lect03_ImageProc_notes - A @ x, y D W @ x, y D = 1 W @ x, y...

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1. Images are point samples 1. Of created geometry - lines, polygons, 3D models, etc. 2. Of real world - People, scenery, geographical info, organs and tissues, etc. 3. Of other samples - Scanning photos, editing and processing images 2. Types of image editing/processing 1. Editing 1. Cutting and pasting 2. Compositing (blending) 1. Alpha-value: 3. Rotation, translation, scaling 4. Gamma/contrast/color changing 5. Up/down sampling 6. Blurring, sharpening 2. Processing 1. Edge detection 2. Feature detection 3. Written character recognition 4. Registration 3. Weighted area sampling 1. Box filter: Constant distribution 2. Cone filter: Linear distribution 4. Frequency domain filtering 1. Low-pass: attenuate high-frequencies - Blurring 2. High-pass: attenuate low-frequencies - Edge enhancing 3. Band-pass: attenuate very high and low frequencies - Both smoothing and edge enhancement C new = H 1 −α L C 1 C 2 α C 2 8 C 1 , α < W @ x, y D Å
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Unformatted text preview: A @ x, y D W @ x, y D = 1 W @ x, y D = H R r L R, r = &quot; x 2 + y 2 R W @ x, y D H f g L@ x D = f @ y D g @ y x D y H f g L@ i D = j = f @ j D g @ j i D 0.5 1 1.5 2 2.5 3 3.5 4 0.25 0.5 0.75 1 1.25 1.5 1.75 2 0.5 1 1.5 2 2.5 3 3.5 4 0.25 0.5 0.75 1 1.25 1.5 1.75 2-2-1.5-1-0.5 0.5 1 1.5 2 0.25 0.5 0.75 1 1.25 1.5 1.75 2 f @ x D g @ x D f @ y D g @ y 2 D H f g L@ 2 D 5. Convolution 1. Continuous definition 2. Discrete definition 3. Area under the product curve of f and g 6. Examples 1. Gaussian filter 2. Low-pass blur 3. Edge detectors (discrete) g @ x D = Sinc @ x D = Sin @ x D x g @ x D = 1 2 x 2 2 2 Sobel Operator Prewitt Operator-4-2 2 4 0.1 0.2 0.3 0.4-4-2 2 4-0.2 0.2 0.4 0.6 0.8 1...
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lect03_ImageProc_notes - A @ x, y D W @ x, y D = 1 W @ x, y...

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