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Author Fadi Dornaika; Angel Sappa edit  openurl
  Title Real Time Image Registration for Planar Structure and 3D Sensor Pose Estimation Type Book Chapter
  Year 2008 Publication Stereo Vision Abbreviated Journal  
  Volume 18 Issue Pages 299–316  
  Keywords (down)  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Asim Bhatti  
  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 Approved no  
  Call Number ADAS @ adas @ DoS2008c Serial 1057  
Permanent link to this record
 
 
Author David Aldavert; Ricardo Toledo edit  openurl
  Title Stereo Vision Local Map Alignment for Robot Environment Mapping Type Book Chapter
  Year 2008 Publication Robot Vision Second International Workshop, RobVis Abbreviated Journal  
  Volume 4931 Issue Pages 111–124  
  Keywords (down)  
  Abstract  
  Address Auckland (New Zealand)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ AlT2008 Serial 1100  
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Author Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis edit  openurl
  Title Advances in Tracking and Recognition of Human Motion Type Book Chapter
  Year 2009 Publication Encyclopedia of Information Science and Technology Abbreviated Journal  
  Volume I Issue 2nd edition Pages 65–71  
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  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  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ ASG2009 Serial 1143  
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Author Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis edit  openurl
  Title Prior Knowledge Based Motion Model Representation Type Book Chapter
  Year 2009 Publication Progress in Computer Vision and Image Analysis Abbreviated Journal  
  Volume 16 Issue Pages  
  Keywords (down)  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Horst Bunke; JuanJose Villanueva; Gemma Sanchez  
  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 Approved no  
  Call Number ADAS @ adas @ SAM2009 Serial 1235  
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Author David Geronimo edit  isbn
openurl 
  Title A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems Type Book Whole
  Year 2010 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords (down)  
  Abstract At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area.  
  Address Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-936529-5-1 Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ Ger2010 Serial 1279  
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Author Jose Manuel Alvarez edit  isbn
openurl 
  Title Combining Context and Appearance for Road Detection Type Book Whole
  Year 2010 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords (down)  
  Abstract Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the
development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit
color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene.
In this way, we present two different approaches to infer the geometry of the road ahead
the moving vehicle. Finally, we focus on combining these context and appearance (color)
approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art.		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Theo Gevers  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-937261-8-8 Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ Alv2010 Serial 1454  
Permanent link to this record
 
 
Author Angel Sappa (ed) edit  isbn
openurl 
  Title Computer Graphics and Imaging Type Book Whole
  Year 2010 Publication Computer Graphics and Imaging Abbreviated Journal  
  Volume Issue Pages  
  Keywords (down)  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Angel Sappa  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978–0–88986–836–6 Medium  
  Area Expedition Conference CGIM  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ Sap2010 Serial 1468  
Permanent link to this record
 
 
Author Aura Hernandez-Sabate; Debora Gil edit   pdf
url  doi
isbn  openurl
  Title The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries Type Book Chapter
  Year 2012 Publication Intravascular Ultrasound Abbreviated Journal  
  Volume Issue Pages 185-206  
  Keywords (down)  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Intech Place of Publication Editor Yasuhiro Honda  
  Language English Summary Language english Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-953-307-900-4 Medium  
  Area Expedition Conference  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HeG2012 Serial 1684  
Permanent link to this record
 
 
Author Ferran Diego edit  openurl
  Title Probabilistic Alignment of Video Sequences Recorded by Moving Cameras Type Book Whole
  Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords (down)  
  Abstract Video alignment consists of integrating multiple video sequences recorded independently into a single video sequence. This means to register both in time (synchronize
frames) and space (image registration) so that the two videos sequences can be fused
or compared pixel–wise. In spite of being relatively unknown, many applications today may benefit from the availability of robust and efficient video alignment methods.
For instance, video surveillance requires to integrate video sequences that are recorded
of the same scene at different times in order to detect changes. The problem of aligning videos has been addressed before, but in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, most works rely
on restrictive assumptions which reduce its difficulty such as linear time correspondence or the knowledge of the complete trajectories of corresponding scene points on the images; to some extent, these assumptions limit the practical applicability of the solutions developed until now. In this thesis, we focus on the challenging problem of aligning sequences recorded at different times from independent moving cameras following similar but not coincident trajectories. More precisely, this thesis covers four studies that advance the state-of-the-art in video alignment. First, we focus on analyzing and developing a probabilistic framework for video alignment, that is, a principled way to integrate multiple observations and prior information. In this way, two different approaches are presented to exploit the combination of several purely visual features (image–intensities, visual words and dense motion field descriptor), and
global positioning system (GPS) information. Second, we focus on reformulating the
problem into a single alignment framework since previous works on video alignment
adopt a divide–and–conquer strategy, i.e., first solve the synchronization, and then
register corresponding frames. This also generalizes the ’classic’ case of fixed geometric transform and linear time mapping. Third, we focus on exploiting directly the
time domain of the video sequences in order to avoid exhaustive cross–frame search.
This provides relevant information used for learning the temporal mapping between
pairs of video sequences. Finally, we focus on adapting these methods to the on–line
setting for road detection and vehicle geolocation. The qualitative and quantitative
results presented in this thesis on a variety of real–world pairs of video sequences show that the proposed method is: robust to varying imaging conditions, different image
content (e.g., incoming and outgoing vehicles), variations on camera velocity, and
different scenarios (indoor and outdoor) going beyond the state–of–the–art. Moreover, the on–line video alignment has been successfully applied for road detection and
vehicle geolocation achieving promising results.		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Joan Serrat  
  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 Approved no  
  Call Number Admin @ si @ Die2011 Serial 1787  
Permanent link to this record
 
 
Author Muhammad Anwer Rao edit  openurl
  Title Color for Object Detection and Action Recognition Type Book Whole
  Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords (down)  
  Abstract Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition.

In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection.

In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task.

Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images.		  
  Address Barcelona  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Joost Van de Weijer  
  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 Approved no  
  Call Number Admin @ si @ Rao2013 Serial 2281  
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