Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2014 | OriginalPaper | Chapter

3. Pre-processing

Author : Prashan Premaratne

Published in: Human Computer Interaction Using Hand Gestures

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Computer vision is aimed at simulating the human visual system in order to extract useful information for machines to make decisions. A visual camera is usually used for this purpose which detects brightness, colour, texture and dimensions of an object in focus. When a camera captures scenery, it contains both ‘wanted’ as well as ‘unwanted’ information. If the camera is focussed on a person’s hand looking for a possible gesture, then the ‘unwanted’ objects in the scenery would be the background which may contain the person’s body, clothing, other people, pets, walls, windows, curtains or any other equipment. Since the system is developed to respond to gestures, the system would try to extract only the ‘wanted’ information. However, as the system would not have the level of intelligence as a human, it relies on ‘clues’ to extract only the ‘wanted’ objects.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Historical Development of Hand Gesture Recognition
next chapter Feature Extraction
Literature
1.
Abdel-Mottaleb, M., Elgammal, A.: Face detection in complex environments from color lmages. Proceedings of the International Conference on Image Processing (ICIP), 622–626 (1999)
2.
Alshebani, Q., Premaratne, P., Vial, P.: An Embedded Door Access Based on Face Recognition System: A Survey. To appear in (ICSPCS), 2013, Australia, (2013)
3.
Ahmed, E., Crystal, M., Dunxu H.: Skin Detection-a short Tutorial. Encyclopedia of Biometrics by Springer-Verlag Berlin, Heidelberg, 1218–1224 (2009)
4.
Forsyth, D.A., Fleck, M.M.: Identifying nude pictures. Proceeding of Third IEEE Workshop on Applications of Computer Vision, 103–108 (1996)
5.
Albiol, A., Torres, L., Delp, E.: Optimum color spaces for skin detection. In: Proceedings of the International Conference on Image Processing (ICIP), 122–124 (2001)
6.
Shin, M.C., Chang, K.I., Tsap, L.V.: Does colorspace transformation make any difference on skin detection? WACV ’02: Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, 275 (2002)
7.
Zheng, Q.F., Zhang, M.J., Wang, W.Q.: A hybrid approach to detect adult web images. PCM 2 3332, 609–616 (2004)
8.
Lee, Y., Yoo, S.I.: An elliptical boundary model for skin color detection. In: Proceedings of the International Conference on Imaging Science, Systems, and Technology, (2002)
9.
Senior, A., Hsu, R.L., Mottaleb, M.A., Jain, A.K.: Face detection in color images. IEEE Trans. PAMI 24(5), 696–706 (2002)CrossRef
10.
Menser, B., Wien, M.: Segmentation and tracking of facial regions in color image sequences. Proceeding of SPIE Visual Communications and Image Processing, 731–740 (2000)
11.
Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. In: Proceeding of CVPR’99 1, 274–280 (1999)
12.
Beetz, M., Radig, B., Wimmer, M.: A person and context specific approach for skin color classification. 18th International Conference on Pattern Recognition (ICPR 2006), (2006)
13.
Soriano, M., et al.: Skin detection in video under changing illumination conditions. 15th International Conference on Pattern Recognition, (2000)
14.
Kawato, S., Ohya, J.: Automatic skin-color distribution extraction for face detection and tracking. 5th International Conference on Signal Processing Proceedings (WCCC-ICSP 2000), (2000)
15.
Park, J., et al.: Detection of human faces using skin color and eyes, IEEE International Conference on Multimedia and Expo (ICME 2000), (2000)
16.
Kovac, J., Peer, P., Solina, F.: 2D versus 3D color space face detection. 4th EURASIP Conference on Video/Image Processing and Multimedia Communications, 449–454 (2003)
17.
Gomez, G., Morales, E.F.: Automatic feature construction and a simple rule induction algorithm for skin detection. Proceedings of ICML workshop on Machine Learning in Computer Vision, 31–38 (2002)
18.
