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The Slit Island Method (SIM) is a technique for the estimation of the fractal dimension of an object by determining the areaperimeter relations for successive slits. The SIM could be applied for image analysis of irregular grayscale objects and their classification using the fractal dimension. It is known that this technique is not functional in some cases. It is emphasized in this paper that for specific objects a negative or an infinite fractal dimension could be obtained. The transformation of the input image data from unipolar to bipolar gives a possibility of reformulated image analysis using the Ising model context. The polynomial approximation of the obtained area-perimeter curve allows object classification. The proposed technique is applied to the images of cervical cell nuclei (Papanicolaou smears) for the preclassification of the correct and atypical cells.

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Track-before-detect (TBD) algorithms are used for tracking systems, where the object's signal is below the noise floor (low-SNR objects). A lot of computations and memory transfers for real-time signal processing are necessary. GPGPU in parallel processing devices for TBD algorithms is well suited. Finding optimal or suboptimal code, due to lack of documentation for low-level programming of GPGPUs is not possible. High-level code optimization is necessary and the evolutionary approach, based on the single parent and single child is considered, that is local search approach. Brute force search technique is not feasible, because there are N! code variants, where N is the number of motion vectors components. The proposed evolutionary operator-LREI (local random extraction and insertion) allows source code reordering for the reduction of computation time due to better organization of memory transfer and the texture cache content. The starting point, based on the sorting and the minimal execution time metric is proposed. The unbiased random and biased sorting techniques are compared using experimental approach. Tests shows significant improvements of the computation speed, about 8 % over the conventional code for CUDA code. The time period of optimization for the sample code is about 1 h (1,000 iterations) for the considered recursive spatio-temporal TBD algorithm.

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