Download - Filtering and masking
FILTERING AND MASKING
-AMUDHINI.R111EC102
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
MASK
• Input image equal to output image.• Types of maskConvolutionCross correlation
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
CONVOLUTION
CROSS CORRELATION
• Without flipping mask is converted to image.• measure the similarity between images or
parts of images.• Mask symmetric – correlation and convolution
same.
Cross correlation
FILTERING
• LINEAR FILTERo have the property that the output is a linear
combination of the inputs• NON LINEAR FILTERo Erosion & dilation
Smoothing filter
• Low pass filter• Noise reduction & image blurring• Removes the finer details of image• Types of filterMean filterGaussian filterMedian filter
Mean filter
• Averaging filter.• Positive element in
mask.• Size of the mask
determines the degree of smoothing.
Gaussian filter
Median filter
• Used to remove the salt and pepper noise
Sharpening filter
• emphasize the fine details of an image .• Points of high contrast can be detected by
computing intensity differences in local image regions.
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.