A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3...
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A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.
We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of different...
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We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.
image decomposition based on the discrete wavelet transform (DWT) has been proposed for efficient storage and progressive transmission of images for visual browsing in digital image libraries. Although the compression...
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image decomposition based on the discrete wavelet transform (DWT) has been proposed for efficient storage and progressive transmission of images for visual browsing in digital image libraries. Although the compression aspects of the DWT have been carefully researched, reconstruction errors due to corrupted wavelet coefficients have received less attention. In this paper we consider the problem of bit errors affecting uniformly quantized wavelet coefficients. The proposed method, which is based on a local image model, simultaneously detects and masks corrupted wavelet coefficients.
The performance of two adaptive algorithms, the LMS and the adaptive delay algorithm, for measuring the actual delay between two blood volume pulse (BVP) signals is studied. Results are obtained from an experimental s...
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The performance of two adaptive algorithms, the LMS and the adaptive delay algorithm, for measuring the actual delay between two blood volume pulse (BVP) signals is studied. Results are obtained from an experimental setup examined with several test signals as well as pairs of BVP signals for a subject's three different cardiovascular conditions.
We formulate the spotlight-mode synthetic aperture radar (SAR) imaging problem as a maximum a posteriori (MAP) estimation problem. Our statistical perspective offers an alternative to standard SAR processing technique...
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We formulate the spotlight-mode synthetic aperture radar (SAR) imaging problem as a maximum a posteriori (MAP) estimation problem. Our statistical perspective offers an alternative to standard SAR processing techniques by incorporating prior knowledge about either the data quality or the underlying reflectivity function. In our formulation we consider exploiting the similarity between computer assisted tomography (CAT) and spotlight-mode SAR, which was first exposed by Munson et al. (1983). We present the effectiveness of the proposed approach by imagery formed from simulated spotlight-mode SAR data.
By expressing the frequency response of a Nyquist filter as a convolution of two functions, the mathematical complexity required to find the impulse response of the filter is reduced. Moreover, more efficient Nyquist ...
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By expressing the frequency response of a Nyquist filter as a convolution of two functions, the mathematical complexity required to find the impulse response of the filter is reduced. Moreover, more efficient Nyquist filters than the popular Raised Cosine filter (i.e. wider 3 dB bandwidth, same absolute bandwidth, and shorter or same length impulse response) are generated by using well known Window functions such as Ramming and Blackman.
In this paper, a contrast pyramid based image coding method is described in which a simple nonlinear difference operation is introduced to generate residual information between two contiguous level images. The represe...
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In this paper, a contrast pyramid based image coding method is described in which a simple nonlinear difference operation is introduced to generate residual information between two contiguous level images. The representation of the residual information can be considered as a local contrast measure. As the difference image in established pyramid coding techniques, the contrast image has low variance, and data compression is achieved by quantizing the contrast image. Based on Weber's law and spatial masking of the human visual system, further data compression can be attained by selecting only perceptually significant information from the contrast image and coding it efficiently.
This paper describes a simulator for the Shiva multiprocessor system and the simulator construction methodology (SCM) used in its creation. The SCM, based on the active functional unit (AFU) construct, is a modern SCM...
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Automatic cloud classification of satellite imagery can be of great help to meteorological studies. A neural network-based cloud classification system is developed and introduced. Several image transformation schemes ...
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Automatic cloud classification of satellite imagery can be of great help to meteorological studies. A neural network-based cloud classification system is developed and introduced. Several image transformation schemes such as wavelet transform (WT) and singular value decomposition (SVD) are used to extract the salient textural feature of the data and is then compared with those of the well-known gray-level co-occurrence matrix (GLCM) approach. Two different neural network paradigms namely the probability neural network (PNN) and the unsupervised Kohonen (1990) self-organized feature map (SOM) are chosen and examined. The performance of the proposed cloud classification system is benchmarked on the Geostationary Operational Environmental Satellite (GOES) 8 data set and promising results have been achieved.
The delta operator approach to continuous-time cellular neural networks (CT-CNNs) is investigated in terms of a robust realization. It is shown that earlier results concerning the robustness of CT-CNNs can be obtained...
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The delta operator approach to continuous-time cellular neural networks (CT-CNNs) is investigated in terms of a robust realization. It is shown that earlier results concerning the robustness of CT-CNNs can be obtained as a limiting case of this approach, while at the same time, this allows us to formulate robustness considerations for discrete-time CNNs.
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