Gasparini, F., Schettini, R.: Skin Segmentation using Multiple Thresholding. Proceedings of SPIE 6061, 128–135 (2006)
19.
Vezhnevets, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-Based Skin Color Detection Techniques, In Proceedings of GRAPHICON-2003, (2003)
20.
Zarit, B.D., Super, B.J., Quek, F.K.H.: Comparison of five color models in skin pixel classification. ICCV’99 Int’l Workshop on recognition, analysis and tracking of faces and gestures in Real-Time systems, 58–63 (1999)
21.
Hsu, R.-L., Abdel-Motalleb, M., Jain, A. K.: Face detection in color images. IEEE Trans. PAMI 24(5), 696–706 (2002)CrossRef
22.
Ahlberg, J.: A system for face localization and facial feature extraction. Technical Report no. LiTH-ISY-R-2172, Linkoping University, (1999)
23.
Sebastian, P., Yap, V.V., Comley, R.: The effect of colour space on tracking robustness. 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), 2512–2516 (2008)
24.
Tsekeridou, S., Pitas, I.: Facial feature extraction in frontal views using biometric analogies. Proceedings of IX European Signal Processing Conference 1, 315–318 (1998)
25.
Garcia, C., Tziritas, G.: Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Transaction on Multimedia. 1, 264–277 (1999)CrossRef
26.
Poynton, C.A..: Frequently Asked Questions About Colour. In ftp://www.inforamp.net/pub/users/poynton/doc/colour/ColorFAQ.ps.gz (1995)
27.
Skarbek, W., Koschan, A.: Colour image segmentation—a survey. Technical Report, Institute for Technical Informatics, Technical University of Berlin, (1994)
28.
Sigal, L., Sclaroff, S., Athitsos, V.: Estimation and prediction of evolving color distributions for skin segmentation under varying illumination. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2, 152–159 (2000)
29.
Mckenna, S., Gong, S., Raja, Y.: Modelling facial colour and identity with gaussian mixtures. Pattern Recognit 31, 12, 1883–1892 (1998)
30.
Jordao, L., Perrone, M., Costeira, J., Santos-Victor, J.: Active face and feature tracking. In Proceedings of the 10th International Conference on Image Analysis and Processing, 572–577 (1999)
31.
Fleck, M., Forsyth, D.A., Bregler, C.: Finding nacked people. In Proceedings of the ECCV 2, 592–602 (2002)
32.
Brown, D., Craw, I., Lewthwaite, J.: A som based approach to skin detection with application in real time systems. In Proceedings of the British Machine Vision Conference, (2001)
33.
34.
Terrillon, J.-C., Shirazi, M.N., Fukamachi, H., Akamatsu, S.: Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In Proceedings of the International Conference on Face and Gesture Recognition, 54–61 (2000)
35.
Poynton, C., Funt, B.: Perceptual uniformity in digital image Representation and display. Color Research and Applications, (2013)
36.
Kaur, A., Kranthi, B.V.: Comparison between YCbCr color space and CIELab color space for skin color segmentation. Int. J. Appl. Info. Syst. 3(4), 30–33 (2012)
37.
Singh, S.K., Chauhan, D.S., Mayank, V., Singh, R.: A robust skin color based face detection algorithm. Tamkang J. Sci. Engg. 6(4), 227–234 (2003)
38.
Khan, R., Khan, Z., Aamir, M., Sattar, S.Q.: Static filtered skin detection. IJCSI International Journal of Computer Science Issues. 9(2), 257–261 (2012)
39.
Poudel, R.P.K., Nait-Charif, H., Zhang, J.J., Liu, D.: Region-based skin color detection. VISAPP 1, 301–306 (2012)
40.
Hikal, N.H., Kountchev, R.: Skin color segmentation using adaptive PCA and modified elliptic boundary model. ICACSIS. 2011, 407–412 (2011)
41.
Chen, Q., Wu, H., Yachida, M.: Face detection by fuzzy pattern matching. In Proceedings of the Fifth International Conference on Computer Vision, 591–597 (1995)
42.
