Einzusetzende Mittel - Intelligente Systeme

Seminar Vorbesprechung
„Intelligente Systeme“, Sommersemester 2011
Betreut von:
Prof. Dr. Pauli
Dipl.-Inf. Bürger
Dipl.-Inform. Herwig
Dipl.-Inform. Hoefinghof
Dipl.-Inform. Korn
Seminar Intelligente Systeme
13.04.2012
1
Organisatorisches
Dieser Vortrag sowie weitere aktuelle Informationen stehen
unter http://www.is.uni-due.de/seminar zur
Verfügung.
Seminar Intelligente Systeme
13.04.2012
2
Organisatorisches
Anforderungen:
– Einarbeitung in das gewählte Thema
– mehrmalige Besprechung mit dem jeweiligen Betreuer
– Erstellen von Vortragsfolien im PDF oder PowerPointFormat (max 30 Folien)
– Erstellen einer schriftlichen Ausarbeitung im PDF-Format
(ca. 20 Seiten)
–
–
Vorbereitung auf die Vorträge der anderen Teilnehmer
Teilnahme an der Diskussion nach den Vorträgen
Seminar Intelligente Systeme
13.04.2012
3
Organisatorisches
Themenvergabe
1. Besprechung
-7
Weitere Besprechungen
vorläufige Folien
-2
Abgabe der Folien
-1
Vorbereitung
Vortrag
0
Diskussion
Abgabe der Ausarbeitung
+3
t
t in Wochen
Seminar Intelligente Systeme
13.04.2012
4
Automatic Panorama Image Stitching
Automatic Panorama Image Stitching
Literatur:
–
–
–
Brown, Lowe; Automatic Panoramic Image Stitching using
Invariant Features, International Journal of Computer Vision, 2007
Eden,Berkeley, Uyttendaele, Szeliski; Seamless Image Stitching
of Scenes with Large Motions and Exposure Differences,
Conference on Computer Vision and Pattern Recognition, 2006
Nomura, Zhang, Nayar; Scene Collages and Flexible Camera
Arrays, Eurographics Symposium on Rendering (2007)
Einzusetzende Mittel: Robuste Bildmerkmale, Optimierung
Visual Attention for Rapid Scene Analysis
Hierarchie „interessanter“ Bereiche im Bild
Visual Attention for Rapid Scene Analysis
Literatur:
–
–
–
Itti, Koch, Niebur; A Model of Saliency-Based Visual Attention
for Rapid Scene Analysis, IEEE TRANSACTIONS ON PATTERN
ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 11,
NOVEMBER 1998
Hou, Zhang Department of Computer Science; Saliency Detection:
A Spectral Residual Approach, Computer Vision and Pattern
Recognition, 2007. CVPR '07. IEEE Conference on
Itti, Koch, A saliency-based search mechanism for overt and
covert shifts of visual attention, Vision Research 2000
Einzusetzende Mittel: Objekterkennung, Spektralanalyse
Robust Energy Based Image Segmentation Methods
Robuste Segmentierung bei verrauschten Bildern…
… komplexen Strukturen …
… und unscharfen Konturen
Robust Energy Based Image Segmentation Methods
Literatur:
–
–
–
Krinidis, Chatzis; Fuzzy Energy-Based Active Contours; IEEE
TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 12,
DECEMBER 2009
Shyu, Pham, Tran, Lee; Global and local fuzzy energy-based
active contours for image segmentation
Chan, Vese; Active Contours Without Edges, IEEE
TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 2,
FEBRUARY 2001
Einzusetzende Mittel: Differentialgleichungen, Segmentierung,
Fuzzymethoden
Interactive Graph Based Image Segmentation
Interaktive Vorgabe von Regionen mit Vordergrund- und Hintergrundtextur
Interactive Graph Based Image Segmentation
Literatur:
–
–
BOYKOV FUNKA-LEA; Graph Cuts and Efficient N-D Image
Segmentation; International Journal of Computer Vision 2006
Li, Fan, Zhao, Zhang; Graph Cuts Based Image Segmentation
Using Local Color And Texture; 2011 4th International Congress
on Image and Signal Processing
Einzusetzende Mittel: Graphalgorithmen, Textursegmentierung
IMAGE FUSION
High Dynamic Range Imaging
High Dynamic Range Imaging
Literatur:
–
–
–
–
–
–
–
–
Debevec, Malik; Recovering High Dynamic Range Radiance Maps
from Photographs; ACM 1997
Robertson, Borman, Stevenson; Dynamic Range Improvement
Through Multiple Exposures; IEEE 1999
