Xử lý ảnh trong cơ điện tử machine vision chapter 2 digital image fundamentals

TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI XỬ LÝ ẢNH TRONG CƠ ĐIỆN TỬ Machine Vision Giảng viên: TS Nguyễn Thành Hùng Đơn vị: Bộ môn Cơ điện tử, Viện Cơ khí Hà Nội, 2021 Chapter Digital Image Fundamentals Image Sensing and Acquisition Image Sampling and Quantization Some Basic Relationships Between Pixels Basic Mathematical Tools Used in Digital Image Processing Image Sensing and Acquisition ❖Image Sensing and Acquisition (a) Single sensing element (b) Line sensor (c) Array sensor Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Image Sensing and Acquisition ❖Image Acquisition Using a Single Sensing Element Combining a single sensing element with mechanical motion to generate a 2-D image Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Image Sensing and Acquisition ❖Image Acquisition Using Sensor Strips (a) Image acquisition using a linear sensor strip Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) (b) Image acquisition using a circular sensor strip Image Sensing and Acquisition ❖Image Acquisition Using Sensor Arrays An example of digital image acquisition (a) Illumination (energy) source (b) A scene (c) Imaging system (d) Projection of the scene onto the image plane (e) Digitized image Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Image Sensing and Acquisition ❖A Simple Image Formation Model ➢ We denote images by two-dimensional functions of the form f(x, y) f(x, y) = i(x, y) r(x, y) • i(x, y): the amount of source illumination incident on the scene being viewed • r(x, y): the amount of illumination reflected by the objects in the scene Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Digital Image Fundamentals Image Sensing and Acquisition Image Sampling and Quantization Some Basic Relationships Between Pixels Basic Mathematical Tools Used in Digital Image Processing Image Sampling and Quantization ❖Basic Concepts in Sampling and Quantization ➢ Digitizing the coordinate values is called sampling Digitizing the amplitude values is called quantization (a) Continuous image (b) A scan line showing intensity variations along line AB in the continuous image (c) Sampling and quantization (d) Digital scan line (The black border in (a) is included for clarity It is not part of the image) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Image Sampling and Quantization ❖Basic Concepts in Sampling and Quantization (a) Continuous image projected onto a sensor array (b) Result of image sampling and quantization Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 10 Basic Mathematical Tools Used in Digital Image Processing ❖Basic Set Operations Set operations involving grayscale images (a) Original image (b) Image negative obtained using grayscale set complementation (c) The union of image (a) and a constant image Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 42 Basic Mathematical Tools Used in Digital Image Processing ❖Logical Operations Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 43 Basic Mathematical Tools Used in Digital Image Processing ❖Logical Operations Illustration of logical operations involving foreground (white) pixels Black represents binary 0’s and white binary 1’s The dashed lines are shown for reference only They are not part of the result Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 44 Basic Mathematical Tools Used in Digital Image Processing ❖Single-Pixel Operations (a) An 8-bit image (b) Intensity transformation function used to obtain the digital equivalent of a “photographic” negative of an 8-bit image The arrows show transformation of an arbitrary input intensity value into its corresponding output value (c) Negative of (a), obtained using the transformation function in (b) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 45 Basic Mathematical Tools Used in Digital Image Processing ❖Neighborhood Operations Local averaging using neighborhood processing The procedure is illustrated in (a) and (b) for a rectangular neighborhood (c) An aortic angiogram (d) The result of using Eq (2) with m = m = 41 The images are of size 790 x 686 pixels (2) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 46 Basic Mathematical Tools Used in Digital Image Processing ❖Geometric Transformations Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 47 Basic Mathematical Tools Used in Digital Image Processing ❖Geometric Transformations Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 48 Basic Mathematical Tools Used in Digital Image Processing ❖Geometric Transformations Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 49 Basic Mathematical Tools Used in Digital Image Processing ❖Geometric Transformations (a) A 541 x 421 image of the letter T (b) Image rotated -210 using nearest-neighbor interpolation for intensity assignments (c) Image rotated -210 using bilinear interpolation (d) Image rotated -210 using bicubic interpolation (e)-(h) Zoomed sections (each square is one pixel, and the numbers shown are intensity values) Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 50 Basic Mathematical Tools Used in Digital Image Processing ❖Geometric Transformations (a) A digital image (b) Rotated image (note the counterclockwise direction for a positive angle of rotation) (c) Rotated image cropped to fit the same area as the original image (d) Image enlarged to accommodate the entire rotated image Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 51 Basic Mathematical Tools Used in Digital Image Processing ❖Image Registration Image registration (a) Reference image (b) Input (geometrically distorted image) Corresponding tie points are shown as small white squares near the corners (c) Registered (output) image (note the errors in the border) (d) Difference between (a) and (c), showing more registration errors Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 52 Basic Mathematical Tools Used in Digital Image Processing ❖Vector and Matrix Operations Forming a vector from corresponding pixel values in three RGB component images Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 53 Basic Mathematical Tools Used in Digital Image Processing ❖Image Transforms ➢ 2-D linear transforms ➢ Inverse transform Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 54 Basic Mathematical Tools Used in Digital Image Processing ❖Image Transforms General approach for working in the linear transform domain Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 55 Basic Mathematical Tools Used in Digital Image Processing ❖Image Transforms (a) Image corrupted by sinusoidal interference (b) Magnitude of the Fourier transform showing the bursts of energy caused by the interference (the bursts were enlarged for display purposes) (c) Mask used to eliminate the energy bursts (d) Result of computing the inverse of the modified Fourier transform Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) 56 ... ? ?Digital image processing,” Pearson (20 18) 12 Image Sampling and Quantization ❖Representing Digital Images Rafael C Gonzalez, Richard E Woods, ? ?Digital image processing,” Pearson (20 18) 13 Image. .. (20 18) 22 Chapter Digital Image Fundamentals Image Sensing and Acquisition Image Sampling and Quantization Some Basic Relationships Between Pixels Basic Mathematical Tools Used in Digital Image. .. scene Rafael C Gonzalez, Richard E Woods, ? ?Digital image processing,” Pearson (20 18) Chapter Digital Image Fundamentals Image Sensing and Acquisition Image Sampling and Quantization Some Basic