Abstract
An efficient method to detect and extract color objects from a cluttered scene based on Statistical and spatial color similarity is proposed. Color region adjacency graphs (CRAG) and six 1-D histograms corresponding to the RGB and HIS color spaces are used to represent models and scenes. A histogram intersection (Hl) strategy is applied to a similarity measure of Statistical color distribution between them and the CRAGs are exploited to guide the search for the interesting object regions at which a global maximal value of histogram intersection is available. The color spatial relationships among the CRAGs are also used to check the matching result to avoid the false positive identifications, which may be caused by a normal HI method. This strategy of combining CRAG and HI makes the detection robuster and preciser. The experiments conducted have shown that known color objects in a complex scene can be accurately identified and extracted from the background
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References
W.F.L. Grimson, Object Recognition by Computer: The Role of Geometric Constraints. MIT Press. Cambridge, Massachusetts (1990).
P. Suetens, P. Fua and A.J. Hanson, Some computational strategies for object recognition. Suveys 24(1) (March 1992).
M.J. Swain and D.H. Ballard, Color Indexing. Int. J. Comput. Vision 7(1), 11–32(November 1991).
F. Ennesser and G. Medioni, Finding Waldo, or focus of attention using local color information. IEEE Trans. Pattem Analysis Mach. Intell. 17, 805–809(1995).
V.V. Vinod and H. Murase, Focused color intersection with efficient searching for object extraction. Pattem Recognition 30(10), 1787–1797(1997).
R. Schettini, Multicolored object recognition and location, Pattem Recognition Lett. 15, 1089–1097(1994).
H. Murase and S.K. Nayar, Detection of 3D objects in cluttered scenes using hierarchical eigenspace. Pattem Recognition Lett. 18. 375–384(1997).
A.K. Jain and A. Vailaya, Image retrieval using color and shape. Pattem Recognition. 29(8) 1233–1244(1996).
T.F. Syeda-Mahmood, Data and model-driven selection using color regions. AI-Memo 1270, Artificial Intelligence Lab., MIT (1992)
A. Del Bimbo, M. Mugnaini, P. Pala and F. Turco,Visual querying by color perceptive regions. Pattem Recognition, 31(9), 1241–1253(1998).
F.M. Wahl, Digitale Bildsignalverarbeitung. Berlin, Heidelberg, New York, Tokio, Springer Verlag(1984).
T. Gevers and A.W.M. Smeulders, Color-based object recognition. Pattem Recognition, 32,453–464(1999)
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Cheng, J., Drüe, S., Hartmann, G. (2000). Graph Based Histogram Intersection for Efficient Location of Color Objects. In: Sommer, G., Krüger, N., Perwass, C. (eds) Mustererkennung 2000. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59802-9_38
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DOI: https://doi.org/10.1007/978-3-642-59802-9_38
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