A central issue in computer vision is the problem of signal-to-symbol transformation. This problem has two components: first, the choice of symbols to be derived and, second, the exact method used to transform the giv...
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A central issue in computer vision is the problem of signal-to-symbol transformation. This problem has two components: first, the choice of symbols to be derived and, second, the exact method used to transform the given image into this set of symbols. The objective of this paper is to develop an approach to the solution of signal-to-symbol transformation in the domain of flow fields, such as oriented texture fields and velocity vector fields. We use the geometric theory of differential equations to derive a symbol set based on the visual appearance of phase portraits. Phase portraits are a geometric representation of the solution curves of a system of differential equations. In the linear 2-D case, only a finitely different number of phase portraits is possible. This theory provides a machinery to describe flow fields both qualitatively and quantitatively. An attractive feature of this symbol set is that it is domain independent. We also provide the computational framework to start with a given flow field and derive its symbolic representation. In the case of texture, this is done by computing an orientation field and then using a nonlinear least squares technique over successive windows to determine the changing spatial behavior of the texture. Specifically, we segment the given texture, derive its symbolic representation, and perform a quantitative reconstruction of the salient features of the original texture based on the symbolic descriptors. We present results of applying this technique to several real texture images. This technique is useful in describing complex flow visualization pictures, defects in lumber processing, defects in semiconductor wafer inspection, and optical flow fields.
Both adaptive and nonadaptive methods have been developed in the past for determining optimal Weighted Order Statistic (WOS) filters. In practical applications there is very often a need to preserve or to remove certa...
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An analog module for processing a fax scanner's signals is presented. The module is a section of a complete solution fax system chip, which utilizes current mode CMOS circuits in a noisy environment to produce an ...
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An analog module for processing a fax scanner's signals is presented. The module is a section of a complete solution fax system chip, which utilizes current mode CMOS circuits in a noisy environment to produce an 8-b resolution, 64 gray-level pictures from a low-cost (low performance) charge coupled device (CCD), or contact-type image sensor (CIS) scanners.< >
Describes the accuracy and limitations of the image velocity computational technique based on delay lines. Three major sources of computational error are identified, namely, the image-area quantization, signal refract...
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Describes the accuracy and limitations of the image velocity computational technique based on delay lines. Three major sources of computational error are identified, namely, the image-area quantization, signal refraction, and delay-line mismatch. computational errors obtained experimentally are discussed.< >
A massively parallel computer, the Princeton Engine (PE), is applied to the task of volumetric ultrasound image reconstruction. Results are compared with those obtained on a conventional, sequential computer (a Sun Sp...
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A massively parallel computer, the Princeton Engine (PE), is applied to the task of volumetric ultrasound image reconstruction. Results are compared with those obtained on a conventional, sequential computer (a Sun SparcStation 1). The PE reduces reconstruction times by a factor of approximately 500 over the Sun, making possible the creation of a flexible and efficient tool for experimenting with image-reconstruction parameters and algorithms. The image reconstruction of volumetric data is reviewed, and the approach used to map the reconstruction algorithm onto the parallel processor, the implementation problems resolved, and the acceleration achieved are discussed.< >
Multiscale signal decomposition is required in computational vision for the analysis of the information content of an image. The authors use the Fejer filtering multispectral signal decomposition technique, which uses...
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Multiscale signal decomposition is required in computational vision for the analysis of the information content of an image. The authors use the Fejer filtering multispectral signal decomposition technique, which uses the Fejer kernel in the classical Fourier analysis. It is built on a diffraction imaging model from the perspective of Fourier optics based on the uncertainty principle and the argument that a real signal should be bandlimited. The implementation is based on its first-order approximation form.< >
Presents two recursive-in-order least squares algorithms for parameter estimation of 2D Gaussian Markov random field (GMRF) models. Algorithm I implements the recursive computation by introducing auxiliary variables w...
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Presents two recursive-in-order least squares algorithms for parameter estimation of 2D Gaussian Markov random field (GMRF) models. Algorithm I implements the recursive computation by introducing auxiliary variables without changing the structure of the model, while algorithm II realizes the recursive computation by replacing the noncausal symmetric GMRF model by an equivalent causal nonsymmetric model. The concept of recursive path, which is used to increase the speed of computation of the model parameters and accomplish the choice of the optimal model support, is proposed. The computational complexity of both algorithms is O(M/sup 2/m) multiplications per order, where m is the total number of parameters and M/sup 2/ is the size of a sample image.< >
This paper presents a subband image coding method with an incorporating HVS model. In this method, an image to be coded is first divided into several frequency bands by a 2D subband analysis system, and then different...
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This paper presents a subband image coding method with an incorporating HVS model. In this method, an image to be coded is first divided into several frequency bands by a 2D subband analysis system, and then different bands are transformed into the spectral domain through the DCT. HVS models are used to weight the transformed coefficients in accordance with the human eye's sensitivity to AC energy within different frequency bands; the characteristics of eye's sensitivity to frequency orientation is also considered. The simulation results show that the coding method has good coding performance. More subjectively, a pleasing decoded image is obtained even at low bit-rates.< >
A methodology for optical flow analysis based on cepstral filtering is introduced. The power cepstrum is extended to multiframe analysis. A correlative cepstral technique, cepsCorr, is developed. It significantly incr...
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A methodology for optical flow analysis based on cepstral filtering is introduced. The power cepstrum is extended to multiframe analysis. A correlative cepstral technique, cepsCorr, is developed. It significantly increases the signal-to-noise ratio, reduces ambiguities, and it provides a predictive or multievidence approach to visual motion analysis.< >
We present a method to smooth a signal-whether it is an intensity image, a range image or a planar curve-while preserving discontinuities. This is achieved by repeatedly convolving the signal with a very small averagi...
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We present a method to smooth a signal-whether it is an intensity image, a range image or a planar curve-while preserving discontinuities. This is achieved by repeatedly convolving the signal with a very small averaging mask weighted by a measure of the signal continuity at each point. The method is extremely attractive since edge detection can be performed after a few iterations, and features extracted from the smoothed signal are correctly localized. Hence no tracking is needed, as in Gaussian scale-space. This last property allows us to derive a new scale-space representation of a signal using the adaptive smoothing parameter k as the scale dimension. We then show how this process relates to anisotropic diffusion. When a large amount of smoothing is desired, we propose a multigrid implementation which reduces the computational time significantly. Given the local nature of the algorithm, we also propose a parallel implementation: the running time on a 16K Connection Machine is three orders of magnitude faster than on a serial machine. We then present several applications of adaptive smoothing: edge detection, range image feature extraction, corner detection, and stereo matching. Examples are given throughout the text using real images.
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