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Sriramemarose.blogspot.in
COUNTING NUMBER OF FRUITS USING WATERSHEDING
Problem statement:
 Fruits distributed closely wil...
Sriramemarose.blogspot.in
Steps used:
 Filter the image to eliminate noise
 Create an edge emphasizing filter kernel(say...
Sriramemarose.blogspot.in
LIQUID LEVEL IN BEVERAGE BOTTLES
Problem statement:
 Overfill and Underfill identification
 Qu...
Sriramemarose.blogspot.in
Nuts and Bolts
Problem statement:
 Distinguish between nut and bolt
 Count number of nuts and ...
Sriramemarose.blogspot.in
PENCIL LENGTH IDENTIFICATION
Problem statement:
 To identify objects (pencil) length to ensure ...
Sriramemarose.blogspot.in
RICE GRAIN INSPECTION
Problem statement:
 To identify broken grains
 To segment good quality g...
Sriramemarose.blogspot.in
BLISTER INSPECTION
Problem statement: To identify the missing in the tablet strips( Blisters)
Sa...
Sriramemarose.blogspot.in
NUTS SORTING
Problem statement:
 To measure the diameter of the nuts
 To sort them based on th...
Sriramemarose.blogspot.in
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Machine Vision applications development in MatLab

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The document contains simple steps to implement machine vision applications in matlab.
Following are the applications covered in the document,
1. Counting connected objects using watershed algorithm (number of fruits)
2.Liquid level estimation in beverage bottles
3.Segmenting nuts/bolts and counting
4.Pencil length identification
5.Rice grain Inspection
6.Blister Inspection
7.Nut sorting

Published in: Engineering, Technology, Business
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Machine Vision applications development in MatLab

  1. 1. Sriramemarose.blogspot.in COUNTING NUMBER OF FRUITS USING WATERSHEDING Problem statement:  Fruits distributed closely will be considered as a single blob in normal thresholding, therefore counting is impossible with thresholding Sample image: Input image Object boundaries and regional mamima superimposed on orginal image after watershed Output image with counted fruits
  2. 2. Sriramemarose.blogspot.in Steps used:  Filter the image to eliminate noise  Create an edge emphasizing filter kernel(say ‘a’) after converting the image to grayscale  Create a transpose of the filter kernel(say ‘b’)  Obtain two images with one filtered with a and other filtered with b  Calculate the gradient magnitude of the two images  Perform morphological operations and reconstruct the image on the original image  Convert the resultant to binary image and estimate the distance transform  Perform watershedding and segment the watershed boundary lines  Obtain the regional minima of the gradient magnitude by morphological reconstruction of the boundary lines and regional maxima of the original image  Find the number of fruits from the boundaries of the new image Other examples:  Counting number of cells in medical imaging  Connected objects segmentation
  3. 3. Sriramemarose.blogspot.in LIQUID LEVEL IN BEVERAGE BOTTLES Problem statement:  Overfill and Underfill identification  Quantity estimation Sample image: Processed image: Steps involved:  Perform color segmentation based on sample`s threshold  Smoothen the segmented image with suitable filter  Apply morphological operators to remove remaining components other than sample  Calculate the pixels contributing to the sample  Calibrate the pixels in terms quantity(volume)  Label the calibrated quantity value to its corresponding sample Applications:  Pharmaceutical Industries  Beverage Industries  Batch processing
  4. 4. Sriramemarose.blogspot.in Nuts and Bolts Problem statement:  Distinguish between nut and bolt  Count number of nuts and bolts Sample image Processed image Steps involved:  Adjust the contrast after converting to grayscale image  Obtain the binary image with suitable threshold level  Filter the noises with suitable filters  Apply morphological operators to enhance the features  Detect the nuts using hough circle transform with appropriate sensing radius and sensitivity  Subtract the detected nuts from the image, which leaves only with the bolts  Detect the number of bolts using binary labeling Applications:  Automotive Industries  Manufacturing Industries  Industrial Automation
  5. 5. Sriramemarose.blogspot.in PENCIL LENGTH IDENTIFICATION Problem statement:  To identify objects (pencil) length to ensure manufacturing defects Sample image: Test image Pencil length Pencil and lead length Steps involved:  Obtain a Boolean image with suitable threshold value  Apply filters to remove noises  Perform morphological operation to enhance the detection, without altering the object dimension  Segmented the object from background and label the object blob  Find the region properties of the object blob  Measure the pixels and calibrate in real world units Applications:  Manufacturing industries  Factory Automation  Quality control
  6. 6. Sriramemarose.blogspot.in RICE GRAIN INSPECTION Problem statement:  To identify broken grains  To segment good quality grains Sample image: Input image Steps involved:  Eliminate the uneven illumination using morphological tophat operation  Adjust the image contrast  Obtain the binary image with suitable threshold value  Find the connected components in the image to locate each grain, use filter if needed  Find the region properties of the grains  Traverse through every connected component (pixel index list) and check its corresponding properties  If a grain does not satisfy the standard quality (based on its property value), subtract that particular component(grain) from the pixel index list Applications:  Food processing Industries  Quality control
  7. 7. Sriramemarose.blogspot.in BLISTER INSPECTION Problem statement: To identify the missing in the tablet strips( Blisters) Sample images: Good sample Processed image Sample with defect Processed image Steps involved:  Convert to grayscale image and adjust the contrast  Obtain the binary image with suitable threshold value  Eliminate the noise with appropriate filters  Perform morphological operations to segment tablet and tablet strip  Apply hough transform to find the tablets  Based on the detection, mark the blister as defected or good. Applications:  Pharmaceutical Industries  Manufacturing industries
  8. 8. Sriramemarose.blogspot.in NUTS SORTING Problem statement:  To measure the diameter of the nuts  To sort them based on their size Sample image: Processed image: Nut with minimum diameter Detected nuts Steps involved:  Convert to grayscale image and adjust the contrast  Obtain the binary image with suitable threshold value  Eliminate the noise with appropriate filters  Perform morphological operations to enhance the features  Use hough circle transform to detect the nuts since it has circular feature  Detect the required nuts radius using mathematical operators  Segment the detected nuts Applications:  Manufacturing Industries  Industrial Automation  Quality control
  9. 9. Sriramemarose.blogspot.in

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