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![Results Review of Detecting ofHuman Errors Algorithm for image files Author: Minh Anh Nguyen Email: minhanhnguyen@q.com Purpose The purpose of this analysis is to determine if the method of comparing two image files can be used for detecting of human errors for channel-not-loaded-correctly issue. There are many algorithms which can be used to compare two image files. These algorithms are: edge detection, Short Time Fourier Transform (STFT), wavelet, and histogram. In this project, histogram algorithm is used to compare two image files. Why use Histogram Histogram is a way of visualizing the largest intensities of an image. It is used to depict problems which originate during image acquisition such as exposure, contrast, dynamic range. The contrast of a grayscale image shows how easily objects in the image can be distinguished. The dynamic range is the number of distinct pixel value in an image. It is used to show image statistics in an easily interpreted visual format. It is also a graph showing the number of pixels in an image at each different intensity value found in that image. The histogram of a digital image is a distribution of its discrete intensity levels in the range [0, L-1]. The number of possible intensity values depends on the numerical type encoding the image. For example, a grayscale image encoded with n=8 bits, it will has an array of size L=28=256. The number of possible intensity values go from 0 representing black to L-1=255 representing white. Since there are 256 different possible intensities, the histogram will graphically display 256 numbers showing the distribution of pixels amongst those greyscale values. When represented as a plot, the x-axis is the intensity value from 0 to 255, and the y-axis is the number of pixels with that intensity value. The y-axis varies depending on the number of the pixels in the image and how their intensities are distributed as shown in figure 2. An image has only one histogram, but many images may have the same histogram. Histograms have many uses. One of the more common is to determine if the overall intensity in the image is high enough for this inspection task. Histogram is used to determine whether an image contains distinct regions of certain grayscale values. If the image has poor dynamic range, then the histogram confirms what one can see by visual inspection. It provides a general description of the appearance of an image and helps identify various components such as the background, objects, and noise. How Histogram Works The operation is very simple. The image is scanned in a single pass and a running count of the number of pixels found at each intensity value is kept. This is then used to construct a suitable histogram. The histogram of an 8-bit image, for example can be thought of as a table with 256 entries, or ‘bins’, indexed from 0 to 255. In bin 0 we record the number of times a gray level of 0 occurs; in bin 1 we record the number of times a grey level of 1 occurs, and so on, up to bin 255.](https://image.slidesharecdn.com/resultsreviewofdetectingofhumanerrorsalgorithmimage-160620001659/75/The-method-of-comparing-two-image-files-1-2048.jpg)
![Matlab Code for comparing two image files. %% Matlab Code: % Author: Minh Anh Nguyen (minhanhnguyen@q.com) %This function will perform the followign tasks: %1. read in two ".png" file, display both images on the computer screen. %2. histogram of the images % 3. compare both image files. % 4. display a message on the computer screen if both image are not the % same. function [imagefunction] = imagefunction( file1, file2 ); % clear all; % Erase all existing variables. close all; % Close all figures (except those of imtool.) clc;% Clear the command window. imtool close all; % Close all imtool figures. workspace; % Make sure the workspace panel is showing. fontSize = 20; %%%%%%%%%%%%% % %% team: edit your file here % %%% set the path % folder = 'I:F2015_486Aimage'; % %reading images as array to variable 'a' & 'b'. image1 = imread( file1 ); image2= imread( file2 ); % % %reading images as array to variable 'a' & 'b'. % image1 = imread('Loaded-Mid Channel.PNG'); % image2= imread('Gross Misloaded-Mid Channel.PNG'); % % %% info imfinfo( file1 ) imfinfo( file2) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % do not edit anything below here!!!! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% a = image1; b = image2; %% size a1= size(a); b1= size (b); figure; [rows columns numberOfColorBands] = size(a); subplot(2, 2, 1); imshow(a, []); title('Reference image', 'Fontsize', fontSize); set(gcf, 'Position', get(0,'Screensize')); % Maximize figure. redPlane = a(:, :, 1);](https://image.slidesharecdn.com/resultsreviewofdetectingofhumanerrorsalgorithmimage-160620001659/75/The-method-of-comparing-two-image-files-10-2048.jpg)
