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Recognition of a Moving Object in a Stereo Environment Using a Content Based Image Database

Recognition of a Moving Object in a Stereo Environment Using a Content Based Image Database. Attila Kiss, Tamás Németh, Szabolcs Sergyán, Zoltán Vámossy, László Csink Budapest T ech. Contents. Introduction System build-up Techniques Results Summary. Project description.

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Recognition of a Moving Object in a Stereo Environment Using a Content Based Image Database

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  1. Recognition of a Moving Object in a Stereo Environment Using a Content Based Image Database Attila Kiss, Tamás Németh, Szabolcs Sergyán, Zoltán Vámossy, László Csink Budapest Tech

  2. Contents • Introduction • System build-up • Techniques • Results • Summary 2005.01.21. SAMI 2005 (2/21)

  3. Project description • Two-camera system that is able to detect and recognize a moving object in the workspace Sub goals • Detection of moving objects • Produce 3D model for the detected objects using disparity map • Forward deepness and other features to a content based retrieval system for object recognition 2005.01.21. SAMI 2005 (3/21)

  4. System build-up • Camera handler • Motion detection • Model preparation • Modeling system • Model creation • OpenGL - visualization • Content-based image retrieval system • Feature extraction • Similarity measure • Feedback 2005.01.21. SAMI 2005 (4/21)

  5. Camera handler • Object detection • Difference from background • Marking regions • Connection with CBIRS • Surround the found object with the smallest rectangle or with convex hull • Model preparation • Searching feature points with Harris type corner detection algorithm 2005.01.21. SAMI 2005 (5/21)

  6. Modeling system • Model creation • Intensity cross correlation • Finding correspondence between left and right picture with intensity cross correlation using feature points • Get deepness information from the matched feature points disparity • Correlation based stereo • Runs on whole image • Slow • Visualization • OpenGL Mubarak Shah - „Fundamentals Of Computer Vision” Computer Science Department University of Central Florida, Orlando, 1997. 2005.01.21. SAMI 2005 (6/21)

  7. Content based retrieval – 1 Jose A. Lay, Ling Guan – „Image Retrieval Based On Energy Histograms Of The Low Frequency DCT Coefficients”, 2004.12.18. 2005.01.21. SAMI 2005 (7/21)

  8. Content-based retrieval – 2 • Preprocessing, noise filtering • Gaussfilter • Color normalization (Finlayson) • Low level features • DCT coefficients • Color histograms in 6 colorspaces (RGB, HSV, YIQ, XYZ, L*u*v*, L*a*b*) • Cornerness • Disparity map B. V. Funt, G. D. Finlayson– “Color Constant Color Indexing” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMIÖ, Bd. 17, Nr. 5, 1995, S. 522-529. 2005.01.21. SAMI 2005 (8/21)

  9. Content-based retrieval – 3 • Similarity measure • Minkowski distance • Histogram intersection • Hierarchical search • Multi dimensional similarity measure • Feedback, Yong Rui technique Yong Rui, Thomas S. Huang, Michael Ortega and Sharad Mehrotra: Relevance Feedback - „A Power Tool for Interactive Content-Based Image Retrieval” IEEE Transactions on Circuits and Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content, pp644-655, Vol 8, No. 5, Sept, 1998 2005.01.21. SAMI 2005 (9/21)

  10. Testing, results – 1 • Convex hull • Motion detection • Feature points 2005.01.21. SAMI 2005 (10/21)

  11. Testing, results – 2 • Matching feature points • Detected feature points • Corresponding feature points 2005.01.21. SAMI 2005 (11/21)

  12. Pentagon satellite stereo images and their disparity map Testing, results – 3 • Own images and their disparity map 2005.01.21. SAMI 2005 (12/21)

  13. Testing, results – 4 Contents of own test dataset 2005.01.21. SAMI 2005 (13/21)

  14. Testing, results – 5 2005.01.21. SAMI 2005 (14/21)

  15. Testing, results – 6 • Some results of content based image retrieval group test 2005.01.21. SAMI 2005 (15/21)

  16. Precision with University of Washington collection Henning Müller, Wolfgang Müller, Stephane Marchand-Maillet, Thierry Pun: A web-basedevaluation system for CBIR, 2004.12.18. http://woodworm.cs.uml.edu/~rprice/ep/mueller/ Some examples Testing, results – 7 2005.01.21. SAMI 2005 (16/21)

  17. Summary – 1 • Two-camera stereo environment • Detect moving • Model workspace • Forward disparity map to a CBIR • Content based image retrieval system • Pluginable by indexing techniques • Automatically produce indices • Color based • Texture based • Depth based 2005.01.21. SAMI 2005 (17/21)

  18. Summary – 2 • Semantic interpretations • Textual description • Hierarchically build database • Query type • Nearest neighbors • Threshold • Relevance Feedback • Automatic testing and evaluation • Store • Compare 2005.01.21. SAMI 2005 (18/21)

  19. Future plans • Implement other techniques • Fasten existing modeling algorithms • Camera calibration • Using OODB with special indexing e.g. a type of B-tree 2005.01.21. SAMI 2005 (19/21)

  20. References • M.J. Swain and B.H. Ballard - “Color Indexing” Int’l J. Computer Vision, vol. 7, no. 1, pp. 11-32, 1991. • B. V. Funt, G. D. Finlayson– “Color Constant Color Indexing” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMIÖ, Bd. 17, Nr. 5, 1995, S. 522-529. • Yong Rui, Thomas S. Huang, Michael Ortega and Sharad Mehrotra: Relevance Feedback - „A Power Tool for Interactive Content-Based Image Retrieval” IEEE Transactions on Circuits and Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content, pp644-655, Vol 8, No. 5, Sept, 1998 • Jonathan Owens, Andrew Hunter & Eric Fletcher - „A Fast Model-Free Morphology-Based Object Tracking Algorithm” http://www.bmva.ac.uk/bmvc/2002/papers/99/full_99.pdf • Marc Pollefeys - „3D Modelling from Images” http://www.esat.kuleuven.ac.be/~pollefey/tutorial/ • C. Harris and M. Stephens – „A combined corner and edge detector” Fourth Alvey Vision Conference, pp.147-151, 1988. • Henning Müller, Wolfgang Müller, Stephane Marchand-Maillet, Thierry Pun: A web-based evaluation system for CBIR, 2004.12.18 http://woodworm.cs.uml.edu/~rprice/ep/mueller/ • Mubarak Shah - „Fundamentals Of Computer Vision” Computer Science Department University of Central Florida, Orlando, 1997. 2005.01.21. SAMI 2005 (20/21)

  21. Thanks for Your attention! Accessibility: Attila Kiss (wampy@freemail.hu) Tamás Németh(pheenix@freemail.hu) Zoltán Vámossy(vamossy.zoltan@nik.bmf.hu) Szabolcs Sergyán(sergyan.szabolcs@nik.bmf.hu) László Csink(csink.laszlo@nik.bmf.hu) Homepage: http://roberta.obuda.kando.hu/iar/2004_2005/FTT 2005.01.21. SAMI 2005 (21/21)

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