Bayesian texture classification method using a random sampling scheme

2004 ◽  
Author(s):  
V. Ayala-Ramirez ◽  
M. Obara-Kepowicz ◽  
R.E. Sanchez-Yanez ◽  
R. Jaime-Rivas
Keyword(s):  
Random Sampling ◽  
Texture Classification ◽  
Classification Method ◽  
Sampling Scheme

scholarly journals A Random Sampling Scheme for Path Planning

1997 ◽  
Vol 16 (6) ◽  
pp. 759-774 ◽  
Author(s):  
Jérôme Barraquand ◽  
Lydia Kavraki ◽  
Jean-Claude Latombe ◽  
Rajeev Motwani ◽  
Tsai-Yen Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Random Sampling ◽  
Sampling Scheme

scholarly journals Restructured class of estimators for population mean using an auxiliary variable under simple random sampling scheme

2020 ◽  
Vol 16 (1) ◽  
pp. 61-75
Author(s):  
S. Baghel ◽  
S. K. Yadav
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Order Of Approximation ◽  
Study Variable ◽  
Population Mean ◽  
First Order ◽  
New Class ◽  
Class Of Estimators

AbstractThe present paper provides a remedy for improved estimation of population mean of a study variable, using the information related to an auxiliary variable in the situations under Simple Random Sampling Scheme. We suggest a new class of estimators of population mean and the Bias and MSE of the class are derived upto the first order of approximation. The least value of the MSE for the suggested class of estimators is also obtained for the optimum value of the characterizing scaler. The MSE has also been compared with the considered existing competing estimators both theoretically and empirically. The theoretical conditions for the increased efficiency of the proposed class, compared to the competing estimators, is verified using a natural population.

scholarly journals ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 177-183 ◽  
Author(s):  
S. A. Samsudin ◽  
R. C. Hasan
Keyword(s):  
Spatial Resolution ◽  
Clustering Algorithm ◽  
High Spatial Resolution ◽  
Texture Classification ◽  
Classification Method ◽  
Sediment Type ◽  
Range Analysis ◽  
Sediment Classification ◽  
Sediment Types ◽  
Seafloor Sediment

Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7&amp;thinsp;km<sup>2</sup>, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77&amp;thinsp;% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.

scholarly journals Restructured class of estimators for population mean using an auxiliary variable under simple random sampling scheme

2021 ◽  
Vol 17 (2) ◽  
pp. 75-90
Author(s):  
B. Prashanth ◽  
K. Nagendra Naik ◽  
R. Salestina M
Keyword(s):  
Characteristic Polynomial ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Signed Graph ◽  
Population Mean ◽  
Class Of Estimators

Abstract With this article in mind, we have found some results using eigenvalues of graph with sign. It is intriguing to note that these results help us to find the determinant of Normalized Laplacian matrix of signed graph and their coe cients of characteristic polynomial using the number of vertices. Also we found bounds for the lowest value of eigenvalue.

On a Generalization of the Classical Random Sampling Scheme and Unbiased Estimation

2007 ◽  
Vol 36 (4) ◽  
pp. 693-705 ◽  
Author(s):  
Ya. Lumelskii ◽  
V. Voinov ◽  
M. Nikulin ◽  
P. Feigin
Keyword(s):  
Random Sampling ◽  
Sampling Scheme ◽  
Unbiased Estimation

Texture classification in aerial photographs using multiscale and multilayer complex networks

2021 ◽  
Author(s):  
M.N. Favorskaya ◽  
A.N. Zhukovskaya
Keyword(s):  
Complex Networks ◽  
Texture Analysis ◽  
Complex Network ◽  
Multilayer Structure ◽  
Statistical Approach ◽  
Texture Classification ◽  
Classification Method ◽  
Aerial Photographs ◽  
Functional Connections

Texture classification using oriented complex networks considers the functional connections between topological elements and simulates the complex textures more accurately. In contrast to the classical spatial texture analysis, we offer a novel function of weights in complex networks and a classification method that takes into account the scaling and color of textures. For this, three complex networks represented R, G and B components are built, which provide invariance of color aerial photographs obtained at different times. Comparison of the classification results using the proposed multiscale complex networks and conventional texture analysis based on a statistical approach is given. Also we extended this approach on color aerial photographs using multilayer structure of complex network.

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