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Algorithms for object recognition based on invariant grey scale features

Nonlinear transforms for object recognition research focused on the development of invariants for grey scale images. From the extension of the theory of invariants to the group of motions in space, important new results arose.

Invariant grey scale features are characteristics of grey scale images which remain constant if the images are transformed according to the action of a transformation group. Such features have several applications in computer vision and pattern. Algorithms for determining translation- and rotation invariant features from grey scale images. Have been developed. These features are calculated in two steps. First, a local nonlinear function has to be evaluated for every pixel of the image and afterwards the results of the local computations must be summed. This strategy can be used to determine the properties of the features for scenes with more than one object. It is explained how to construct features which are invariant even if the objects in the scene are rotated and translated independently. Moderate occlusions are tolerable. Furthermore it is shown how to use these techniques for the recognition of articulated objects. It has to be emphasized that the algorithms work directly with the grey values and do not rely on the extraction of geometric primitives like edges or corners in a preprocessing step. All algorithms have been implemented and tested both on synthetic and real image data. The algorithms have been implemented on a massively parallel system using the parallel image processing library developed in the project. An increase in the adaptivity of the features can be achieved by a parameterized calculation. Inspired by neural network, techniques have been developed a feedforward network. These techniques can be expected to have applications ,for example, in visual inspection and quality control.

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Albert-Ludwigs-Universitat Freiburg
Georges-Köhler-Allee 52
79100 Freiburg
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