In the paper we present a Hopfield neural network approach to blind bilevel image restoration. In the approach two kinds of Hopfield neural networks are used. One is the analog Hopfield neural network utilized to esti...
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The dual irregular pyramid combines the advantage of adaptivity with a limited computational complexity of neighborhood operations. The levels of the pyramid, dual graphs, are defined only if (hey are planar. We prove...
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The method described here for reconstructing the shape of Lambertain surfaces from the shading information inherent in a monocular image can deal with the surface whose image,a smooth and non-self occluding one,is pro...
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ISBN:
(纸本)0780312333
The method described here for reconstructing the shape of Lambertain surfaces from the shading information inherent in a monocular image can deal with the surface whose image,a smooth and non-self occluding one,is produced by orthographic projection and parallel light source from a known *** dividing the image area into serial triangles,the intensity function I(x,y) on each triangle can be *** the help of a support function and a transformation,it is proved that each triangle represents a cylinder in a new(X-Y) coordinates with (s,t) as its components of the surface ***,the ill-posed image brightness constraint equation in cartesian coordinates can be solved in(X-Y) coordinates with only one variable(s or t).It is necessary to find out,firstly,the pixel which has the maximal intensity,the assigning of its depth leads to the direct recovery of the shape at each point without the necessity of integration.
A Hopfield neural network approach to blind bilevel image restoration is presented. In the approach, two kinds of Hopfield neural networks are used. One is the analog Hopfield neural network, utilized to estimate the ...
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A Hopfield neural network approach to blind bilevel image restoration is presented. In the approach, two kinds of Hopfield neural networks are used. One is the analog Hopfield neural network, utilized to estimate the parameters of the finite point spread function (PSF) of a blurring system. The other one is the modified Hopfield neural network used to restore bilevel image. The entire model is based on the alternative operation of the two networks. In the modified Hopfield neural network, the eliminating highest error (EHE) criterion is applied for the purpose of obtaining a more precise solution. Simulation results show that, after a few iterations, the model always obtains a bilevel image whose quality is almost the same as, or even better than, what is obtained by the modified Hopfield network when the precise parameters of PSF are used. The results are quite good. If the EHF criterion is not used, the model does not achieve a good bi-level image.< >
The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, ...
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The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, ...
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The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.< >
A full domain optimum neural network (FDONN) and its application to imagerecognition are proposed in this paper. In general, we cann't ensure the devised neural network to converge to a global minimum. In this pa...
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Approach for bilevel image restoration and reconstruction using a modified Hopfield neural network is proposed in this paper. A group of threshold update (TU) algorithms with respective to simultaneous, partially simu...
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In Hopfield neural network approach for bilevel image restoration the autoconnections of the network generally weight heavier than interconnections. This characteristic exists in general degradation models of image re...
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＜正＞A new neural network for imagerecognition is proposed in this *** many cases of classification the number of class "dimensions is much larger than the number of classes and classes are independent linearly...
＜正＞A new neural network for imagerecognition is proposed in this *** many cases of classification the number of class "dimensions is much larger than the number of classes and classes are independent linearly of each other.A mapping from the class space into a new orthogonality space is used *** the basis of mapping a Hopfield neural network as classifer is proposed *** results of computer simulation show the high performance of the method.
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