On rendering synthetic images for training an object detector

Computer Vision and Image Understanding

Volumn 137, Issue , 2015, Pages 24-37

  Rozantsev, Artem ^a   Lepetit, Vincent ^a,b   Fua, Pascal ^a  

^a EPFL   (Switzerland)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Object detection; Synthetic data; Synthetic image rendering

Indexed keywords

OBJECT DETECTION; OBJECT RECOGNITION; THREE DIMENSIONAL COMPUTER GRAPHICS;

BETTER PERFORMANCE; CAR DETECTION; OBJECT DETECTORS; SYNTHETIC DATA; SYNTHETIC IMAGES; TARGET OBJECT; TRAINING IMAGE; TRAINING OBJECTS;

RENDERING (COMPUTER GRAPHICS);

EID: 84930472910     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2014.12.006     Document Type: Article

References (30)

1. Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, A. Ng, Building high-level features using large scale unsupervised learning, in: International Conference on Machine Learning, 2012.
2. J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, A. Blake Efficient human pose estimation from single depth images IEEE Trans. Pattern Anal. Mach. Intell. 99 2012
3. C. Burges, B. Schölkopf, Improving the accuracy and speed of support vector machines, in: Advances in Neural Information Processing Systems, 1997, pp. 375-381.
4. D. Decoste, B. Schölkopf Training invariant support vector machines Mach. Learn. 46 2002 161 190
5. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, in: Intelligent Signal Processing, 2001, pp. 306-351.
6. T. Varga, H. Bunke, Generation of synthetic training data for an HMM-based handwriting recognition system, in: 7th Int. Conference on Document Analysis and Recognition, 2003, pp. 618-622.
7. F. Fleuret, D. Geman Coarse-to-fine visual selection Int. J. Comput. Vision 41 1 2001 85 107
8. V. Lepetit, P. Lagger, P. Fua, Randomized trees for real-time keypoint recognition, in: Conference on Computer Vision and Pattern Recognition, 2005.
9. J. Marin, D. Vázquez, D. Geronimo, A.M. Lopez, Learning appearance in virtual scenarios for pedestrian detection, in: Conference on Computer Vision and Pattern Recognition, 2010, pp. 137-144.
10. L. Pishchulin, A. Jain, A. Mykhaylo, T. Thormaehlen, B. Schiele, Articulated people detection and pose estimation: reshaping the future, in: Conference on Computer Vision and Pattern Recognition, 2012.
11. K. Rematas, T. Ritschel, M. Fritz, T. Tuytelaars, Image-based synthesis and re-synthesis of viewpoints guided by 3D models, in: Conference on Computer Vision and Pattern Recognition, 2014.
12. P. Felzenszwalb, D. Mcallester, D. Ramanan, A discriminatively trained, multiscale, deformable part model, in: Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
13. Y. Freund, R. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, in: European Conference on Computational Learning Theory, 1995, pp. 23-37.
14. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio Robust object recognition with cortex-like mechanisms IEEE Trans. Pattern Anal. Mach. Intell. 29 2007 411 426
15. V. Lepetit, P. Fua Keypoint recognition using randomized trees IEEE Trans. Pattern Anal. Mach. Intell. 28 9 2006 1465 1479
16. D. Cireşan, A. Giusti, L. Gambardella, J. Schmidhuber, Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images, in: Advances in Neural Information Processing Systems, 2012.
17. A. Handa, R. Newcombe, A. Angeli, A. Davison, Real-time camera tracking: when is high frame-rate best?, in: European Conference on Computer Vision, 2012, pp. 222-235.
18. B. Horn, B. Schunck Determining optical flow Artif. Intell. 17 1981 185 204
19. J.L. Barron, D.J. Fleet, S.S. Beauchemin Performance of optical flow techniques Int. J. Comput. Vision 12 1994 43 77
20. M. Stark, M. Goesele, B. Schiele, Back to the future: learning shape models from 3D CAD data, in: British Machine Vision Conference, 2010, pp. 1061- 10611.
21. J. Liebelt, C. Schmid, Multi-view object class detection with a 3D geometric model, in: Conference on Computer Vision and Pattern Recognition, 2010.
22. S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, R. Szeliski A database and evaluation methodology for optical flow Int. J. Comput. Vision 92 2011 1 31
23. B. Kaneva, A. Torralba, W. Freeman, Evaluation of image features using a photorealistic virtual world, in: International Conference on Computer Vision, 2011.
24. V. Athitsos, S. Sclaroff, Estimating 3D hand pose from a cluttered image, in: Conference on Computer Vision and Pattern Recognition, 2003, pp. 432-439.
25. L. Taycher, G. Shakhnarovich, D. Demirdjian, T. Darrell, Conditional random people: tracking humans with CRFs and grid filters, in: Conference on Computer Vision and Pattern Recognition, 2006.
26. G. Klein, D.W. Murray Simulating low-cost cameras for augmented reality compositing IEEE Trans. Visual Comput. Graphics 16 3 2010 369 380
27. S. Kirkpatrick, C. Gelatt, M. Vecchi Optimization by simulated annealing Science 220 4598 1983 671 680
28. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Conference on Computer Vision and Pattern Recognition, 2005.
29. K. Levi, Y. Weiss, Learning object detection from a small number of examples: the importance of good features, in: Conference on Computer Vision and Pattern Recognition, 2004.
30. T. Ruzic, A. Pizurica, Texture and color descriptors as a tool for context-aware patch-based image inpainting, in: SPIE Electronic Imaging, 2012.