016 Serial 2669  
Permanent link to this record
 

 
Author Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf edit   pdf
doi  openurl
  Title Robust lane markings detection and road geometry computation Type Journal Article
  Year 2010 Publication International Journal of Automotive Technology Abbreviated Journal IJAT  
  Volume 11 Issue 3 Pages 395–407  
  Keywords lane markings  
  Abstract Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.  
  Address  
  Corporate Author Thesis  
  Publisher (up) The Korean Society of Automotive Engineers Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1229-9138 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LSC2010 Serial 1300  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: