FEATURE_IMAGES_CVIP
feature_images_cvip() - extracts features from a group of images.
Contents
SYNTAX
[ out_table ] = feature_images_cvip( folder , out_file_name, bin_feat, his_feat, rst_feat, tex_feat, spec_feat,varargin)
Input parameters include :
- folder - Address relative to the current directory,or absolute address to a folder containing the images and their mask folder. The folder should contain a number of images and another subfolder which is assumed to contain the corresponding mask images. The names of the images and their masks should match. Also the number of images should be the same in both the folders.
- out_file_name - Name of the output file. It can be empty. The output is a .CSV file.
- bin_feat - A row vector of 8 elemets at most,that selects the desired binary features to be extracted. For more details look at binary_feature_cvip.
- his_feat - A row vector of 5 elemets at most,that selects the desired histogram features to be extracted. For more details look at hist_feature_cvip.
- rst_feat - A row vector of 7 elemets at most,that selects the desired rst invariant features to be extracted. For more details look at rst_invariant_cvip.
- tex_feat - A row vector of 20 elemets at most,that selects the desired texture features to be extracted. For more details look at texture_features_cvip.
- spec_feat - A row vector of 2 elemets at most, that determines the number of rings and sectors to be used.
- [optional] - The default values for texDist, quantLvl, statsType,normWidth,normHeight can be overwritten by specifying the new values as optional input arguments. For each of these,first,the name should be given as a string,and then the value,i.e. (..., 'par_name', value) For example:feature_objects_cvip( ... , 'quantLvl', 2)
DESCRIPTION
The function extracts the features from a group of images present in the input folder file and outputs a .CSV file that contains all the extracted features. These features depend on the feature selection given as input by the user. The user can select different features from five different feature areas. The function is most useful in Image analysis applications.
REFERENCE
1.Scott E Umbaugh. DIGITAL IMAGE PROCESSING AND ANALYSIS: Applications with MATLAB and CVIPtools, 3rd Edition.
EXAMPLE
% Read input image folder folder = 'C:\Users\lgorant\Desktop\Abnormal'; % Output file name ,Output is a CSV file out_file_name= 'the_output_of_feat_imgs'; % Binary features row vector 8 elements at most bin_feat = [1 0 1 1 0 0 1]; % Histogram features row vector 5 elements at most his_feat = [0 1 1]; % RST feature row vector 7 elements at most rst_feat = [1 0 1 1]; % Texture features row vector 20 elements at most tex_feat = [1 0 0 0]; % Spectral features row vector 2 elements at most spec_feat = [0 0]; % Optional texDist,quantLv1,statsType texDist = 4; quantLvl = 2; statsType = [0 0 1]; % Output features [ out_table ] = feature_images_cvip( folder, out_file_name , bin_feat,his_feat, rst_feat, tex_feat, spec_feat,'statstype', [1 0 1]); disp(out_table);
Columns 1 through 5
'obj_id' 'ASM_Avg' 'ASM_Var' 'rst1' 'rst3'
[ 1] [ 0.2881] [5.2638e-04] [0.2106] [ 0.0017]
[ 2] [ 0.3113] [3.3035e-06] [0.2155] [ 0.0042]
[ 3] [ 0.2702] [4.3471e-05] [0.1701] [4.0394e-04]
