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EN
The Sahara’s Nememcha mountains chain suffers from a significant lack of large-scale geological information. In the Bir Later region with complex morpho-structural settings and arid climate conditions; geological maps have not been yet completed by competent authorities. However, this region harbours Algeria’s largest phosphate mine; with its reserves estimated at more than one billion tons of ore grading 20% phosphorus pentoxide. Geomatic-based techniques of Multisource Remote Sensing data allow the classification and identification of the lithologic features. The adopted method quarries the spectral signal, the alteration processes, and the thickness of the rocky banks. For this task, we apply Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), directional filters, and unsupervised classification (K-Means data) techniques to calibrate and correct Landsat 8 OLI and Sentinel-2A multispectral images. A petrographic study with field and laboratory work was carried out in order to confirm the machine description of the different facies. The results showed that the proposed lithology classification scheme can achieve accurate classification of all lithologic types, in the Cenozoic, Mesozoic, and Holocene deposits of the study area. The lithological map obtained from the GIS-RS-Processing is highly correlated with our field survey. Therefore, multispectral image data (Landsat 8 OLI and Sentinel-2A) coupled with an advanced image enhancement technique and field surveys are recommended as a rapid and cost-effective tool for lithologic discrimination and mapping. The experimental results fully demonstrated the advantages of the reliance on laboratory tests in the sensed lithology validation in an arid area.
EN
In this paper, a non-subsampled wavelet-based contourlet transform (NWCT) is applied in warp-knitted fabric defect segmentation. Compared with the traditional contourlet transform, wavelet transform takes the place of Laplacian pyramid in NWCT and the directional filter bank is non-subsampled. The wavelet transform with improved wavelet threshold is put to use, and the original fabric image can be decomposed into low-frequency approximate coefficient A and high-frequency detail coefficients V, H, and D. The high-frequency detail coefficients are processed by the non-subsampled directional filter bank to get directional sub-band coefficients. Afterward, the effective sub-band coefficients based on regional energy are chosen to reconstruct V, H, and D. And the reconstructed fabric image will be achieved by inverse non-subsampled wavelet-based contourlet transform. The adaptive threshold method and morphological processing are used to obtain the legible defect profile. The experiment demonstrates that NWCT can achieve the positive segmentation regarding the common defects, such as broken warp, width barrier, and oil, and has excellent performance on these directional defects and regional defects. It is acknowledged that NWCT will provide a new way to detect warp-knitted fabric defects automatically.
EN
In the paper some possibilities of using the HSV colour space for the multichannel nage filtering are analysed. The most typical problem with the hue component is its circular character, so for the adaptive filtration the appropriate choice of the "starting" point, known from the conditional ordering, is proposed. Such approach can be useful also or some other colour median filtering methods such as Vector Median Filter, Basic Vector Directional Filter or their combination known as the Distance-Directional Filter. The results of the filtration performed using the most popular methods with the usage of such modified HSV colour space have been compared to the effects of using standard HSV and RGB colour spaces.
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