XỬ LÝ ẢNH TRONG CƠ ĐIỆN TỬ
Machine Vision
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TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI
Giảng viên: TS. Nguyễn Thành Hùng
Đơn vị: Bộ môn Cơ điện tử, Viện Cơ khí
Hà Nội, 2021
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Chapter 3. Intensity Transformations and Spatial Filtering
Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018).
❖Two principal categories of spatial processing are intensity transformations and
spatial filtering.
➢Intensity transformations operate on single pixels of an image for tasks such
as contrast manipulation and image thresholding.
➢Spatial filtering performs operations on the neighborhood of every pixel in an
image.
➢Examples of spatial filtering include image smoothing and sharpening.
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Chapter 3. Intensity Transformations and Spatial Filtering
1. Background
2. Some Basic Intensity Transformation Functions
3. Histogram Processing
4. Fundamentals of Spatial Filtering
5. Smoothing (Lowpass) Spatial Filters
6. Sharpening (Highpass) Spatial Filters
7. Highpass, Bandreject, and Bandpass Filters from Lowpass Filters
8. Combining Spatial Enhancement Methods
Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018).
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1. Background
❖The Basics of Intensity Transformations and Spatial Filtering
➢The spatial domain processes are based on the expression
Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018).
where f(x, y) is an input image, g(x, y) is the
output image, and T is an operator on f defined
over a neighborhood of point (x, y).
A 3x3 neighborhood about a point (x0, y0) in an image. The neighborhood
is moved from pixel to pixel in the image to generate an output image.
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1. Background
❖The Basics of Intensity Transformations and Spatial Filtering
➢intensity (also called a gray-level, or mapping) transformation function
Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018).
Intensity transformation functions.
(a) Contrast stretching function.
(b) Thresholding function.