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.