Schumeyer, R., Barner, K.: A color-based classifier for region identification in video. Vis. Commun. Image Process. SPIE. 3309, 189–200 (1998)CrossRef
43.
Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In Proceedings of CVPR ’98, 232–237 (1998)
44.
Yang, M.H., Ahuja, N.: Detecting human faces in color images. In International Conference on Image Processing 1, 127–130 (1998)
45.
Kruppa, H., Bauer, M., Schiele, B.: Skin patch detection in real-world images. In: Van Gool, L. (ed.), Pattern Recognition, Lecture Notes in Computer Science 2449, 109–116 (2002)
46.
Chang, F., Ma, Z., Tian, W.: A region-based skin color detection algorithm advances in knowledge discovery and data mining. Lecture Notes in Computer Science 4426, 417–424 (2007)
47.
Ren, X., Malik, J.: Learning a classification model for segmentation. In IEEE International Conference on Computer Vision 1, 10–17 (2003)
48.
Moore, A.P., Prince, S., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In IEEE Conference on Computer Vision and Pattern Recognition, 1–8 (2008)
49.
Soatto, S.: Actionable information in vision. In Proceedings of the International Conference on Computer Vision 25, 17–48 (2009)
50.
Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In Proceedings of International Conference on Computer Vision 5, 670–677 (2009)
51.
Brand, J., Mason, J.: A comparative assessment of three approaches to pixellevel human skin-detection. In Proceedings of the International Conference on Pattern Recognition 1, 1056–1059 (2000)
52.
Soille, P.: Morphological Image Analysis Principles and Applications, 2nd ed., XVI, 391 (2003)
53.
54.
Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing, Chap. 25.
55.
56.
Gonzalez, R., Woods, R.: Digital Image Processing, Addison-Wesley Publishing Company, 518–548 (1992)
57.
Davies, E.: Machine Vision: Theory, Algorithms and Practicalities, Academic Press, 149–161 (1990)
58.
Haralick, R., Shapiro, L.: Computer and Robot Vision 1, Addison-Wesley Publishing Company, Chap. 5, 168–173 (1992)
59.
Jain, A.: Fundamentals of Digital Image Processing, Prentice-Hall, Chap. 9. (1989)
60.
Vernon, D.: Machine Vision, Prentice-Hall, Chap. 4 (1991)
61.
Ionescu, B., Coquin, D.: Dynamic hand gesture recognition using the skeleton of the hand. EURASIP J. Appl. Signal Process. 13, 2101–2109 (2005)CrossRef
62.
Coquin, D., Bolon, P.: Applications of Baddeley’s distance to dissimilarity measurement between gray scale images. Pattern Recognit. Lett. 22(14), 1483–1502 (2001)CrossRefMATH
63.
Reddy, K.S., Latha, P.S., Babu, M.R.: Hand Gesture Recognition Using Skeleton of Hand and Distance Based Metric, D.C. Wyld et al. (eds.) ACITY 2011, CCIS, 198, 346–354 (2011)
64.
Borgefors, G.: Distance transformations in digital images. Comp. Vis. Graphics Image Process. 34(3), 344–371 (1986)CrossRef
65.
Chehadeh, Y., Coquin, D., Bolon, H.: A skeletonization algorithm using chamfer distance transformation adapted to rectangular grids. In: Proceedings of 13th IEEE International Conference on Pattern Recognition (ICPR 1996) 2, 131–135 (1996)
66.
Hasthorpe, J., Mount, N.: The generation of river channel skeletons from binary images using raster thinning algorithms. School of Geography, University of Nottingham
67.
Wu, S., Jiang, F., Zhao, D.: Hand Gesture Recognition based on Skeleton of Point Clouds. 2012 IEEE fifth International Conference on Advanced Computational Intelligence (ICACI), 566–569 (2012)
68.
Premaratne, P., Ajaz, S., Premaratne, M.: Hand Gesture Tracking and Recognition System Using Lucas-Kanade Algorithm for Control of Consumer Electronics. Neurocomputing Journal, (2012)
69.
