Xử lý ảnh trong cơ điện tử: Machine Vision. Chapter 3. Intensity Transformations and Spatial Filtering91

TRƯỜNG ĐẠI HỌC BÁCH KHOA XỬ LÝ ẢNH TRONG CƠ ĐIỆN Machine Vision Giảng viên: TS Nguyễn Thành Hùn Đơn vị: Bộ môn Cơ điện tử, Viện Cơ Hà Nội, 2021 Chapter Intensity Transformations and ❖Two principal categories of spatial processing are intens spatial filtering ➢ Intensity transformations operate on single pixels of an as contrast manipulation and image thresholding ➢ Spatial filtering performs operations on the neighborhoo image ➢ Examples of spatial filtering include image smoothing an Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters Sharpening (Highpass) Spatial Filters Highpass, Bandreject, and Bandpass Filters from Combining Spatial Enhancement Methods Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Background ❖The Basics of Intensity Transformations and Spatial F ➢ The spatial domain processes are based on the expressio 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 poin is moved from pixel to pixel in the Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Background ❖The Basics of Intensity Transformations and Spatial F ➢ intensity (also called a gray-level, or mapping) transformatio Inte (a) (b) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters Sharpening (Highpass) Spatial Filters Highpass, Bandreject, and Bandpass Filters from Combining Spatial Enhancement Methods Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Some Basic Intensity Transformatio ❖Three basic types of functions ➢ linear (negative and identity transformations) ➢ logarithmic (log and inverse-log transformations) ➢ power-law (nth power and nth root transformations) Some basic Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Some Basic Intensity Transformatio ❖Image Negatives (a) A digital mammogram (b) Negative image obtained using (Image (a) Courtesy of General Electric Medical Systems.) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Some Basic Intensity Transformatio ❖Log Transformations where c is a co (a) Fourier spectrum displayed as a grayscale image (b) Result of applying the log transformation in Eq (3-4) with c = Both images are scaled to the range [0, 255] Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Some Basic Intensity Transformatio ❖Power-Law (Gamma) Transformations w Plots of the gamma e of (c = in all case Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Unsharp Masking and Highboost Filtering (a) Unretouched “soft-tone” digital image of size 469x600 pixels (b) Image blurred using a filter with = (c) Mask (d) Result of unsharp masking using Eq (3-65) with k = filtering with k = and k = respectively Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Image Sharpening—the Gradient ➢ The gradient of an image f at coordinates (x, y) ➢ The magnitude (length) of vector f Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Image Sharpening—the Gradient ➢ Roberts cross-gradient operators Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Image Sharpening—the Gradient ➢ Sobel operators Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Image Sharpening—the Gradient ➢ Filter masks (a) A 3x3 region of an image, where the zs are intensity values (b)–(c) Rob (d)–(e) Sobel operators All the kernel coefficients sum to zero, as expected Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Sharpening (Highpass) Spatia ❖Image Sharpening—the Gradient ➢ Example: Using the gradient for edge enhancement (a) Image of a contact lens (note defects on the boundary at and o’c (b) Sobel gradient (Original image courtesy of Perceptics Corpo Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters Sharpening (Highpass) Spatial Filters Highpass, Bandreject, and Bandpass Filters from Combining Spatial Enhancement Methods Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Highpass, Bandreject, and Bandpass Filters f ❖Transfer functions of ideal 1-D filters Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Highpass, Bandreject, and Bandpass Filters f ❖Transfer functions of ideal 1-D filters Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Highpass, Bandreject, and Bandpass Filters f ❖Transfer functions of ideal 1-D filters (a) A 1-D spatial lowpass obtained by rotating the A zone plate image of size 597x597 pixels Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Highpass, Bandreject, and Bandpass Filters f ❖Transfer functions of ideal 1-D filters (a) Zone plate image filtered with a separable lowpass kernel (b filtered with the isotropic lowpass kernel in Fig 3.60(b) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Highpass, Bandreject, and Bandpass Filters f ❖Transfer functions of ideal 1-D filters Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters Sharpening (Highpass) Spatial Filters Highpass, Bandreject, and Bandpass Filters from Combining Spatial Enhancement Methods Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Combining Spatial Enhancemen ➢ Laplacian is superior for enhancing fine detail ➢ The gradient has a stronger response in areas of significant intensity (a) Image of whole body bone scan (b) Laplacian of (a) (c) Sharpened image obt (d) Sobel gradient of image (a) (Original image courtesy of G.E Medical Systems Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Combining Spatial Enhancemen (e) Sobel image smoothed with a 3x3 box filter (f) Mask image formed by the product of obtained by the adding images (a) and (f) (h) Final result obtained by applying a powerimages (g) and (h) with (a) (Original image courtesy of G.E Medical Systems.) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) .. .Chapter Intensity Transformations and ❖Two principal categories of spatial processing are intens spatial filtering ➢ Intensity transformations operate on single... Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters... Pearson (2018) Chapter Intensity Transformations and Background Some Basic Intensity Transformation Functions Histogram Processing Fundamentals of Spatial Filtering Smoothing (Lowpass) Spatial Filters