This paper presents an approach for reconstruction of procedural three-dimensional models of woody plants (trees). The used procedural tree model operates by recursively computing all building parts of a three-dimensional tree structure by applying a fixed procedure on a given large set of numerically coded input parameters. The parameterized procedural model can later be used for computer animation. Reconstruction of a parameterized procedural model from images is done by differential evolution algorithm which evolves this model by fitting a set of its rendered images to a set of given reference images. The comparison is done on pixel level of the images through the integration of distances to the nearest similar pixels. The obtained results show that the presented approach is viable for modeling of woody plants for computer animation by evolution of the numerically coded procedural model.
COBISS.SI-ID: 15175446
Many real-world optimization problems are largescale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problemsć particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Nonparametric statistical procedures were performed formultiple comparisons between the proposed algorithm and three wellknown algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.
COBISS.SI-ID: 14398230
The accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG signals from the biceps femoris muscle has been assessed in order to validate the decomposition in the case of highly-complex pinnate muscle architecture. It has been shown that the CKC method reliably identifies at least a subset of motor units in pennate muscles.
COBISS.SI-ID: 15519254