Abstract
This paper presents an original low-level system for color image segmentation considering the Hue-Saturation-Value (HSV) color space. Many difficulties of color image segmentation may be resolved using the correct color space in order to increase the effectiveness of color components to discriminate color data. The technique proposed in the article uses new data structures that lead to simpler and more efficient segmentation algorithms. We introduce a flexible hexagonal network structure on the pixels image and we extract for each segmented region the syntactic features that can be used in the shape recognition process. Our technique has a time complexity lower than the methods studied from specialized literature and the experimental results on Berkeley Segmentation Dataset color image database show that the performance of method is robust.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gonzalez, W.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
Soille, P., Talbot, H.: Directional Morphological Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (November 2001)
Bashar, M.K., Matsumoto, T., Takeuchi, Y., Kudo, H., Ohnishi, N.: Unsupervised Texture Segmentation via Wavelet-based Locally Orderless Images (WLOIs) and SOM. In: Proceeding (398) Computer Graphics and Imaging (2003)
Makoto, M.: Mathematical Transform of (r,g,b) Colour Data to Munsell (h,s,v) Colour Data. SPIE Visual Communications and Image Processing, 1001 (1988)
Jacobs, D.: Robust and Efficient Detection of Salient Convex Groups. IEEE Transaction on Pattern Analysis and Machine Intelligence (1996)
Salembier, P., Marques, F.: Region-Based Representations of Image and Video: Segmentation Tools for Multimedia Services. IEEE Transaction on Circuits and Systems for Video Technology 9 (December 1999)
Beucher, S., Meyer, F.: The Morphological Approach to Segmentation: The Watershed Transformation. In: Mathematical Morphology in Image Processing. Marcel Dekker, New York (1993)
Canny, J.: Computational Approach to Edge Detection. IEEE Transaction Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Ma, W.Y., Manjunath, B.S.: EdgeFlow: A Technique for Boundary Detection and Image Segmentation. IEEE Transactions on Image Processing 9(8), 1375–1388 (2000)
Chen, T.W., Chen, Y.L., Chien, S.Y.: Fast Image Segmentation based on K-Means Clustering with Histograms in HSV Color Space. IEEE Multimedia Signal Processing (2008)
Krueger, M., Delmas, P., Gimel’farb, G.: Active Contour Based Segmentation of 3D Surfaces. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 350–363. Springer, Heidelberg (2008)
Rogers, D.F.: Procedural Elements for Computer Graphics. McGraw-Hill, New York (1985)
Smith, J.R., Chang, S.F.: VisualSEEk: A Fully Automated Content-Based Image Query System. In: ACM Multimedia, Boston, MA (November 1996)
Fowlkes, C., Martin, D., Malik, J.: Learning Affinity Functions for Image Segmentation: Combining Patch-based and Gradient-based Approaches. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 54–61 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Burdescu, D.D., Brezovan, M., Ganea, E., Stanescu, L. (2009). A New Method for Segmentation of Images Represented in a HSV Color Space. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-04697-1_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04696-4
Online ISBN: 978-3-642-04697-1
eBook Packages: Computer ScienceComputer Science (R0)