Robertson, Borman, Stevenson; Estimation-theoretic approach to
dynamic range enhancement using multiple exposures; 2003
Mann; Extending Dynamic Range By Combining Differently Exposed
Pictures; IS&T 1994
Healey; Radiometric CCD Camera Calibration and Noise Estimation;
IEEE 1994
Mertens, Kautz, Van Reeth; Exposure Fusion;
Greg Ward; Fast, robust image registration for compositing high
dynamic range photographcs from hand-held exposures;
Nayar, Mitsunaga; High dynamic range imaging spatially varying pixel
exposures;
Einzusetzende Mittel: Least-Squares, Polynomiale Optimierung
Fusion of Stereo and Focus Series for Depth Estimation
Defocus →
Stereo →
Detail
Stereo Depth Map
Fused Depth Map
Fusion of Stereo and Focus Series for Depth Estimation
Literatur:
- Ioana Gheta, Christian Frese, Michael Heizmann; Fusion of Combined Stereo and
Focus Series for Depth Estimation; 2006
- Ioana Gheta; Fusion von Stereo- und Fokusserien; 2006
- Christian Frese and Ioana Gheta; Robust Depth Estimation by Fusion of Stereo
and Focus Series Acquired with a Camera Array; 2006
- Johannes Burge and Wilson S. Geisler; Optimal defocus estimation in individual
natural images; 2011
- Akira Kubota, Kazuya Kodama, and Kiyoharu Aixawa; REGISTRATION AND BLUR
ESTIMATION METHODS FOR MULTIPLE DIFFERENTLY FOCUSED IMAGES;
1999
- Gunther Schneider, Bernard Heit, Johannes Honag and Jacques Bremont;
MONOCULAR DEPTH PERCEPTION BY EVALUATION OF THE BLUR IN
DEFOCUSED IMAGES; 1994
Einzusetzende Mittel: Frequenzanalyse, Differentialrechnung, Optik
GRAPHS and
MARKOV RANDOM FIELDS
FOR OBJECT SEGMENTATION
Markov Random Fields (MRFs) in der Bildverarbeitung
Degradation Model
Anwendungsbeispiel: Textursegmentierung
Nachbarschaftsmodell
Markov Random Fields (MRFs) in der Bildverarbeitung
Literatur:
–
–
–
–
S. Z. Li; Markov Random Field Modeling in Image Analysis;
2009
Gerhard Winkler; Image Analysis, Random Fields and Markov
Chain Monte Carlo Methods: A Mathematical Introduction;
2006
Jun S. Liu; Monte Carlo Strategies in Scientific Computing;
2008
Urs Köster, Jussi T. Lindgren and Aapo Hyvarinen; Estimating
Markov Random Field Potentials for Natural images; 2009
Einzusetzende Mittel: Wahrscheinlichkeitstheorie, Mustererkennung
State-of-the-Art Object Segmentation
Input
Boundaries
Image Formation Model:
Patch-based
Classifier
Local
Consistency
Result
State-of-the-Art Object Segmentation
Literatur:
Diane Larlus, Jakob Verbeek and Frédéric Jurie; Category Level Object
Segmentation by Combining Bag-of-Words Models with Dirichlet
Processes and Random Fields; IEEE Int. J. Computer Vision, 2009
Einzusetzende Mittel:
Merkmalsextraktion, Markov Random Fields, Zufallsprozesse, Modellbildung
Object Cosegmentation Segmenting the Same Object in Multiple Images
Object Cosegmentation Segmenting the Same Object in Multiple Images
Literatur:
- Carsten Rother, Vladimir Kolmogorov, Tom Minka and Andrew Blake;
Cosegmentation of Image Pairs by Histogram Matching —
Incorporating a Global Constraint into MRFs;
- Sara Vicente, Carsten Rother, Vladimir Kolmogorov; Object
Cosegmentation;
- Sara Vicente, Vladimir Kolmogorov, Carsten Rother; Cosegmentation
Revisited: Models and Optimization; ECCV 2010
- Dorit S. Hochbaum, Vikas Singh; An efficient algorithm for Cosegmentation;
Einzusetzende Mittel:
Merkmalsextraktion, Markov Random Fields, Graph-Matching
Multi-Object Tracking – Walking Pyramids
Pyramid of Moving Object
Multi-Object Tracking – Walking Pyramids
Literatur:
Walter Kropatsch:
http://www.prip.tuwien.ac.at/people/krw/more/bibliography.php
Walter Kropatsch; When Pyramids Learned Walking; Springer 2009
Walter Kropatsch; Representing Scenes with dynamic objects by Graph
Pyramids; IASTED 2010
Einzusetzende Mittel:
Merkmalsextraktion, Graphen, Geometrische Transformationen
MACHINE LEARNING
Digital Face Beautification
Original
Beautified
Digital Face Beautification
Literatur:
Tommer Leyvand, Daniel Cohen-Or, Gideon Dror and Dani Lischinski;
Data-Driven Enhancement of Facial Attractiveness; ACM SIGGRAPH 2008
Einzusetzende Mittel:
Merkmalsextraktion, Neuronale Netze, Bildregistrierung, Modellbildung
Evolutionary Learning of Collaborative Behavior
Two teams of robots
communicating through
light signals for
collaboratively finding food
but avoiding poison
„Robotic Sand“: Tiny robots communicating with their neighbours
through magnets for self-assembly of learned 3D shapes
Evolutionary Learning of Collaborative Behavior
Literatur:
- Dario Floreano, Sara Mitri, Stephane Magnenat, Laurent Keller; Evolutionary
Conditions for the Emergence of Communication in Robots; 2007
- Richard A. Watson, Sevan G. Ficici, Jordan B. Pollack; Embodied Evolution:
Embodying an Evolutionary Algorithm in a Population of Robots; 1999
- Kyle Gilpin and Daniela Rus; Self-disassembling Robot Pebbles: New
results and ideas for self-assembly of 3d structures; IEEE 2010
Distributed Robotics Group:
http://groups.csail.mit.edu/drl/wiki/index.php?title=Robot_Pebbles
Einzusetzende Mittel:
Genetische Algorithmen, Knowledge Engineering
RGB-D SLAM
Simultaneous localization and mapping
SLAM with a RGB-D camera
voxel grid:
SLAM with a RGB-D camera
Literatur:
- Nikolas Engelhard, Felix Endres, Jürgen Hess, Jürgen Sturm, Wolfram
Burgard; Real-time 3D visual SLAM with a hand-held RGB-D camera;
2011
- F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, W. Burgard; An
Evaluation of the RGB-D SLAM System; 2012
- Virgile Högman; Building a 3D map from RGB-D sensors; 2012
- Software quelloffen (http://www.ros.org/wiki/rgbdslam)
Einzusetzende Mittel:
lokale Bildmerkmale, (Probabilistik, Optimierung)
Generalized-ICP (Iterative Closest Point)
Generalized-ICP (Iterative Closest Point)
Literatur:
- Aleksandr V. Segal, Dirk Haehnel, Sebastian Thrun; Generalized-ICP; 2009
- Weitere Verweise im Paper (ICP und point-to-plane ICP)
- Implementiert in der Point Cloud Library (PCL)
Einzusetzende Mittel:
Optimierung, Probabilistik, geometrische Transformationen
Hierarchical Optimization for Online Mapping
Hierarchical Optimization for Online Mapping
Literatur:
- Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Udo Frese, Christoph
Hertzberg; Hierarchical Optimization on Manifolds for Online 2D and 3D
Mapping; 2010
- Weitere Verweise im Paper
- quelloffen („HOG-Man“)
Einzusetzende Mittel:
Optimierung (Gauß-Newton-Verfahren),
Geometrische Transformationen (lineare Näherung bezüglich Rotation)
SLAM with „general graph optimization“
SLAM with „general graph optimization“
Literatur:
- Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, Wolfram
Burgard; g2o: A General Framework for Graph Optimization; 2011
- Weitere Verweise im Paper
- quelloffen („g2o“)
Einzusetzende Mittel:
Optimierung (diverses),
Geometrische Transformationen (lineare Näherung bezüglich Rotation)
Themenliste
1) Automatic Panorama Image Stitching
2) Visual Attention for Rapid Scene Analysis
3) Robust Energy Based Image Segmentation Methods
4) Interactive Graph Based Image Segmentation
5) High Dynamic Range Imaging
6) Fusion of Stereo and Focus Series for Depth Estimation
7) Markov Random Fields (MRFs) in der Bildverarbeitung
8) State-of-the-Art Object Segmentation
9) Object Cosegmentation
10) Multi-Object Tracking – Walking Pyramids
11) Digital Face Beautification
12) Evolutionary Learning of Collaborative Behavior
13) SLAM with a RGB-D camera
14) Generalized-ICP
15) Hierarchical Optimization for Online Mapping
16) SLAM with „general graph optimization“