A machine-vision system for real-time classification of moving objects with highly reflective metallic surfaces and complex 3D-structures is presented. As an application example of our Three-Color Selective Stereo Gradient Method (Three-Color SSGM) a classification system for the three main coin denominations of Euro coins is presented. The coins are quickly moving in a coin validation system. The objective is to decide only from comparison of measured 3D-surface properties with characteristic topographical data stored in a database whether a coin belongs to one of the reference classes or not. Under illumination of a three-color LED-ring a single image of the moving coin is captured by a CCD-camera. Exploiting the spectral properties of the illumination sources, which correspond to the special spectral characteristics of the camera, three independent subimages can be extracted from the first. Comparison between these subimages leads to a discrimination between a coin with real 3D-surface and a photographic image of a coin of the same type. After the coin has been located and segmented, grey value based rotation and translation invariant features are extracted from a normalized image. In combination with template matching methods, a coin can be classified. Statistical classification results will be reported.
This paper presents an enhanced vision system of the "Selective
Stereo Gradient Method" (SSGM). Its purpose is the detection of
topographies of highly reflective, metallic surfaces of quickly
moving metallic tokens. We call this vision system the
3-Color-SSGM. It represents a decisive improvement of the
serial-SSGM. The objective is to decide from comparison of the
measured characteristic surface topography with topographical data
stored in a database whether the token belongs to a reference
class or not. In the improved SSGM a 3 sector 120° color
LED-illumination setup is used for generating a single image of a
moving object. Using the spectral properties of the illumination,
which matches to the special spectral characteristics of the
camera, three independent images can be extracted. The comparison
between these images leads to a discrimination between a real
object with 3D topography and a photographic image. The
experimental setup and special illumination conditions are
described. The raw data images are segmented and scaled. Rotation
and translation invariance of the recognition and classification
process are implemented. A specimen can be classified by using
statistical image analysis and template matching methods. The
classification statistics results will be reported.
This paper presents a vision system whose purpose is to detect topographies of high reflective, metallic surfaces of minted tokens. We call this technique 'Selective Stereo Gradient Method' (SSGM). The objective is to decide whether the token belongs to a reference class or not. The most important property of the SSGM is that the classification can not be deceived by a photographic image and hence yields high fraud protection. To achieve this a 3 sector 120# LED illumination is used for generating three images under different illumination directions. The comparison between these three sequentially taken images leads to a discrimination between a real object with 3 D topography and a photographic image. The experimental setup and special illumination conditions are described. Rotation and translation invariance of the recognition and classification process are implemented. This is achieved by image transformation into a suitable coordinate system. A specimen will be identified to belong to the class of interest if, in a subsequent template matching step, selected patterns taken from the class reference object, can be successfully identified. If a first pattern is found additional patterns will be searched for. The classification statistics results will be reported for metallic tokens.