Filtering and masking
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Transcript of Filtering and masking
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FILTERING AND MASKING
-AMUDHINI.R111EC102
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MASK
• A mask is a small matrix whose values are called weight.
• Each mask has an origin ,which is usually one of its positions
• Symmetric mask• Non symmetric mask
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MASK
• Input image equal to output image.• Types of maskConvolutionCross correlation
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CONVOLUTION
Mask is placed on the top of the imageMask input image pixel value multiplied with
mask weighs.summed together to yield a single output
value that is placed in the output image at the location of the pixel being processed on the input
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CONVOLUTION
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CROSS CORRELATION
• Without flipping mask is converted to image.• measure the similarity between images or
parts of images.• Mask symmetric – correlation and convolution
same.
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Cross correlation
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FILTERING
• LINEAR FILTERo have the property that the output is a linear
combination of the inputs• NON LINEAR FILTERo Erosion & dilation
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Smoothing filter
• Low pass filter• Noise reduction & image blurring• Removes the finer details of image• Types of filterMean filterGaussian filterMedian filter
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Mean filter
• Averaging filter.• Positive element in
mask.• Size of the mask
determines the degree of smoothing.
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Gaussian filter
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Median filter
• Used to remove the salt and pepper noise
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Sharpening filter
• emphasize the fine details of an image .• Points of high contrast can be detected by
computing intensity differences in local image regions.
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Sharpening using derivatives
• Computing the derivative of an image has as a result the sharpening of the image.
• The most common way to differentiate an image is by using the gradient.
• Using gradient with finite difference has efficient mask.
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