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This article presents a method to track non-Gaussian parametric probability density functions under nonlinear transformations and posterior calculations. The optimal set of parameters for the transformed distribution is a function of the parameters for the prior distribution and any other variables effecting the transformation. This function is approximated by a neural network using offline training...
This paper investigates the applicability of swarm-based algorithms to the game of Tetris. This work proposes an approach to the problem in which neural network weight values are optimized using a particle swarm optimization (PSO) algorithm. Such an approach has not previously been demonstrated as feasible for Tetris. The reported experimental results show the learning progress of the algorithm, as...
Orientation detection is an important preprocessing step for accurate recognition of text from document images. Many existing orientation detection techniques are based on the fact that in Roman script text ascenders occur more likely than descenders, but this approach is not applicable to document of other scripts like Urdu, Arabic, etc. In this paper, we propose a discriminative learning approach...
Starting with principles of neural network and genetic algorithm, new approach, combining genetic algorithm and neural network, of structure optimization were given. Structure optimum target function and design variables set were defined, and with learning algorithm of neural network, non-linear global mapping relationship, between design parameters such as weight, stress, displacement and etc., was...
Statistical model based facial expression synthesis methods are robust and easier to be used in real environment. But facial expressions of human are very various. How to represent and synthesize expressions which is not included in training set is an unresolved problem in statistical model based researches. In this paper, we propose a two step method. At first, we propose a statistical appearance...
This paper presents an improved active shape model algorithm, that exploits auto associative neural networks (AANNs) to estimate the local feature models. The proposed technique aims at solving face feature localization tasks, nevertheless it can be used also in the more general case of object detection. Three main contributions are presented. The first one consists in the estimation of elliptic search...
Shape recognition is an important part of machine intelligence in both decision making and data processing. A good shape representation in shape recognition should describe the shape in the way that makes it distinguishable from other shapes and be invariant to transform of position, size, angle and skew. More importantly, developing and finding appropriate shape representation are still a challenging...
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