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Author Gioacchino Vino; Angel Sappa edit  doi
isbn  openurl
  Title Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach Type Conference Article
  Year (down) 2013 Publication 10th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 7950 Issue Pages 354-363  
  Keywords  
  Abstract This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.  
  Address Póvoa de Varzim; Portugal; June 2013  
  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 978-3-642-39093-7 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS; 600.055 Approved no  
  Call Number Admin @ si @ ViS2013 Serial 2562  
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit  openurl
  Title DA-DPM Pedestrian Detection Type Conference Article
  Year (down) 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 edit  url
doi  openurl
  Title Evaluating Color Representation for Online Road Detection Type Conference Article
  Year (down) 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 R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. edit   pdf
openurl 
  Title On-line Semantic Perception Using Uncertainty Type Conference Article
  Year (down) 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  
  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 IROS  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ NRR2012 Serial 2378  
Permanent link to this record
 
 
Author Naveen Onkarappa; Sujay M. Veerabhadrappa; Angel Sappa edit  doi
isbn  openurl
  Title Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture Type Conference Article
  Year (down) 2012 Publication 4th International Conference on Signal and Image Processing Abbreviated Journal  
  Volume 221 Issue Pages 257-267  
  Keywords  
  Abstract Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow.  
  Address Coimbatore, India  
  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 1876-1100 ISBN 978-81-322-0996-6 Medium  
  Area Expedition Conference ICSIP  
  Notes ADAS Approved no  
  Call Number Admin @ si @ OVS2012 Serial 2356  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez edit   pdf
url  doi
isbn  openurl
  Title Color Attributes for Object Detection Type Conference Article
  Year (down) 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.		  
  Address Providence; Rhode Island; USA;  
  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 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  
Permanent link to this record
 
 
Author David Geronimo; Frederic Lerasle; Antonio Lopez edit   pdf
openurl 
  Title State-driven particle filter for multi-person tracking Type Conference Article
  Year (down) 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal  
  Volume 7517 Issue Pages 467-478  
  Keywords human tracking  
  Abstract Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.		  
  Address Brno, Chzech Republic  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Heidelberg Editor J. Blanc-Talon et al.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ACIVS  
  Notes ADAS Approved yes  
  Call Number GLL2012; ADAS @ adas @ gll2012a Serial 1990  
Permanent link to this record
 
 
Author Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers edit   pdf
doi  isbn
openurl 
  Title Improving HOG with Image Segmentation: Application to Human Detection Type Conference Article
  Year (down) 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.		  
  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 David Vazquez; Antonio Lopez; Daniel Ponsa edit   pdf
isbn  openurl
  Title Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection Type Conference Article
  Year (down) 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3492 - 3495  
  Keywords Pedestrian Detection; Domain Adaptation; Virtual worlds  
  Abstract Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1).  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Tsukuba Science City, JAPAN Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ VLP2012 Serial 1981  
Permanent link to this record
 
 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios edit   pdf
url  isbn
openurl 
  Title Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs Type Conference Article
  Year (down) 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2664 - 2667  
  Keywords  
  Abstract We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint.  
  Address Tsukuba Science City, Japan  
  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 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSL2012a; Serial 2032  
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