Conventional subband coding for image data compression uses two-dimensional (2-D) separable QMF banks in which the analysis and synthesis filters are composed of 1-D filters. Such an implementation produces a large si...
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Recently, a very simple nonlinear algorithm, the so-called Teager's algorithm, has been introduced to calculate the energy of a one-dimensional sequence [1]. In this paper, this algorithm is extended to two-dimens...
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Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis ha...
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Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis has shown that this type of nonlinear filter works like a local-mean-weighted highpass filter. Based on this analysis, a second type of nonlinear filter has been developed which works like local-mean-weighted bandpass filter. The proposed image contrast enhancement technique is based on combining the original image with its filtered version obtained using one of the two nonlinear filters. Very high quality enhancement has been achieved for natural images.< >
An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algo...
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An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algorithm addresses the problems specific to neural element detection and localization, viz., images with minimal resolution, operators with small spatial supports, highly curved, filamentous features, large variation in feature intensity profile, poor signal-to-noise ratio, and determination of depth without stereo. One first finds the magnitude and orientation of the maximum intensity second directional derivative. A family of curves is locally fitted to these data, and the projections of the data on the curve family are found. If a pixel lies on a curve with sufficient total projection, it is labeled with the magnitude, orientation, curvature, spatial extent, and element displacement. Depth is interpolated from the spatial extent data for corresponding neighborhoods in three adjacent (in depth) images by using an approximation to the depth-dependent optical point spread function. Experimental results using photomicrographs of cat visual cortex are presented.< >
The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting nod...
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The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >
The controlled pointing of an Earth-orbiting astronomical telescope in the presence of time-dependent gravity-gradient torques is studied using recent techniques for the analysis and synthesis of time-varying linear s...
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The controlled pointing of an Earth-orbiting astronomical telescope in the presence of time-dependent gravity-gradient torques is studied using recent techniques for the analysis and synthesis of time-varying linear scalar dynamical systems. We present results which show that effective adaptive control of unstable motions due to gravity gradient torques can be accomplished, resulting in high pointing accuracy and stability for astronomical telescopes in both circular and elliptical orbits.
An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combi...
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An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combines a fully parallel vector quantizer with a pipelined codebook generator for a broad area of data compression applications. The NNVQ module is capable of producing good-quality reconstructed data at high compression ratios more than 20. The vector quantizer chip has been designed, fabricated, and tested. It contains 64 inner-product neural units and a high-speed extendable winner-take-all block. This mixed-signal chip occupies a compact silicon area of 4.6*6.8 mm/sup 2/ in a 2.0- mu m scalable CMOS technology. The throughput rate of the 2- mu m NNVQ module is 2 million vectors per second and its equivalent computation power is 3.33 billion connections per second.< >
The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capab...
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The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capable of producing a good-quality codebook for AVQ at high compression ratios of more than 20 in real time. This VLSI neural-network-based vector quantization design includes a fully parallel vector quantizer and a pipelined codebook generator to provide an effective data compression scheme. It provides a computing capability as high as 3.33 billion connections per second. Its performance can achieve a speedup of 750 compared with SUN-3/60 and a compression ratio of 33 at a signal-to-noise ratio of 23.81 dB.< >
This paper investigates some approaches for designing one-dimensional linear phase finite-duration impulse-responses (FIR) notch filters, which are based on the modification of several established design techniques of...
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This paper investigates some approaches for designing one-dimensional linear phase finite-duration impulse-responses (FIR) notch filters, which are based on the modification of several established design techniques of linear phase FIR band-selective filters. Based on extensive design examples and theoretical analysis, formulae have been developed for estimating the length of a linear phase FIR notch filter meeting the given specifications. In addition, the design of two-dimensional linear phase FIR notch filters is briefly considered. Illustrative examples are included.
An algorithm that removes impulse noise in images using a modified dynamic programming method is outlined. The restoration process combines two operations. In the first operation, the pixels corrupted by impulse noise...
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An algorithm that removes impulse noise in images using a modified dynamic programming method is outlined. The restoration process combines two operations. In the first operation, the pixels corrupted by impulse noise are detected by a dynamic programming based searching strategy. In the second operation, the original gray levels of the noisy pixels are estimated by a linear interpolation or extrapolation approach. A separable process is used to detect the noisy pixels of the images. In order to preserve the details of the images and reduce the computational complexity of conventional dynamic programming, two modifications have been developed. One is called neighborhood searching with an adaptive figure of merit and the other is called overlapping block processing. The actual performance of the proposed technique is compared with that of the commonly used median filter by filtering noise-corrupted images. Some computational considerations are also discussed.< >
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