[ 4] [ 0.2825] [2.1622e-04] [0.1960] [7.8158e-04]
[ 5] [ 0.2637] [1.4136e-05] [0.1735] [7.0466e-04]
[ 6] [ 0.2537] [3.2133e-06] [0.2077] [ 0.0012]
[ 7] [ 0.2641] [6.3356e-05] [0.1850] [6.9265e-04]
[ 8] [ 0.2762] [6.9977e-05] [0.1867] [5.8746e-04]
[ 9] [ 0.2660] [1.0736e-04] [0.1945] [4.7313e-04]
[ 10] [ 0.2615] [8.2639e-05] [0.1918] [2.8142e-04]
[ 11] [ 0.3259] [9.2798e-05] [0.1948] [2.1630e-04]
[ 12] [ 0.2642] [1.6752e-07] [0.1869] [ 0.0016]
[ 13] [ 0.2712] [1.1934e-04] [0.1771] [3.7520e-04]
[ 14] [ 0.2781] [1.8811e-05] [0.1845] [7.0526e-04]
Columns 6 through 10
'rst4' 'Area' 'Centroid_r' 'Centroid_c' 'Euler'
[2.4795e-04] [28926] [ 105] [ 120] [ 1]
[4.8774e-04] [28429] [ 100] [ 90] [ 1]
[3.2132e-06] [58849] [ 111] [ 185] [ 1]
[2.6899e-05] [33660] [ 108] [ 228] [ 1]
[4.9977e-07] [46365] [ 110] [ 130] [ 1]
[1.4829e-04] [33462] [ 110] [ 223] [ 1]
[1.9046e-06] [43027] [ 109] [ 193] [ 1]
[2.3185e-05] [38588] [ 111] [ 213] [ 1]
[7.6221e-05] [34812] [ 112] [ 143] [ 1]
[1.2701e-05] [35826] [ 114] [ 209] [ 1]
[3.5763e-05] [40567] [ 117] [ 185] [ 1]
[2.6307e-05] [39248] [ 105] [ 203] [ 1]
[5.0085e-05] [45266] [ 113] [ 128] [ 1]
[2.1621e-05] [37221] [ 109] [ 118] [ 1]
Columns 11 through 15
'Proj_H_1' 'Proj_H_2' 'Proj_H_3' 'Proj_H_4' 'Proj_H_5'
[ 5] [ 5] [ 5] [ 5] [ 4]
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[ 0] [ 9] [ 9] [ 9] [ 9]
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[ 0] [ 6] [ 7] [ 7] [ 6]
[ 8] [ 8] [ 8] [ 6] [ 6]
[ 0] [ 9] [ 9] [ 8] [ 7]
[ 7] [ 7] [ 7] [ 7] [ 6]
Columns 16 through 20
'Proj_H_6' 'Proj_H_7' 'Proj_H_8' 'Proj_H_9' 'Proj_H_10'
[ 5] [ 4] [ 4] [ 3] [ 0]
[ 3] [ 3] [ 3] [ 3] [ 0]
[ 9] [ 8] [ 7] [ 0] [ 0]
[ 6] [ 5] [ 5] [ 4] [ 0]
[ 7] [ 7] [ 6] [ 7] [ 0]
[ 4] [ 5] [ 5] [ 5] [ 0]
[ 6] [ 6] [ 6] [ 6] [ 0]
[ 5] [ 6] [ 6] [ 5] [ 0]
[ 6] [ 6] [ 5] [ 5] [ 0]
[ 5] [ 6] [ 6] [ 5] [ 0]
[ 6] [ 6] [ 6] [ 0] [ 0]
[ 6] [ 5] [ 5] [ 5] [ 0]
[ 7] [ 8] [ 8] [ 6] [ 0]
[ 6] [ 6] [ 5] [ 4] [ 0]
Columns 21 through 25
'Proj_W_1' 'Proj_W_2' 'Proj_W_3' 'Proj_W_4' 'Proj_W_5'
[ 0] [ 0] [ 4] [ 6] [ 8]
[ 0] [ 5] [ 6] [ 8] [ 9]
[ 5] [ 6] [ 7] [ 7] [ 7]
[ 0] [ 4] [ 6] [ 8] [ 9]
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[ 0] [ 2] [ 5] [ 9] [ 9]
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[ 0] [ 3] [ 9] [ 9] [ 9]
[ 1] [ 2] [ 6] [ 7] [ 7]
[ 0] [ 4] [ 9] [ 9] [ 9]
[ 7] [ 8] [ 8] [ 8] [ 8]
[ 0] [ 4] [ 7] [ 8] [ 9]
Columns 26 through 30
'Proj_W_6' 'Proj_W_7' 'Proj_W_8' 'Proj_W_9' 'Proj_W_10'
[ 9] [ 9] [ 4] [ 0] [ 0]
[ 8] [ 3] [ 0] [ 0] [ 0]
[ 7] [ 7] [ 7] [ 7] [ 0]
[ 9] [ 9] [ 9] [ 0] [ 0]
[ 8] [ 8] [ 8] [ 5] [ 0]
[ 9] [ 7] [ 5] [ 0] [ 0]
[ 8] [ 8] [ 8] [ 3] [ 0]
[ 9] [ 8] [ 5] [ 0] [ 0]
[ 9] [ 9] [ 7] [ 1] [ 0]
[ 9] [ 8] [ 5] [ 0] [ 0]
[ 7] [ 6] [ 5] [ 3] [ 0]
[ 9] [ 8] [ 6] [ 3] [ 0]
[ 8] [ 7] [ 5] [ 3] [ 0]
[ 9] [ 9] [ 9] [ 0] [ 0]
Columns 31 through 32
'STD_1' 'Skew_1'
[15.6761] [-2.2913]
[17.9970] [-1.9336]
[11.9590] [ 0.7631]
[14.2356] [-2.3815]
[20.8042] [-0.7656]
[16.0049] [-0.8762]
[17.4432] [-0.9965]
[17.2746] [-2.7127]
[20.6128] [-0.9511]
[15.1631] [-3.3073]
[15.4346] [-1.9310]
[15.7601] [-1.2107]
[13.6662] [ 0.0093]
[15.7707] [-1.7351]
CREDITS
Author: Mehrdad Alvandipour, March 2017
Copyright © 2017-2018 Scott
E Umbaugh
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