![greenPlane = a(:, :, 2); bluePlane = a(:, :, 3); [rows columns numberOfColorBands] = size(b); subplot(2, 2, 3); imshow(b, []); title('Image2', 'Fontsize', fontSize); set(gcf, 'Position', get(0,'Screensize')); % Maximize figure. redPlane1 = b(:, :, 1); greenPlane1 = b(:, :, 2); bluePlane1 = b(:, :, 3); % Let's get its histograms. [pixelCountR grayLevelsR] = imhist(redPlane); subplot(2, 2, 2); %plot(pixelCountR, 'r'); stem(grayLevelsR,pixelCountR, 'r'); title('Histogram of Reference image', 'Fontsize', fontSize); xlabel('Intensity values','Fontsize', fontSize); ylabel('Number of pixels', 'Fontsize', fontSize); xlim([0 grayLevelsR(end)]); % Scale x axis manually. [pixelCountR1 grayLevelsR1] = imhist(redPlane1); subplot(2, 2, 4); %plot(pixelCountR1, 'r'); stem(grayLevelsR1,pixelCountR1); title('Histogram of Image2', 'Fontsize', fontSize); xlabel('Intensity values','Fontsize', fontSize); ylabel('Number of pixels','Fontsize', fontSize); xlim([0 grayLevelsR1(end)]); % Scale x axis manually. %check image for different different = a1- b1 if different==0 disp('The images are same')%output display else disp('the images are not same') msgbox('Test error. Please check your setting', 'Error') end;](https://image.slidesharecdn.com/resultsreviewofdetectingofhumanerrorsalgorithmimage-160620001659/75/The-method-of-comparing-two-image-files-11-2048.jpg)
The method of comparing two image files
- 1. Results Review ofDetecting ofHuman Errors Algorithm for image files Author: Minh Anh Nguyen Email: minhanhnguyen@q.com Purpose The purpose of this analysis is to determine if the method of comparing two image files can be used for detecting of human errors for channel-not-loaded-correctly issue. There are many algorithms which can be used to compare two image files. These algorithms are: edge detection, Short Time Fourier Transform (STFT), wavelet, and histogram. In this project, histogram algorithm is used to compare two image files. Why use Histogram Histogram is a way of visualizing the largest intensities of an image. It is used to depict problems which originate during image acquisition such as exposure, contrast, dynamic range. The contrast of a grayscale image shows how easily objects in the image can be distinguished. The dynamic range is the number of distinct pixel value in an image. It is used to show image statistics in an easily interpreted visual format. It is also a graph showing the number of pixels in an image at each different intensity value found in that image. The histogram of a digital image is a distribution of its discrete intensity levels in the range [0, L-1]. The number of possible intensity values depends on the numerical type encoding the image. For example, a grayscale image encoded with n=8 bits, it will has an array of size L=28=256. The number of possible intensity values go from 0 representing black to L-1=255 representing white. Since there are 256 different possible intensities, the histogram will graphically display 256 numbers showing the distribution of pixels amongst those greyscale values. When represented as a plot, the x-axis is the intensity value from 0 to 255, and the y-axis is the number of pixels with that intensity value. The y-axis varies depending on the number of the pixels in the image and how their intensities are distributed as shown in figure 2. An image has only one histogram, but many images may have the same histogram. Histograms have many uses. One of the more common is to determine if the overall intensity in the image is high enough for this inspection task. Histogram is used to determine whether an image contains distinct regions of certain grayscale values. If the image has poor dynamic range, then the histogram confirms what one can see by visual inspection. It provides a general description of the appearance of an image and helps identify various components such as the background, objects, and noise. How Histogram Works The operation is very simple. The image is scanned in a single pass and a running count of the number of pixels found at each intensity value is kept. This is then used to construct a suitable histogram. The histogram of an 8-bit image, for example can be thought of as a table with 256 entries, or ‘bins’, indexed from 0 to 255. In bin 0 we record the number of times a gray level of 0 occurs; in bin 1 we record the number of times a grey level of 1 occurs, and so on, up to bin 255.
- 2. Techniques for comparingtwo image files Step1: read images Read two grayscale images into the workspace. Step 2: Resize images To make the image comparison easier, resize the images to have the same width. Step 3: Find matching features between images Step 4: Display error message on the computer monitor screen, if images are not matched. Flow chart for comparing two image files Figure1. Flow chart for comparing two image files Image test in simulation mode The following steps are a procedure to test the image in simulation mode. 1. Make sure image files are loaded into the workspace without any issues. 2. Make sure a correct file is loaded 3. Plot histogram of the image 4. Verify that the histogram value is correct. 5. Positive test: load two image files which have 2 different images and verify that the test results show the difference.
