Nowadays multichannel (multi and hyperspectral) remote sensing (RS) is widely used in different areas. One of the basic factors that can deteriorate original image quality and prevent retrieval of useful information f...
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ISBN:
(纸本)0819463949
Nowadays multichannel (multi and hyperspectral) remote sensing (RS) is widely used in different areas. One of the basic factors that can deteriorate original image quality and prevent retrieval of useful information from RS data is noise. Thus, image filtering is a typical stage of multichannel image pre-processing. Among known filters, the most efficient ones commonly require a priori information concerning noise type and its statistical characteristics. This explains a great need in automatic (blind) methods for determination of noise type and its characteristics. Several such methods already exist, but majority of them do not perform appropriately well if analyzed images contain a large percentage of texture regions, details and edges. Besides, many blind methods are multistage where some preliminary and appropriately accurate estimate of noise variance is required for next stages. To get around aforementioned shortcomings, below we propose a new method based on using inter-quantile distance and its minimization for obtaining appropriately accurate estimates of noise variance. It is shown that mathematically this task can be formulated as finding a mode of contaminated asymmetric distribution. And this task can be met for other applications. The efficiency of the proposed method is studied for a wide set of model distribution parameters. Numerical simulation results that confirm applicability of the proposed approach are presented. They also allow evaluating the designed method accuracy. Recommendations on method parameter selection are given.
In this paper, we present a system developed to identify metal nanoparticles at different orientations, using digital imageprocessing and analysis. The correct identification is important in nanotechnology, where it ...
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In this paper, we present a system developed to identify metal nanoparticles at different orientations, using digital imageprocessing and analysis. The correct identification is important in nanotechnology, where it is possible to build structures for different purposes at the nanometric level. The recognition system computes automatically different characteristics such as: nanoparticle area, polygons, symmetry and molecular arrays (twins) in order to recognize different nanostructures. All these characteristics are obtained through the use of morphological, texture (co-occurrence matrix) and region analysis. Complexity issues, advantages, and results are presented and discussed. (C) 2003 Elsevier Science Ltd. All rights reserved.
Current models of primary visual cortex (V I) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behavior of neurons. The nonlinear stage has been modeled as a di...
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ISBN:
(纸本)0819448079
Current models of primary visual cortex (V I) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behavior of neurons. The nonlinear stage has been modeled as a divisive normalization in which each input linear response is half-rectified, squared and then divided by a weighted sum of half-rectified and squared linear responses in a certain neighborhood. Recently, Simoncelli and colleagues have suggested that this normalization reduces the statistical dependence of neuron responses. In this communication, we present an efficient implementation of these ideas as a practical image representation, and suggest some applications. The linear stage is implemented as a four-level orthogonal wavelet decomposition based on Daubechies filters, and the nonlinear normalization stage uses an improved version of Simoncelli's scheme. The normalization parameters are adapted to minimize statistical dependence between the output responses, so that the resulting representation consists of a set of statistically independent features or visual events. Since both linear and non-linear transforms applied can be inverted, this representation can be highly useful in different applications.
Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements,...
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Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food industry, then identifies areas for further research and wider application the technique. (C) 2002 Elsevier Science B.V. All rights reserved.
Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements,...
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Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food industry, then identifies areas for further research and wider application the technique. (C) 2002 Elsevier Science B.V. All rights reserved.
Water uptake and its distribution during germination of bean seeds were studied by dynamic neutron radiography. The neutron radiography (NR) images were analyzed by an imageprocessing system and statistical data eval...
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Water uptake and its distribution during germination of bean seeds were studied by dynamic neutron radiography. The neutron radiography (NR) images were analyzed by an imageprocessing system and statistical data evaluations were applied upon the images. The time scale of water uptake and germination was assessed for a period of 2880 minutes. In the water distribution of germinating seeds three characteristic zones, i. e. absorption, evaporation and accumulation, were identified. NR enables the appearance and growth of primary root along with the opening of the cotyledons to be Visualized.
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