Systems for processing high resolution images need to be fast, compact, and efficient. imageprocessing systems that incorporate optics into its architecture can provide the speed and potentially the compactness to me...
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ISBN:
(纸本)081944815X
Systems for processing high resolution images need to be fast, compact, and efficient. imageprocessing systems that incorporate optics into its architecture can provide the speed and potentially the compactness to meet the demands of analyzing images. In this paper a hybrid approach to image analysis using Winner Take All neural network dynamics with optical and electronic implementation is discussed. Resulting images from the system simulations are explored for use in object and background discrimination for image segmentation tasks.
In this paper, we discuss to develop automatic classification system for true color Leukocyte image. In view of the deficiencies of traditional combination optimization method, a new method based on genetic algorithm ...
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ISBN:
(纸本)081944815X
In this paper, we discuss to develop automatic classification system for true color Leukocyte image. In view of the deficiencies of traditional combination optimization method, a new method based on genetic algorithm is proposed. Combining the specific situation of cell classification, we made some modification. Finally neural network with error back-propagation is training using the selected feature sets. The result shows this method optimize the classification performance.
A neural network based image enhancement method is introduced to improve the image resolution from a sequence of low resolution image frames. Most of the existing methods reconstruct a high-resolution image from a mul...
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ISBN:
(纸本)081944815X
A neural network based image enhancement method is introduced to improve the image resolution from a sequence of low resolution image frames. Most of the existing methods reconstruct a high-resolution image from a multiple of low-resolution image frames by minimizing some established cost function using a mathematical technique. This method, however, uses an integrated recurrent neural network (IRNN) that is particularly designed to be capable of learning an optimal mapping from a multiple of low-resolution image frames to a high-resolution image through training. The IRNN consists of four feed-forward sub-networks working collectively with the ability of having a feedback of information from its output to input. As such, it is capable of both learning and searching the optimal solution in the solution space leading to high resolution images. Simulation results demonstrate that the proposed IRNN has good potential in solving image resolution enhancement problem, as it can adapt itself to the various conditions of the reconstruction problem by learning.
The ability to detect objects from image sequences and estimate their trajectory is useful in many applications like satellite tracking, missile guidance and interception. This paper proposes a reliable and an effecti...
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ISBN:
(纸本)0819449318
The ability to detect objects from image sequences and estimate their trajectory is useful in many applications like satellite tracking, missile guidance and interception. This paper proposes a reliable and an effective application for preventing loss of lives on event of airline crashes similar to the one on 9/11/2001. This contribution uses the MixeD algorithm for object detection, velocity estimation and the trajectory of the moving object in the spatiotemporal domain. The case study of the 9/11 event shows that the proposed method could have helped the authorities alert the people inside the towers far in advance about the hostile situation and could have saved a few more lives.
The proceedings contains 14 papers from the conference on SPIE: applications of artificialneuralnetworks in imageprocessing ViiI. The topics discussed include: selective visual attention in object detection process...
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The proceedings contains 14 papers from the conference on SPIE: applications of artificialneuralnetworks in imageprocessing ViiI. The topics discussed include: selective visual attention in object detection processes;application of genetic algorithms and neuralnetworks in the automatic classification of leukocytes;character recognition by synergetic neural network based on selective attention parameters and dynamic electrical impedance tomography method based on a multilevel BP neural network.
A feedback neural network (FBNN) can be triggered by ANY input analog pattern vector. Then depending on the domain-of-convergence (or domain-of-attraction in the languages of nonlinear systems) that this triggering pa...
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ISBN:
(纸本)081944815X
A feedback neural network (FBNN) can be triggered by ANY input analog pattern vector. Then depending on the domain-of-convergence (or domain-of-attraction in the languages of nonlinear systems) that this triggering pattern falls into, the FBNN will go around and around the feedback loop and finally settle down at one of the few designated patterns associatively stored in the connection matrix. This recalled (or the settle-down) pattern will stay at the output even when the input triggering pattern is removed because of the self-sustained feedback action of the FBNN. The triggering pattern does not have to be the same as the stored pattern that it recalls. It can be a noise-affected pattern. But as long as it falls within the designated noise range (or the designated domain of convergence) of an accurately stored pattern, that accurate pattern will be recalled and permanently appear at the output even when the input triggering is removed.
In this paper a learning algorithm of synergetic neural network based on selective attention parameters is proposed. According to the mechanism of the Human Visual System (HVS), the weight matrix of synergetic neural ...
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ISBN:
(纸本)081944815X
In this paper a learning algorithm of synergetic neural network based on selective attention parameters is proposed. According to the mechanism of the Human Visual System (HVS), the weight matrix of synergetic neural network can be obtained by multiplying the prototype matrix by selective attention parameters. Two selective attention models based on the human visual system are put forward in this paper. The comparative experiments between the traditional algorithm SCAP and the new method we proposed in the application of recognising the real gray images of numeric and alphabetic characters are done. And the results show that our method can improve the synergetic neural network's recognition performance and be more suitable to human visual system.
This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The...
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ISBN:
(纸本)081944815X
This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neuralnetworks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.
A method for recognizing faces in relatively unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As ...
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ISBN:
(纸本)081944815X
A method for recognizing faces in relatively unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As the pattern recognition mechanism, a bank of small neuralnetworks of the multilayer perceptron type is used, where each perceptron has the task of recognizing only a single person's face. The perceptrons are trained with a set of nine face images representing the nine main facial orientations of the person to be identified, and a set face images from various other persons. The center of the neck is determined as the reference point for face position unification. Geometric normalization and reference point determination utilizes 3-D data point measurements obtained with a stereo camera. The system achieves a recognition rate of about 95%.
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution or change by making voltage and current measurements on the objec...
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ISBN:
(纸本)081944815X
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution or change by making voltage and current measurements on the object's periphery. image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method deciding the place of impedance change for EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface, and then the impedance change will be reconstructed with linear approximated method. MBPNN can decide the impedance change location exactly without needing long training time. It alleviates some noise affection and can be expanded, which makes sure about the high precision and space resolution of the reconstructed image that can't be accessed by the back projection method.
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