Counting colored objects using highlights

Henryk Palus

doi:10.1117/12.364310

16 September 1999 Counting colored objects using highlights

Henryk Palus

Proceedings Volume 3826, Polarization and Color Techniques in Industrial Inspection; (1999) https://doi.org/10.1117/12.364310
Event: Industrial Lasers and Inspection (EUROPTO Series), 1999, Munich, Germany

Abstract

Object counting in the scene can be used for visual inspection processes. Highlights are the characteristic bright spots occurring on the surfaces of individual objects. In this work the importance of highlights in image processing is described and some reflection models are briefly reviewed. First of all the possibilities of the Dichromatic Reflection Model (DRM) are presented. The paper presents a new idea of object counting based on highlights counting on surfaces of objects. Object counting is composed of following stages: extraction of highlights in color image by thresholding of selected IHS components, morphological consolidation of extracted highlight regions and region counting (labeling) in binary image. Object counting takes into consideration the number of used light sources, because in case of more than one lighting source multiple highlights per object are observed. This is realized without using reflection model. The proposed method was tested for a number of different, real world images. Input images were acquired directly from 1-CCD color camera without preprocessing. Best results were achieved for optically inhomogeneous (e.g. plastic) chromatic objects in dark background. Typical lighting system based on two fluorescent tubes (5400 K) was used. The method seems promising for practical applications.

Citation Download Citation

Henryk Palus "Counting colored objects using highlights", Proc. SPIE 3826, Polarization and Color Techniques in Industrial Inspection, (16 September 1999); https://doi.org/10.1117/12.364310

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KEYWORDS

RGB color model

Reflection

Visual process modeling

Light sources and illumination

Image segmentation

Binary data

Image processing

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