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The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera's internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%. [ABSTRACT FROM AUTHOR]

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient. [ABSTRACT FROM AUTHOR]

This paper deals with the application of neural networks to design intelligent nonlinear predictive controllers. Predictive controllers are now widely used in many industrial applications. They have been used for linear systems in early applications and then some methods based on predictive control theory were proposed to govern the dynamics of nonlinear systems. In this paper, we will make use of multi-layer perceptron neurofuzzy models with Locally Linear Model Tree (LoLiMoT) learning algorithm as a part of intelligent predictive control system, which has shown excellent performance in identifying of nonlinear systems. The nonlinear dynamics of the system is identified using the neural network based method and then the identified model is used as a part of predictive control algorithm. The proposed method is used to solve the control problems in some benchmark systems. As a first study, the viscosity control in a Continuous Stirred Tank Reactor (CSTR) plant is considered. The mathematical model of the plant is used to generate the input output data set and then the dynamic behavior of the system is identified using a proper multi-layer perceptron neural network, which is used in the predictive control loop. Also, the predictive control based on the locally linear neurofuzzy model is applied to temperature control of an electrically heated micro heat exchanger. The dynamic behavior of the heat exchanger is identified based on some experimental data of the real plant. Comparing the identification results obtained by the neurofuzzy model with those of some linear models such as ARX and BJ, confirms the superior performance for the locally linear neurofuzzy model. Then, the predictive control is applied to the identified model to obtain a satisfactory performance in the output temperature that should track a desired reference signal. As another application, the algorithm is applied to temperature control of a solution polymerization methyl methacrylate in a batch reactor. The results show also somehow satisfactory performance for this highly nonlinear system. All the simulation results reveal the effectiveness of the proposed intelligent control strategy. [ABSTRACT FROM AUTHOR]

Conjugate gradient methods constitute excellent neural network training methods which are characterized by their simplicity and their very low memory requirements. In this paper, we propose a new spectral conjugate gradient method which guarantees sufficient descent using any line search, avoiding thereby the usually inefficient restarts. Moreover, we establish the global convergence of our proposed method under some assumptions. Experimental results provide evidence that our proposed method is preferable and in general superior to the classical conjugate gradient methods in terms of efficiency and robustness. [ABSTRACT FROM AUTHOR]

The development of satellite image acquisition tools helped improving the extraction of information about natural scenes. In the proposed approach, we try to minimize imperfections accompanying the image interpretation process and to maximize useful information extracted from these images through the use of blind source separation (BSS) and fusion methods. In order to extract maximum information from multi-sensor images, we propose to use three algorithms of BSS that are FAST- ICA2D, JADE2D, and SOBI2D. Then by employing various fusion methods such as the probability, possibility, and evidence methods we can minimize both imprecision and uncertainty. In this paper, we propose a hybrid approach based on five main steps. The first step is to apply the three BSS algorithms to the satellites images; it results in obtaining a set of image sources representing each a facet of the land cover. A second step is to choose the image having the maximum of kurtosis and negentropy. After the BSS evaluation, we proceed to the training step using neural networks. The goal of this step is to provide learning regions which are useful for the fusion step. The next step consists in choosing the best adapted fusion method for the selected source images through a case-based reasoning (CBR) module. If the CBR module does not contain a case similar to the one we are seeking, we proceed to apply the three fusion methods. The evaluation of fusion methods is a necessary step for the learning process of our CBR. [ABSTRACT FROM AUTHOR]

In this paper, the problem of stability analysis of neural networks with discrete time-varying delays is considered. By constructing a new Lyapunov functional and some novel analysis techniques, new delay-dependent criteria for checking the asymptotic stability of the neural networks are established. The criteria are presented in terms of linear matrix inequalities, which can be easily solved and checked by various convex optimization algorithms. Three numerical examples are included to show the superiority of our results. [ABSTRACT FROM AUTHOR]

Motion blur is one of the most common causes of image corruptions caused by blurring. Several methods have been presented up to now, which precisely identify linear motion blur parameters, but most of them possessed low precision in the presence of the noise. The present paper is aimed to introduce an algorithm for estimating linear motion blur parameters in noisy images. This study presents a method to estimate motion direction by using Radon transform, which is followed by the application of two other different methods to estimate motion length; the first of which is based on one-dimensional power spectrum to estimate parameters of noise free images and the second uses bispectrum modeling in noisy images. A Feed-Forward Back Propagation neural network has been designed on the basis of Weierstrass approximation theorem to model bispectrum and the Delta rule as the network learning rule. The methods were tested on several standard images like Camera man, Lena, Lake, etc. that were degraded by linear motion blur and additive noise. The experimental results have been satisfactory. The proposed method, compared to other related methods, suggests an improvement in the supported lowest SNR and precision of estimation. [ABSTRACT FROM AUTHOR]

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing. [ABSTRACT FROM AUTHOR]

Page segmentation is necessary for optical character recognition and very useful in document image manipulation. This paper describes two classification methods, a modified linear adaptive method and a proposed neural network system that classifies an image into text, halftone image (photos, dark images, etc.), and graphics (graphs, tables, flowcharts, etc.). The blocks were segmented using the Run Length Smearing Algorithm. The smearing process was done automatically by fixing the threshold values for smearing. Features are extracted from the segmented blocks for classification into text, graphics, and halftone images. The second method uses a multi-layer perceptron neural network for classification. Two parameters, a shape factor, f1, and an angle from the rectangular block segments, were fed into the neural network system giving us three classes: text, halftone images, and graphics. Experiments on 30 mixed-content document images show that the method works well on a wide variety of layouts in document images. [ABSTRACT FROM AUTHOR]