The presented paper deals to PC modeling of scaled and rotated images recognition based on different types of distortion invariant correlation filters. There was used a database of different images (both false and true class) that are under geometrical distortions and there were calculated correlation outputs for images recognition with the help of MACE, MINACE, GMACE, DCCF and polynomial filters and it's combinations for two-stage recognition. The results provide a possibility for successful usage of implemented algorithms.
The presented paper is of PC modeling of scaled object recognition with the help of invariant correlation filters for
optical correlators. The object database consists of objects of true and false classes with different changes of scale. The
recognition process consits of two stages-multiclass recognition and geometrical change recognition with the help of
filters of different types. The results of modeling present data on comparison of different combination of filter types.
There are presented results of scaled image recognition modeling with the help of optical correlator with invariant correlation filters like MACE, GMACE, MINACE and DCCF. The image database for testing include images of true and false classes. There are presented qualitative and quantitative characteristics of output correlation peaks. Also there is provided an analysis of positive and negative side of each filter's type as for single class only scaled images recognition so for multiclass one. Also there are shown results for modeling of images with more complex distortions recognition using all mentioned types of filters.
The paper presents the results of computer modeling of scaled images recognition using MACE, GMACE and MINACE
invariant correlation filters. There is given data about testing of above mentioned filters on database of grayscale images
with different resolution that contains only true class and both true class and false class objects. There presented data
about output correlation peak qualitative and quantitative characteristics and about the comparison for different filter
types. The filters were synthesized specially for case of such geometrical transform as change of scale. Also there is
presented data about testing of mentioned filters for recognition of rotated images and given an analysis of results.
This paper present the results of computer simulations of pattern recognition using LPCC filters, designed for various types of distortions of input images (rotation, change of scale and rotation + change of scale).
One of the widespread methods for distorted patterns is to use a distortion invariant correlation filters. Invariant filters have different properties that are quite good for different pattern recognition problems. This paper presents the results of computer simulations of pattern recognition using different modern approaches on distortion invariant correlation filters. The different types of correlation filters (MACE, GMACE, LPCCF, WBKF and others) are compared for input test sets of different examples of patterns. There are presented results of pattern recognition for different types of distortions. The output correlation peaks are compared by its characteristics. The obtained results of comparison provide that in some cases there are correspondences between the choused correlation filter, the variant of pattern and type of the distortion for optimal output peak characteristics.
One of the main problems of optical data processing is the problem of image recognition. There were given much attention to optoelectronic methods of recognition of distorted images nowadays. There are a number of different approaches for the solution of such problem. One of the most popular approaches is using of optical correlators for this field. The main problem of this approach is to select an object to provide a correlation of input image with it. One of the widespread methods is to use an effective object-an invariant correlation filter. The paper presents the results of investigations on image recognition with the help of Wavelet Basis Kernel Filters (WBKF). Both results of the theory and computer simulations are presented. Also computer simulations hold a comparison of image recognition results with the help of other different approaches (GMACE, SDF and so on). The obtained results seem to be better for WBKF recognition in some cases. There are presented authors suggestions about using of WBKF filters for different distortion invariant image recognition problems and results of image recognition in presence of white noise.
Authors made a research in computer modeling of invariant correltaion filters design. The results of modeling shows good perspective of such approach for solution of image recognition problems. The aim of research is to find a type of filters that gives a stability of correlation peak for different geometrical distortions.
Authors made theoretical research in building of correlation filters, using direct decomposition of integral transforms on correspondent orthonormal basis. The obtained filters have different properties, which are quite good for different image recognition problems: 1). Such filters itself have all properties of elementary function; 2). From the family of such filters we can easily make MACE filters (authors made it for the case of wavelet transform); 3). Invariant properties of filters of this family are determined by the presence of such properties in integral transform, which is the base for construction of such filters (so we can select these properties by the selection of the type of transform). Also authors made experimental check-up of modeling of results on a computer. Results of modeling shows good perspective of such filters in solving of such problems.
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