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Author Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca edit  doi
openurl 
  Title Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications Type Journal Article
  Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 32 Issue 13 Pages 1581-1587  
  Keywords  
  Abstract Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.

This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.

In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.		  
  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 (up)  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ PGB2011a Serial 1707  
Permanent link to this record
 
 
Author Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez edit   pdf
doi  openurl
  Title Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection Type Journal Article
  Year 2011 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 20 Issue 10 Pages 2954 - 2966  
  Keywords  
  Abstract This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions.  
  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 (up)  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ AMB2011 Serial 1716  
Permanent link to this record
 
 
Author Arjan Gijsenji; Theo Gevers; Joost Van de Weijer edit   pdf
url  doi
openurl 
  Title Computational Color Constancy: Survey and Experiments Type Journal Article
  Year 2011 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 20 Issue 9 Pages 2475-2489  
  Keywords computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting  
  Abstract Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets.  
  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 (up)  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes ISE;CIC Approved no  
  Call Number Admin @ si @ GGW2011 Serial 1717  
Permanent link to this record
 
 
Author Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
  Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 96 Issue 1 Pages 83-102  
  Keywords  
  Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.		  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes CAT;ISE;CIC;ADAS Approved no  
  Call Number Admin @ si @ BGW2012 Serial 1718  
Permanent link to this record
 
 
Author Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez edit   pdf
doi  openurl
  Title Determining the Best Suited Semantic Events for Cognitive Surveillance Type Journal Article
  Year 2011 Publication Expert Systems with Applications Abbreviated Journal EXSY  
  Volume 38 Issue 4 Pages 4068–4079  
  Keywords Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces  
  Abstract State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ FBR2011a Serial 1722  
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