Lecture 4 Flashcards
Image Filtering
techniques for modifying or enhancing an image
Types of Image Filtering
Linear filters and Non-linear filters
Which are the linear filters
image smoothing/blurring
edge detection
image sharpening
Which are the non-linear filters
morphological operators (dilation,erosion)
median filter (noise reduction)
matched filters
the value in each output pixel depends on the which values
the local values of neighboring pixels
what does a non-linear filtering
replace each pixel by a non-linear combination of its neighbors
what does linear filtering
replace each pixel by a linear combination of its neighbors
Image Filtering Steps
- move filter matrix over the image
- multiply the corresponding image pixel
- sum the results and store it as a new pixel value
Image smoothing
replace each pixel with the average intensity of its neighbors
cross-correlation
measures the similarities between two signals
convolution
measures the similarities between two signals, but the filter is flipped upside-down
which convolutions can save a lot of time
separable convolutions
strides
skip steps at a time (for efficiancy)
padding
extending the frame of an image with empty pixels, so when the kernel goes over the image we can maintain the size of the image
why we need padding
allow for more space for the kernel to cover the whole image