Premaratne, P., Nguyen, Q.: Consumer electronics control system based on hand gesture moment invariants. IET Comp. Vis. 1(1), 35–41 (2007)CrossRef
70.
Zou, Z., Premaratne, P., Premaratne, M., Monaragala, R., Bandara, N.: Dynamic hand gesture recognition system using moment invariants. 5th International Conference on Information and Automation for Sustainability, 108–113 (2010)
71.
Herath, D.C., Kroos, C., Stevens, C.J., Cavedon, L., Premaratne, P.: Thinking head: Towards human centred robotics. 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2042–2047 (2010)
72.
Premaratne, P., Ajaz, S., Premaratne, M.: Hand Gesture Tracking and Recognition System for Control of Consumer Electronics. Springer Lecture Notes in Artificial Intelligence (LNAI) 6839, 588–593 (2011)
73.
Premaratne, P., Nguyen, Q., Premaratne, M.: Human computer interaction using hand gestures. Adv. Intell. Comput. Theor. Appl. Commun. Comput. Info. Sci. 93, 381–386 (2010)
74.
Premaratne, P., Safaei, F., Nguyen, Q.: Moment invariant based control system using hand gestures Intelligent Computing in Signal Processing and Pattern recognition, Book Series Lecture Notes in Control and Information Sciences vol. 345, 322–333 (2006)
75.
Premaratne, P., Safaei, F.: Feature based Stereo Correspondence using Moment Invariant. Proceedings of the IEEE International Conference on Information and Automation for Sustainability, 104–108 (2008)
76.
McGuire, D., Premaratne, P.: A System for the 3D Reconstruction of the Human Face using the Structured Light Approach. The 5th Workshop on the Internet Telecommunications and Signal Processing, 1–7 (2006)
77.
Ding, Y., Ping, X., Hu, M., Wang, D.: Range image segmentation using randomized Hough transform. In Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on 2, 807–811 (2003)
78.
Jiang, X., Bunke, H.: Edge Detection in Range Images Based on Scan Line Approximation. Comp. Vis. Image Underst. 73(2), 183–199 (1999)CrossRef
79.
Besl, P.J., Jain, R.C.: Segmentation through Variable-Order Surface Fitting. IEEE Trans. Pattern Anal. Mach. Intell. 10(2), 167–192 (1988)CrossRef
80.
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. International Conference on Computer Vision, (2011)
81.
Leutenegger, S., Chli, M., Siegwart, R.: Brisk: Binary robust invariant scalable keypoints. In Dimitris N. Metaxas, Long Quan, Alberto Sanfeliu, Luc J. Van Gool (eds.) ICCV, 2548–2555 (2011)
82.
83.
OpenCV, W.G.: Opencv 2.4.5.0 documentation. (2013)
84.
Herrera, D.C., Kannala, J., Heikkil, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2058–2064 (2012)CrossRef
85.
Howard, I., Rogers, B.: Seeing in depth. (2002)
86.
Coutant, B.E., Westheimer, J.: Population distribution of stereoscopic ability. Ophthal. Physiol. Optics. 13(1), 3–7 (1993)CrossRef
87.
Liesbeth, I.N., Mazyn, Lenoir, M., Montagne, G., Geert, J., Savelsbergh, P.: The contribution of stereo vision to one-handed catching. Exp. Brain Res. 157(3), 383–390 (2004)
88.
Salas, J., Tomasi, C.: People detection using color and depth images. Pattern Recognition, Lecture Notes in Computer Science 6718, 27–135 (2011)
89.
Payeur, P., Desjardins, D.: Structured light stereoscopic imaging with dynamic pseudo-random patterns. Image Analysis and Recognition. Lect. Notes Comput. Sci. 5627, 687–696 (2009)CrossRef
90.
Desjardins, D., Payeur, P.: Dense stereo range sensing with marching pseudo-random patterns. Fourth Canadian Conference on Computer and Robot Vision (CRV ’07), 216–226 (2007)
91.
