Colour and Pattern Matching

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Kent Yeap 2017-2-28

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https://ww2.mathworks.cn/matlabcentral/answers/327309-colour-and-pattern-matching

评论： Image Analyst 2017-3-10

Hi, I am developing a Fabric Pattern and Colour Matching System. I hope to have the right thought process on this project. The system is to find matching Database fabric image based on user’s input of random Testing fabric images. The system is as follow:

Database consists of a number of fabric images, of various colour & pattern (as shown in the attachment).
User inputs random testing images (might / might not exist in the database).
System finds the EXACT matching database fabric image (if available); or shows ALMOST matched database image.

I am wondering what colour space would be more suitable for this system. HSV, RGB or other colour space? Besides, what pattern recognition / feature extraction + classification methods should I use? Currently, I have planned to use normalized cross correlation for pattern recognition; however, besides this method, are there any other better pattern recognition methods?

Any links to similar/existing projects is highly appreciated as well!

[Add. Info: Full database fabric image & testing images can be found in this Google Drive link https://drive.google.com/drive/folders/0B3kRUtBpzKROamx3S1dkLUtNRFE?usp=sharing

Thank you in advance for your help!

Database Images

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Image Analyst 2017-2-28

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https://ww2.mathworks.cn/matlabcentral/answers/327309-colour-and-pattern-matching#answer_256670

Normalized cross correlation is good for finding where a pattern exists in an image, and how closely it matches the template. It will not be so good when the scale (magnification) is not matched up, like the pattern template is in the reference image but just has a different size, or if the pattern template is rotated. For scale and rotation invariant metrics, you might want to look at Hu's moments http://www.youtube.com/watch?v=Nc06tlZAv_Q. There are also a number of other metrics you could think up, like a list of dominant colors (see http://www.mathworks.com/matlabcentral/fileexchange/28164-color-frequency-image ) and their area fractions, certain sizes or shapes of things in the image, etc. You could build up a feature vector of a bunch of things. So you look at the overall accuracy of the things as a group rather than just using any one particular metric. This is the basis of how CBIR works, and you might investigate that field for more ideas.

As an aside, you might be interested in the color thesaurus: http://colorthesaurus.epfl.ch/

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Kent Yeap 2017-3-4

Hi, thank you for your fast reply. The link I posted just now is inaccurate , the correct one should be this one. http://labs.tineye.com/color/

Yeah, you are right. Kmeans and pre-defined list would not be suitable if the testing images consists of lots of different & undefined colors.

If a user were to present a cloth which does not exist in the database, the system would just show the most similar fabric images.

I am sorry to say I am still very confused and vague on what should I do for this project, as I just ventured into MATLAB image processing. But I will try my best to learn. Are you able to provide some guideline so that I am able to work on this project? I find that my previous project description is vague, here's a clearer project description:

Both database and testing fabric images are captured by user. Database would consists of 13 Plain, 15 Checks and 12 Striped fabrics. Meanwhile, the testing images would be mostly captured from fabric which are already present in the database (Batch A) and some other unknown random fabrics (Batch B). The main aim of this project would be to find the exact matching database fabric based on the testing fabric (Batch A).

For simplicity, the system will be implemented in a closed controlled illumination environment; hence the fabric images will not be affected by different lighting environment. The camera will be placed at a fixed height above the fabric to ensure that the scale of all captured fabric will be the same. Both database images and testing images will be captured using the same camera from the same fixed height and with vertical orientation.

Here's a link to the complete updated database & testing images: https://drive.google.com/drive/folders/0B3kRUtBpzKROWmVDVWhPSG11cWc?usp=sharing

Can you give me a list of steps that I should do for this project?

Thank you.

Kent Yeap 2017-3-5

在 MATLAB Online 中打开

Hi, thank you. I have followed what you have said, and used the imopen() and developed the edge detector. Here's the code that I have written.

clear;
i = imread('C5.jpg');
grayImg = rgb2gray(i);
bwImage=im2bw(i);
se = strel('disk',5);
grayImageOpen = imopen(bwImage,se);
BW = edge(grayImageOpen,'Sobel',.2);
imshowpair(i,BW,'montage');
cc = bwconncomp(BW);
measurements = regionprops(cc, 'BoundingBox');
% fprintf('Found %d regions\n', cc.NumObjects);
numHorizontalRegions = 0;
numVerticalRegions = 0;
for k = 1 : cc.NumObjects
  thisBB = measurements(k).BoundingBox;
  horizAspRatio(k) = thisBB(4)/thisBB(3);
    vertiAspRatio(k) = thisBB(3)/thisBB(4);
      % check horizontal
    if (thisBB(3) >= 30) && (horizAspRatio(k) >1/40)
      numHorizontalRegions = numHorizontalRegions + 1;
      end
      % check vertical 
      if (thisBB(4) >= 30) && (vertiAspRatio(k) > 1/40)
          numVerticalRegions = numVerticalRegions + 1;
      end
end
if (numHorizontalRegions > 1) && (numVerticalRegions > 1)
    fprintf('Pattern is checks fabric \n');
end 
if (numHorizontalRegions > 1) && (numVerticalRegions == 0) || (numHorizontalRegions == 0) && (numVerticalRegions > 1)
    fprintf('Pattern is striped fabric \n');
end 
if (numHorizontalRegions == 0) && (numVerticalRegions == 0)
    fprintf('Pattern is plain fabric \n');
end

The code works well for both striped and checks fabrics. However, for plain fabric, random edges are shown on the binary image. Is there anything wrong with my code? Can you help me to have a look?

Here's a link to the complete updated database & testing images: https://drive.google.com/drive/folders/0B3kRUtBpzKROOHl5ZjM2ZGdoNE0?usp=sharing

Thank you.