Intelligent Vision Systems 1
Intelligent Vision Systems
@ Institute of Computer Science III
Jens Behley, Ali Borji, Armin B. Cremers,
Simone Frintrop, Dominik Klein, Volker Steinhage
8.10.2009
Intelligent Vision Systems 2
Team
now Ph.D.
Intelligent Vision Systems 3
Computational Visual Attention• Visual attention:
concept of human vision
• Computational attention systems: simulate this behaviour
• Useful especially for complex systems:
– quickly determine region of interest,
– restrict time-consuming processes to small parts
Computational attention system
Input image
Saliency map
Intelligent Vision Systems 4
Attention System VOCUS
Intensity OrientationColor
FeatureMaps
input image
ConspicuityMaps
[Frintrop, Springer LNAI, 2005]SaliencyMap
greenblueoff on
0o 45o
90o 135o
yellowred
Uniquenessweigt
Uniquenessweigt
Intelligent Vision Systems 5
Attentive Robot Localization• Use visual landmarks to
localize robot
• tracking and redetection of discriminative landmarks
• attention systems to find salient landmarks
Where am I?
landmark 1
landmark 2
landmark 3
match!
match!
match!
I must be in the kitchen!
[Frintrop,Jensfelt:IEEE Trans. on Robotics 2008][Frintrop: ECCV workshop, 2008]
Intelligent Vision Systems 6
Attentive Robot LocalizationCan also be integrated into visual SLAM (simultaneous localization and mapping):
Video at:http://www.informatik.uni-bonn.de/~frintrop/research/aslam.html
Intelligent Vision Systems 7
Visual Tracking• Challenges:
– moving camera– real-time constraints– illumination changes– unknown environment– quick online learning
of objects desired
• We use probabilistic particlefilter approach with a cognitiveobservation model
[Yukie Nagai, University Bielefeld]
[Frintrop,Kessel, ICRA 2009][Frintrop, Königs, Hoeller, Schulz: J. on Social Robotics 2010]
Intelligent Vision Systems 8
Results
[Frintrop,Kessel, ICRA 2009][Frintrop, Königs, Hoeller, Schulz: J. on Social Robotics 2010]
Considerably better resultsthan standard color histogramtracking
Current work:
- consider spatial layout of target to compute component-based descriptor:
- adapt target descriptoraccording to backgroundchanges
[Frintrop, submitted]
[Borji, Frintrop, submitted]
Intelligent Vision Systems 9
Adapting Feature Descriptors forBackground Change
• Before: target descriptor from first frame is used in particles
• Now: target descriptor for the cluster of each background is used
• How : A number of background clusters from a train sequence is first derived. Then over a test sequence, cluster of the current background is determined and particles are updated with the descriptor of this cluster
Comparing approaches 1) first frame, 2) clustering 3) frame by frame and 4) ground truth object positions
Results in finer tracking than previous approach[Borji, Frintrop, submitted]
Intelligent Vision Systems 10
Multi-Sensor Object Classification• smart combination of sensors• Compute a spatial mapping of
sensors• AdaBoost to learn optimal
combination of sensors.
→ see our poster
[Klein,Schulz,Frintrop, ICVS 2009]
Intelligent Vision Systems 11
Object Classification & Tracking• co-operation with Fraunhofer FKIE• dense 3D laserscans (Velodyne HDL64-E)
current work:• classification in dynamic scenes
using CRFs and kNN• tracking of multiple objects
with particle filters
Intelligent Vision Systems 12
Generation of 3D City ModelsStrategies:
• Recognition by Components
• Data Fusion(Areal images, DSM, GIS)
→ see our poster
[Behley, Steinhage - ICVS 2009]
Intelligent Vision Systems 13
Maintenance of 3D City ModelsStrategy:
• Open Source Components
• SQL queries
• Point & window queries
[Steinhage, Behley, Meisel - submitted]
select Window(Geometry)from Buildingswhere RoofType =“HipRoof” and
OverlapsRect (Footprint,@Rectangle);
Intelligent Vision Systems 14
Taxon Identification in SystematicsStrategy:
• Fingerprinting Bees:
– Automatic Extraction of Morphological Features
– Non-linear Kernel Discriminant Analysis
– Multi-Media Database
[Steinhage et al. - ATIS, Taylor & Francis 2007]
[Francoy et al. - Genetics and Molecular Research 2009]
Intelligent Vision Systems 15
Taxon Identification in SystematicsStrategy:
• Fingerprinting Orthoptera:
– Automatic Extraction of Morphological Features
– Multi-Image Analysis
[Steinhage et al. - ATIS, Taylor & Francis 2007]
Intelligent Vision Systems 16
Thank You
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