Filtering and masking

1. FILTERING AND MASKING -AMUDHINI.R 111EC102

2. 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

3. MASK Input image equal to output image. Types of mask Convolution Cross correlation

4. CONVOLUTION Mask is placed on the top of the image Mask 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

5. CONVOLUTION

6. CROSS CORRELATION Without flipping mask is converted to image. measure the similarity between images or parts of images . Mask symmetric – correlation and convolution same.

7. Cross correlation

8. FILTERING LINEAR FILTER h ave the property that the output is a linear combination of the inputs NON LINEAR FILTER Erosion & dilation

9. Smoothing filter Low pass filter Noise reduction & image blurring Removes the finer details of image Types of filter Mean filter Gaussian filter Median filter

10. Mean filter Averaging filter. Positive element in mask. Size of the mask determines the degree of smoothing.

11. Gaussian filter

12. Median filter Used to remove the salt and pepper noise

13. Sharpening filter emphasize the fine details of an image . Points of high contrast can be detected by computing intensity differences in local image regions .

14. 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.