- 3. 6. Negative test:load two image files which have 2 similar images and verify that the test results show the same thing or match. Software Matlab R2015b Labview 2014 Summary of results The following figure illustrates the difference between the displays of the histogram of the same image in a linear and the results of the techniques and flow chart for comparing two image files above. Figure2. Matlab results for comparing two images which are identical.
- 4. Figure3. Matlab resultsfor comparing two images which are not identical. The object being viewed is light in color and it is placed on a dark background, and so the histogram exhibits a good bi-modal distribution. One peak represents the object pixels, other represents the background.
- 5. Figure4. Matlab resultsfor comparing two images which are not identical. The object being viewed is dark in color and it is placed on a light background, and so the histogram exhibits a good bi-modal distribution. One peak represents the object pixels, one represents the background. The peak represents of back ground is at the highest intensity value, X = 241.
- 6. Figure5. Matlab resultsfor comparing two hex pin images which are identical. The resulting of histograms above showed a large spike at 250 intensity value which results in the rest of the histogram being scaled up to 256.
- 7. Figure6. Matlab resultsfor comparing two hex pin images which are not identical
- 8. Figure7. Labview resultsfor comparing two image files which are identical
- 9. Figure8. Labview resultsfor comparing two image files which are not identical These results showed that the method of comparing two image files can be used for detecting of human errors for channel-not-loaded-correctly issue. A histogram is the frequency distribution of the gray levels with the number of pure black values displayed on the left and number of pure white values on the right.
- 10. Matlab Code forcomparing two image files. %% Matlab Code: % Author: Minh Anh Nguyen (minhanhnguyen@q.com) %This function will perform the followign tasks: %1. read in two ".png" file, display both images on the computer screen. %2. histogram of the images % 3. compare both image files. % 4. display a message on the computer screen if both image are not the % same. function [imagefunction] = imagefunction( file1, file2 ); % clear all; % Erase all existing variables. close all; % Close all figures (except those of imtool.) clc;% Clear the command window. imtool close all; % Close all imtool figures. workspace; % Make sure the workspace panel is showing. fontSize = 20; %%%%%%%%%%%%% % %% team: edit your file here % %%% set the path % folder = 'I:F2015_486Aimage'; % %reading images as array to variable 'a' & 'b'. image1 = imread( file1 ); image2= imread( file2 ); % % %reading images as array to variable 'a' & 'b'. % image1 = imread('Loaded-Mid Channel.PNG'); % image2= imread('Gross Misloaded-Mid Channel.PNG'); % % %% info imfinfo( file1 ) imfinfo( file2) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % do not edit anything below here!!!! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% a = image1; b = image2; %% size a1= size(a); b1= size (b); figure; [rows columns numberOfColorBands] = size(a); subplot(2, 2, 1); imshow(a, []); title('Reference image', 'Fontsize', fontSize); set(gcf, 'Position', get(0,'Screensize')); % Maximize figure. redPlane = a(:, :, 1);
- 11. greenPlane = a(:,:, 2); bluePlane = a(:, :, 3); [rows columns numberOfColorBands] = size(b); subplot(2, 2, 3); imshow(b, []); title('Image2', 'Fontsize', fontSize); set(gcf, 'Position', get(0,'Screensize')); % Maximize figure. redPlane1 = b(:, :, 1); greenPlane1 = b(:, :, 2); bluePlane1 = b(:, :, 3); % Let's get its histograms. [pixelCountR grayLevelsR] = imhist(redPlane); subplot(2, 2, 2); %plot(pixelCountR, 'r'); stem(grayLevelsR,pixelCountR, 'r'); title('Histogram of Reference image', 'Fontsize', fontSize); xlabel('Intensity values','Fontsize', fontSize); ylabel('Number of pixels', 'Fontsize', fontSize); xlim([0 grayLevelsR(end)]); % Scale x axis manually. [pixelCountR1 grayLevelsR1] = imhist(redPlane1); subplot(2, 2, 4); %plot(pixelCountR1, 'r'); stem(grayLevelsR1,pixelCountR1); title('Histogram of Image2', 'Fontsize', fontSize); xlabel('Intensity values','Fontsize', fontSize); ylabel('Number of pixels','Fontsize', fontSize); xlim([0 grayLevelsR1(end)]); % Scale x axis manually. %check image for different different = a1- b1 if different==0 disp('The images are same')%output display else disp('the images are not same') msgbox('Test error. Please check your setting', 'Error') end;
- 12. Labview Code forcomparing two image files.