fingertip detection with morphology and geometric calculation dung duc nguyen ; thien cong pham ;...
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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
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Outline
Introduction
Related Work
Proposed Method
Experimental Results
Conclusion
Introduction
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Introduction
Human computer interaction (HCI) systems are popular.
Voice
Gesture: detection, recognition
Gesture recognition system
Trajectories of hand motion
Hand configuration
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Introduction
Goal:
Extract hands using depth and color information.
Detect fingertips using morphology and geometric calculation.
The hand and expected position of fingertips.
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Outline
Introduction
Related Work
Proposed Method
Experimental Results
Conclusion
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Related Work (1/2)
How to recognize hands?Shape [2,5,6]
Color [11,14]
Entropy analysis on videos [7]
Color better than shapeShape can be recovered from skin region
???
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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]
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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.
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Outline
Introduction
Related Work
Proposed Method
Experimental Results
Conclusion
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Proposed Method
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• Mixture of Gaussian model• Experiment
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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.
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Skin Segmentation
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Skin Segmentation
Skin color distribution based on Mixture-of-Gaussian [13]
k : number of components
: weight factors of each component
Color similarity based on Mahalanobis distance
Skin : less than a specified threshold
(experiment = 3)
(experiment = 55)
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• Skin region using morphology• Disparity constraint
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Skin Objects
Skin objects by morphology to reduce noises
: Skin image ; : Binary image from skin image
and : kernel windows for erode and dilate operator
Disparity of objects
: Disparity image ;
Disparity range = [55;155]
erodedilate
http://tavmjong.free.fr/INKSCAPE/MANUAL/html/Filters-Pixel.html
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Hand Detection
Evaluation function 1.
k: number of criteria
: weight factor associated with
: similarity; : difference size; : disparity
Claim two hands
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Hand Detection
Evaluation function 2.
1) Arrange objects in descending order of area size.
2) Reject small and large objects
3) Reject objects which are not in the range given by
4) Filter candidates: choose the three largest objects in range [a;b]. They are the head and two hands.
5) 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.
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Hand Detection
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• Using morphology• Edge detector
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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
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Finger Detection
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Finger Detection
Subtraction result and Finger Response function
: edge image detected from the hand patch
: threshold function (75%)
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Proposed Method
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• Geometric calculation • Angle and length
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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
𝑿𝒆𝒂𝒇
𝑿𝒆
𝑿𝟎
𝑿
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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
𝑿 𝒊
𝑿𝒆
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Fingertip
Open:
Close:
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Proposed Method
Skin segmentationMixture of Gaussian model (Experiment)
Hand detectionMorphology with skin and disparity information
Finger detectionMorphology and edge detector
Relocating finger positionGeometric calculation
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Outline
Introduction
Related Work
Proposed Method
Experimental Results
Conclusion
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Experimental Results
Device : CPU AMD Althlon 4800+
Stereo resolution : 640x480
Runtime
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Experimental Results
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Experimental Results
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Experimental Results
Recognition rate (120 frames)Open fingers: 90-95%
Closed fingers: 10-20%
Reason: image quality and morphology operator
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Outline
Introduction
Related Work
Proposed Method
Experimental Results
Conclusion
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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