Grin, P.M., Narasimhan, L.S., Yee, S.R.: Generation of uniquely encoded light patterns for range data acquisition. Pattern Recog. 25(6), 609–616 (1992)CrossRef
92.
Morita, H., Yajima, K., Sakata, S.: Reconstruction of surfaces of 3D objects by M-array pattern projection method. Second International Conference on Computer Vision, 468–473 (1998)
93.
Salvi, J., Pagès, J., Batlle, J.: Pattern codification strategies in structured light systems. Pattern Recognit. 37(4), 827–849 (2004)CrossRefMATH
94.
van Aardenne-Ehrenfest, T., de Bruijn, N.G.: Circuits and trees in oriented linear graphs. Simon Stevin. 28, 203–217 (1951)MATHMathSciNet
95.
Han, Y.K., Yang, K.: New M-ary power residue sequence families with low correlation. Proceedings of IEEE International Symposium on Information Theory (ISIT2007), 2616–2620 (2007)
96.
Han, Y.K., Yang, K.: New M-ary sequence families with low correlation and large size. IEEE Trans. Inf. Theory 55(4), 1815–1823 (2009)CrossRefMathSciNet
97.
Kim, Y.-S., Chung, J.-S., No, J.-S.: and Chung, H.: New families of M-ary sequences with low correlation constructed from Sidel’nikov sequences. IEEE Trans. Inf. Theory 54(8), 3768–3774 (2008)CrossRefMATHMathSciNet
98.
Zhang, L., Cudess, B., Seitz, M.: Rapid Shape Acquisition Using Color Structured Lightand Multi-pass Dynamic Programming. 1st IEEE International Symposium on 3D Data Processing, Visualization, and Transmission, 1–13 (2002)
99.
Vuylsteke, P., Oosterlinck, A.: Range image acquisition with a single binary-encoded light pattern. Pattern Analy. Mach. Intell. 12(2), 148–163 (1990)CrossRef
100.
Carrihill, B., Hummel, R.: Experiments with the intensity ratio depth sensor. Comp. Vis. Graphics Image Process. 32, 337–358 (1985)CrossRef
101.
Hung, D.: 3d scene modelling by sinusoid encoded illumination. Image Visi. Comp. 11, 251–256 (1993)CrossRef
102.
Tajima, J., Iwakawa, M.: 3-D data acquisition by rainbow range finder. International Conference on Pattern Recognition, 309–313 (1990)
103.
Geng, Z.J.: Rainbow 3-dimensional camera new concept of high-speed 3-dimensional vision systems. Opt. Eng. 35(2), 376–383 (1996)CrossRef
104.
Wust, C., Capson, D.W.: Surface profile measurement using color fringe projection Mach. Vis. Appl. 4, 193–203 (1991)CrossRef
105.
Sato, T.: Multispectral pattern projection range finder. Proceedings of the Conference on Three-Dimensional Image Capture and Applications II 3640, SPIE, 28–37 (1999)
106.
Morano, R.A., Ozturk, C., Conn, C., Dubin, S., Zietz, S., Nissanov, J.: Structured light using pseudorandom codes. Pattern Anal. Mach. Intell. 20(3), 322–327 (1998)CrossRef
107.
108.
109.
110.
111.
Katz, S.: Boxing with ZCam. Engineering TV. (2009)
112.
Iddan, G.J., Yahav, G.: 3D imaging in the studio. Proceedings of SPIE 4298, (2003)
113.
Iddan, G.J., Yahav, G.: 3D imaging in the studio.Three-Dimensional Image Capture and Applications IV, Brian D.C., Joseph H.N., Roy P.P. (eds.), Proceedings of SPIE 4298, 48–55 (2001)
Metadata
Title
Pre-processing
Author
Prashan Premaratne
Copyright Year
2014
Publisher
Springer Singapore
Book
Human Computer Interaction Using Hand Gestures

Print ISBN: 978-981-4585-68-2

Electronic ISBN: 978-981-4585-69-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-981-4585-69-9_3

Premium Partner

    Image Credits