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Fingertip Detection with Morphology and Geometric Calculation

Fingertip Detection with Morphology and Geometric Calculation. Dung Duc Nguyen ;  Thien Cong Pham ; Jae Wook Jeon Intelligent Robots and Systems, 2009. IEEE/RSJ International Conference on. Outline. Introduction Related Work Proposed Method Experimental Results Conclusion.

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Fingertip Detection with Morphology and Geometric Calculation

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  1. Fingertip Detection with Morphology and Geometric Calculation Dung Duc Nguyen ; Thien Cong Pham ; Jae WookJeon Intelligent Robots and Systems, 2009. IEEE/RSJ International Conference on

  2. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion Introduction

  3. Introduction • Human computer interaction (HCI) systems are popular. • Voice • Gesture: detection, recognition • Gesture recognition system • Trajectories of hand motion • Hand configuration

  4. Introduction • Goal: • Extract hands using depth and color information. • Detect fingertips using morphology and geometric calculation. The hand and expected position of fingertips.

  5. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion

  6. Related Work (1/2) • How to recognize hands? • Shape [2,5,6] • Color [11,14] • Entropy analysis on videos [7] • Color better than shape • Shape can be recovered from skin region ???

  7. Related Work (2/2) • How to detection fingers? • Learning based (contour...)[7,3,14] • Analyzing hand structure (Gabor feature…)[9,10] • No feature (Shape) • Boundary Shape[8] • Five outside and four inside fingertips[5] • Curvature[2]

  8. Reference [2] W. Chen, R. Fujiki, D. Arita, and R. ichiro Taniguchi. Real-time 3d hand shape estimation based on image feature analysis and inverse kinematics. In Proc. of the 14th International Conference on Image Analysis and Processing (ICIAP), pages 247–252, Washington, DC, USA, 2007. IEEE Computer Society. [3] Y.-T. Chen and K.-T. Tsengn. Developing a multiple-angle hand gesture recognition system for human machine interactions. 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON), pages 489–492, 2007. [5] X. Jiang, W. Xu, L. Sweeney, Y. Li, R. Gross, and D. Yurovsky. New directions in contact free hand recognition. In International Conference on Image Processing (ICIP), volume 2, pages 389–392, 2007. [6] C. Kerdvibulvech and H. Saito. Vision-based detection of guitar players’ fingertips without markers. In Proc. of the Computer Graphics, Imaging and Visualisation (CGIV), pages 419–428. IEEE Computer Society, 2007. [7] J. Lee, Y. Lee, E. Lee, and S. Hong. Hand region extraction and gesture recognition from video stream with complex background through entropy analysis. Proc. of the 26th Annual International Conference of the IEEE EMBS, 2004. [8] Y. Ma, F. Pollick, and W. T. Hewitt. Using b-spline curves for hand recognition. In Proc. of 17th International Conference on the Pattern Recognition (ICPR), volume 3, pages 274–277, Washington, DC, USA, 2004. IEEE Computer Society. [11] S. Schmugge, M. A. Zaffar, L. V. Tsap, and M. C. Shin. Task-based evaluation of skin detection for communication and perceptual interfaces. Journal of Visual Communication and Image Representation (JVCIR), 18(6):487–495, 2007. [14] Y. Wu and T. S. Huang. View-independent recognition of hand postures. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 88–94, 2000.

  9. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion

  10. Proposed Method

  11. Mixture of Gaussian model • Experiment

  12. Skin Segmentation • Skin color distribution based on Mixture-of-Gaussian [13] • k : number of components • : weight factors of each component [13] V. Vezhnevets, V. Sazonov, and A. Andreeva. A survey on pixelbased skin color detection techniques. In Proc. of 13th International Conference of Computer Graphics and Visualization Graphicon, 2003.

  13. Skin Segmentation

  14. Skin Segmentation • Skin color distribution based on Mixture-of-Gaussian[13] • k : number of components • : weight factors of each component • Color similarity based on Mahalanobisdistance • Skin : less than a specified threshold • (experiment = 3) • (experiment = 55)

  15. Skin region using morphology • Disparity constraint

  16. Skin Objects erode dilate • Skin objects by morphology to reduce noises • : Skin image ; : Binary image from skin image • and : kernel windows for erodeand dilate operator • Disparity of objects • : Disparity image ; • Disparity range = [55;155] http://tavmjong.free.fr/INKSCAPE/MANUAL/html/Filters-Pixel.html

  17. Hand Detection • Evaluation function 1. • k: number of criteria • : weight factor associated with • : similarity; : difference size; : disparity • Claim two hands

  18. Hand Detection • Evaluation function 2. • Arrange objects in descending order of area size. • Reject small and large objects • Reject objects which are not in the range given by • Filter candidates: choose the three largest objects in range [a;b]. They are the head and two hands. • Reject the human head: the head can be eliminated by checking the relative position and size compared to other objects. The two remaining objects are hands.

  19. Hand Detection

  20. Using morphology • Edge detector

  21. Finger Detection • Finger characteristic ? • Long and thin • Morphology on gray images • : Opening => • : Gaussian kernel with deviation • : Morphology window size defined by disparity http://www.dspguide.com/ch25/4.htm

  22. Finger Detection

  23. Finger Detection • Subtraction result and Finger Response function • : edge image detected from the hand patch • : threshold function (75%)

  24. Proposed Method

  25. Geometric calculation • Angle and length

  26. Relocate Finger Position • Estimate average point of fingers • Center of hand palm • : weight factor • : major axis length; : minor axis length • : ratio between major and minor axis

  27. Relocate Finger Position • Classify open or closed finger vector of finger center to hand center angle of the ith finger = distance between finger center and hand center • Open finger • length > • Close finger • length

  28. Fingertip • Open: • Close:

  29. Proposed Method • Skin segmentation • Mixture of Gaussian model (Experiment) • Hand detection • Morphology with skin and disparity information • Finger detection • Morphology and edge detector • Relocating finger position • Geometric calculation

  30. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion

  31. Experimental Results • Device : CPU AMD Althlon 4800+ • Stereo resolution : 640x480 • Runtime

  32. Experimental Results

  33. Experimental Results

  34. Experimental Results • Recognition rate (120 frames) • Open fingers: 90-95% • Closed fingers: 10-20% • Reason: image quality and morphology operator

  35. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion

  36. Conclusion • Proposed a simple and effective method to detect fingertips. • Noise tolerance • Good performance of run-time • Depend on experiment too much • Future work • Improve finger response function • Gradient and texture information • Adaptive skin detector

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