Image processing and analysis based on the use of phase information

Pavelyeva, E. A.; Cybernetics, Moscow

doi:10.18287/2412-6179-2018-42-6-1022-1034

Cited by 17 publications

(9 citation statements)

References 63 publications

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“…To get a relationship between LST and VIs, we used Pearson correlation coefficient during two decades study period (figure 5) and find a clear negative correlation. The correlation coefficient of determination of each linear regression (r 2 ) exceeded more than 0.90 in all years [48,49]. Extreme temperature shows low vegetation and reducing temperature represent increasing high and healthy vegetation condition.…”

Section: Lst and Vismentioning

confidence: 93%

Vegetation Drought Dynamic Analysis in European Russia

Boori

Paringer

Choudhary

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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This research work deals with the spatial-temporal characteristics of the relationship between drought events (Standardized Precipitation Index [SPI]), land surface temperature (LSI) and vegetation indexes (VIs) in the spring-summer (May-August) over the European Russia (ER) from 2000 to 2018. We use Terra-MODIS -NDVI and LST product and TRMM for rainfall data. Statistical results indicate that year 2004, 2009 and 2015 were the most significant changing-point in mean annual rainfall values and VIs. Results indicate that vegetation area and VIs variate according to SPI values. Analysis results also indicate that low NDVI values (0.2-0.4) shift in high NDVI values (0.5-0.8) with high SPI values and viceversa, also high LST values associated with low VIs values and vice-versa, with correlation coefficients 0.90, means high-temperature show low vegetation. Correlation analysis of VIs, SPI and LST deficit shows that vegetation is closely related to rainfall and temperature, especially under the dry and wet conditions and indicates that this correlation can use for nearreal-time monitoring of vegetation drought dynamics. All predictions and monitoring using satellite-derived VIs is a low cost and effective means of identifying longer-term changes as opposed to natural inter-annual variability in vegetation growth.

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Section: Lst and Vismentioning

confidence: 93%

Vegetation Drought Dynamic Analysis in European Russia

Boori

Paringer

Choudhary

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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“…After creating above thematic layers, we combined all layers in ArcGIS software by using the raster calculator module to generate the final groundwater potential zone map of Orenburg, Russia. Than all factors in each thematic layer were given a specific weight (arithmetic value in between 1 to 9) based on its sensitivity or strength of influence to the groundwater possibility [8,9,10]. In last further, classify final groundwater potentiality map into five classes from very high to very low classes.…”

Section: Methodsmentioning

confidence: 99%

Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

Choudhary

Boori

Kupriyanov

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

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“…The second step, feature vector extraction, represents the main difference between existing approaches. Since vein recognition is relatively young study, some feature extraction methods could be derived from other biometric recognition algorithms based on statistical information [2], image key points [3,4,5], subspace-based methods [6], phase based methods [7,8], etc. Some approaches were developed specifically for vein recognition [9].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Palm Vein Image Segmentation

Safronova

Pavelyeva

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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In this article the new hybrid algorithm for palm vein image segmentation using convolutional neural network and principal curvatures is proposed. After palm vein image preprocessing vein structure is detected using unsupervised learning approach based on W-Net architecture, that ties together into a single autoencoder two fully convolutional neural network architectures, each simi-lar to the U-Net. Then segmentation results are improved using principal cur-vatures technique. Some vein points with highest maximum principal curva-ture values are selected, and the other vein points are found by moving from starting points along the direction of minimum principal curvature. To obtain the final vein image segmentation the result of intersection of the principal curvatures-based and neural network-based segmentations is taken. The evaluation of the proposed unsupervised image segmentation method based on palm vein recognition results using multilobe differential filters is given. Test results using CASIA multi-spectral palmprint image database show the effectiveness of the proposed segmentation approach.

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Image processing and analysis based on the use of phase information

Cited by 17 publications

References 63 publications

Vegetation Drought Dynamic Analysis in European Russia

Vegetation Drought Dynamic Analysis in European Russia

Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

Unsupervised Palm Vein Image Segmentation

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