SE-ADM signal data from the AD converter were transferred to a personal computer (Intel Core i7, 2.8 GHz, Windows 7), and high-resolution SE-ADM images were processed from the LPF signal and scanning signal using Matlab R2007b software with an image processing toolbox (Math Works Inc., Natick, MA, USA). We initially observed a 3,840 × 1,020 pixel image using the AD converter of the SE-ADM system and a sampling frequency of 50 kHz. This initial image was converted to the correct size of 1,280 × 1,020 pixels or that of the corresponding SEM image. Corrected SE-ADM images were filtered using a two-dimensional (2D) Gaussian filter (GF) with a kernel size of 7 × 7 pixels and radius of 1.2σ. Background subtraction was achieved by subtracting SE-ADM images from the filtered images using a broad GF (400 × 400 pixels, 200σ).
Matlab r2007b software
Manufactured by MathWorks
Sourced in United States
MATLAB R2007b is a software package developed by MathWorks for numerical computing and visualization. It provides a high-level programming language and a user-friendly environment for data analysis, algorithm development, and visualization. The core function of MATLAB R2007b is to enable users to perform matrix and array computations, implement signal processing and image processing algorithms, and create custom applications and user interfaces.
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Lab products found in correlation
2 protocols using matlab r2007b software
1
High-Resolution SE-ADM Image Processing
2
Leaf and Petal Image Analysis
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After images from all of the lines of the fourth and seventh leaves and the petals were prepared, the digital images were properly oriented (with the tip always pointing to the right and with good horizontality) using Photoshop CS5 software (Adobe Inc.). After that, we used MATLAB R2007b software (MathWorks Inc.) and the AAM Toolbox (version 6.5) (Bensmihen et al., 2008) to construct the model of each individual leaf and petal separately (Feng et al., 2009) . The outline of each leaf and petal was represented by the Cartesian coordinates of its 25 points, which were placed around the leaf and petal outline using the leaf (Le) and petal (Pe) templates. These points were plotted to show the pattern of allometry in the data set, and PCA was used on the whole data set to identify trends in variation.
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