Application of using fuzzy-neural network on PTZ camera.
ABSTRACT Surveillance system is widely applied in various fields in recent years. The demand for monitoring quality is not only to capture the object image, but also to present sharp and recognizable image. Thus, PTZ camera, which has pan and zoom functions, has become the best equipment. How to zoom in object and move camera lens so that the object is placed at the center of image becomes the major issue. This paper proposes the method of using the widest camera viewing angle as the moving range, and employing the spatial adjustment method of camera to establish the relative position of the camera and space. However, once the targeted object is unclear, the zoom in function of the PTZ camera is used; if the targeted object is not within the center of the image, the targeted object will move out of the image frame during the zoom-in process. Hence, by using the fuzzy nerves to simulate nonlinear horizontal movement and vertical rotation of PTZ camera, the image can be moved to the center of the image. In this paper, PTZ camera can zoom in image of the targeted object and move camera lens so that the object is at the frame center. Without additional adjustment, the camera lens can be moved exactly to the target position.
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ABSTRACT: This paper presents a numerically stable non-iterative algorithm for fitting an ellipse to a set of data points. The approach is based on a least squares minimization and it guarantees an ellipse-specific solution even for scattered or noisy data. The optimal solution is computed directly, no iterations are required. This leads to a simple, stable and robust fitting method which can be easily implemented. The proposed algorithm has no computational ambiguity and it is able to fit more than 100,000 points in a second. Keywords: ellipses, fitting, least squares, eigenvectors INTRODUCTION One of basic tasks in pattern recognition and computer vision is a fitting of geometric primitives to a set of points (see [Duda73] for a summary). The use of primitive models allows reduction and simplification of data and, consequently, faster and simpler processing. A very important primitive is an ellipse, which, being a perspective projection of a circle, is exploited in many applications of ...02/1999;
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ABSTRACT: In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.Neurocomputing 06/2009; · 2.01 Impact Factor
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ABSTRACT: Moving object recognition by a shape-based neural fuzzy network is proposed in this paper. The moving objects considered in this paper include pedestrians, vehicles, motorcycle, and dogs. Given the shape of the moving object, its contour is calculated by contour following. The distance between the contour center and each contour point is calculated and smoothed. Parts of the feature vector are obtained from discrete Fourier transform coefficients of the smoothed distances. The length-to-width ratio of the object's shape, which is derived from vertical and horizontal projection of the shape of the object, is also used as a feature. Based on the feature vector, the self-constructing neural fuzzy inference network (SONFIN) is used for recognition. To verify the performance of the proposed approach, two experiments were performed. In the first experiment, the shape of an object was extracted manually. In the second experiment, the shape of an object was extracted automatically from a series of image processes, including gray-based and edge-based image subtractions and morphological operations. The experiments show that the proposed approach can recognize moving objects with high accuracy. SONFIN performance is also shown to be better than back-propagation neural network and radial basis function network performance.Neurocomputing 08/2008; 71(13):2937-2949. · 2.01 Impact Factor