Xử lý ảnh trong cơ điện tử: Machine Vision. Chapter 4. Color Image Processing90

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 Color Image Proce Color Fundamentals Color Models Basics of Full-Color Image Processing Color Transformations Color Image Smoothing and Sharpening Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Color spectrum Color spectrum seen by passing white light through a prism Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Wavelengths Wavelengths comprising the visible range of the electromagneti Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖The absorption of light by the red, green, and blue cones in the Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Primary and secondary colors of light and pigments Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Hue, Saturation, and Brightness ➢ The characteristics generally used to distinguish one color from another are saturation ➢ Brightness embodies the achromatic notion of intensity ➢ Hue is an attribute associated with the dominant wavelength in a mixture of represents dominant color as perceived by an observer ➢ Saturation refers to the relative purity or the amount of white light mixed w spectrum colors are fully saturated ➢ Hue and saturation taken together are called chromaticity → a color may be brightness and chromaticity Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Hue, Saturation, and Brightness ➢ The amounts of red, green, and blue needed to form any particular color are tristimulus values, and are denoted, X, Y, and Z, respectively ➢ A color is then specified by its trichromatic coefficients, Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖CIE chromaticity diagram CIE chromaticity dia composition as a fun For any value of x an of z (blue) is obtained z = – (x + y) The p for example, has app 25% red content It fo composition of blue i Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Fundamentals ❖Color gamut Illustr color m and co (shade Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Models ❖The HSI Color Model ➢ EXAMPLE: The HSI values corresponding to the image of the RGB color c HSI components of the image in Fig (p 14): (a) hue, (b) saturation, an Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Models ❖The HSI Color Model ➢ Manipulating HSI Component Images (a) RGB image and the components of its corresponding HSI image: (b) hue, (c) saturation, and (d) intensity Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) (a)-(c) Modif (d) Resulting Chapter Color Image Proce Color Fundamentals Color Models Basics of Full-Color Image Processing Color Transformations Color Image Smoothing and Sharpening Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Basics of Full-Color Image Pro ➢ Let c represent an arbitrary vector in RGB color space: ➢ In order for per-component-image and vector-based processing to be equival have to be satisfied: (1) the process has to be applicable to both vectors and scalars; (2) the operation on each component of a vector (i.e., each voxel) must be inde other components Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Basics of Full-Color Image Pro Spatial neighborhoods for grayscale and RGB color images Observe in (b) tha pair of spatial coordinates, (x, y), addresses the same spatial location in all thre Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Color Image Proce Color Fundamentals Color Models Basics of Full-Color Image Processing Color Transformations Color Image Smoothing and Sharpening Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Transformations ❖Formulation ➢ Color transformations for multispectral images where n is the total number of component images, r are the intensity values of the input component images, s i are the spatially corresponding intensities in the output component images, and T i are a set of transformation or color mapping functions that operate on r i to produce s i i Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Transformations ❖Formulation Adjusting the intensity of an image using color transformations (a) Original image (b) its intensity by 30% (i.e., letting k = 0.7) (c) The required RGB mapping function (d)– CMYK mapping functions (f) The required CMY mapping function (g)–(h) The requi functions Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Transformations ❖Color Complements Color complements on the color circle Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) EXAMPLE: Com Color complement tran Complement transform based on the RGB map the RGB complement u Color Transformations ❖Color Slicing EXAMP ➢ Using a cube of width W ➢ Using a sphere of radius R Color-slicing transform RGB cube of width ce and (b) reds within an centered at the same p sphere were replaced b Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Transformations ❖Histogram Processing of Color Images ➢ EXAMPLE: Histogram equalization in the HSI color space Histogram equalization (followed by saturation adjustment) in the HSI colo Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Chapter Color Image Proce Color Fundamentals Color Models Basics of Full-Color Image Processing Color Transformations Color Image Smoothing and Sharpening Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Image Smoothing and Sha ❖Color Image Smoothing ➢ The average of the RGB component vectors in this neighborhood is Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Color Image Smoothing and Sha ❖Color Image Smoothing ➢ EXAMPLE: Color image smoothing by neighborhood averaging HSI components of the RGB co (a) RGB image (b) Red component image (c) Green component (d) Blue component Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) Image smoothing with a averag component image (b) Result of image and converting to RGB Color Image Smoothing and Sha ❖Color Image Sharpening ➢ EXAMPLE: Image sharpening using the Laplacian Image sharpening using the Laplacian (a) Result of proc Result of processing the HSI intensity component and co between the two results Rafael C Gonzalez, Richard E Woods, “Digital image processing,” Pearson (2018) ... “Digital image processing,” Pearson (2018) (a)-(c) Modif (d) Resulting Chapter Color Image Proce Color Fundamentals Color Models Basics of Full -Color Image Processing Color Transformations Color Image. .. Woods, “Digital image processing,” Pearson (2018) Chapter Color Image Proce Color Fundamentals Color Models Basics of Full -Color Image Processing Color Transformations Color Image Smoothing... Woods, “Digital image processing,” Pearson (2018) Chapter Color Image Proce Color Fundamentals Color Models Basics of Full -Color Image Processing Color Transformations Color Image Smoothing