scholarly journals Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis

Geomatics ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Daniel R. Newman ◽  
Jaclyn M. H. Cockburn ◽  
Lucian Drǎguţ ◽  
John B. Lindsay
Keyword(s):  
Processing Time ◽  
Scale Space ◽  
Optimal Scaling ◽  
Multiscale Methods ◽  
Quadratic Regression ◽  
Scaling Method ◽  
Non Linear ◽  
Gaussian Scale Space

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses.

scholarly journals FEATURES OF DERIVATIVE CONTINUITY IN SHAPE

1999 ◽  
Vol 13 (08) ◽  
pp. 1181-1199
Author(s):  
PHILIP F. HENSHAW
Keyword(s):  
Time Series ◽  
Image Recognition ◽  
Scale Space ◽  
Complex Processes ◽  
Behavioral Dynamics ◽  
Higher Derivative ◽  
Smoothing Kernel ◽  
Key Features ◽  
Invariant Relationship ◽  
Gaussian Scale Space

Derivative continuity is a distributed invariant relationship between parts of flowing shapes. The original techniques presented here were developed for making the behavioral dynamics of complex processes more recognizable, but are equally applicable to assisting in the recognition of shapes in images. Regularizing a sequence using a constraint of derivative continuity is equivalent to using a bimodal smoothing kernel, producing a distinct bias for reducing variation on higher derivative levels, sharply defining shape with minimal suppression of shape. To help determine where reconstructing shapes in this way is valid, a test was developed to help distinguish combinations of noise and smooth flows from random walks. This helps distinguish between illusory and genuine, data shapes but also exposes a flair in using this and other measures of scaling behavior for diagnostic purposes. Gaussian scale space techniques in use for some time in image recognition, for identifying reliable landmarks in the shapes of outlines, are demonstrated for use in identifying key features of shape in time series.

scholarly journals Qualitative Descriptions of Image in the Gaussian Scale-Space

2005 ◽  
Vol 11 (2) ◽  
pp. 157-166
Author(s):  
Tomoya SAKAI ◽  
Atsushi IMIYA
Keyword(s):  
Scale Space ◽  
Gaussian Scale Space

Performance Evaluation of African Breadfruit (Treculia Africana) Seed Dehuller

2018 ◽  
Vol 22 (4) ◽  
pp. 51-60
Author(s):  
Okey Francis Obi ◽  
Clement O. Akubuo
Keyword(s):  
Performance Evaluation ◽  
Regression Model ◽  
Recent Effort ◽  
Quadratic Regression ◽  
Linear Quadratic ◽  
Quadratic Regression Model ◽  
African Breadfruit ◽  
Treculia Africana ◽  
Non Linear

AbstractThis paper reports the effect of the parboiling time on dehulled kernel out-turns (DKO) of African breadfruit seeds, and the most recent effort to upgrade an existing dehuller and its performance. Two common and readily available varieties – Treculia var. africana and var. inverse were used in the study. The seeds were parboiled for 0 (control), 2, 5, 8, 11 and 14 minutes and then dehulled. The result revealed that the parboiling time had a significant effect on the DKO of the two varieties of the seed. The DKO increased from 0 to 5 min of the treatment, after which it decreased considerably up to 14 min of the parboiling time. The obtained data were used to develop a non-linear quadratic regression model to predict the DKO as a function of the parboiling time. The performance evaluation of the breadfruit seeds dehuller revealed that it was significantly influenced by the variety.

Sign in / Sign up

Export Citation Format

Share Document