The document outlines various image processing techniques for identifying and counting objects in different contexts, such as counting fruits, identifying liquid levels in beverage bottles, distinguishing nuts and bolts, measuring pencil lengths, inspecting rice grains, and checking blister packaging defects. Each application includes a problem statement, sample images, and specific steps involving filtering, morphological operations, and analysis to achieve desired outcomes. Industries such as pharmaceuticals, manufacturing, and quality control are highlighted as primary beneficiaries of these techniques.

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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

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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

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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

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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

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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

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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

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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

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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

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