Xử lý ảnh trong cơ điện tử machine vision chapter 4 color image processing

Color Fundamentals❖Hue, Saturation, and Brightness ➢ The amounts of red, green, and blue needed to form any particular color are called the tristimulus values, and are denoted, X, Y, an

XỬ LÝ ẢNH TRONG CƠ ĐIỆN TỬ

Machine Vision

TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI

Giảng viên: TS Nguyễn Thành Hùng

Chapter 4 Color Image Processing

1 Color Fundamentals

❖Color spectrum

1 Color Fundamentals

❖Wavelengths

Wavelengths comprising the visible range of the electromagnetic spectrum.

1 Color Fundamentals

❖The absorption of light by the red, green, and blue cones in the eye

Absorption of light by the red, green, and blue cones in the human eye as

a function of wavelength.

1 Color Fundamentals

❖Primary and secondary colors of light and pigments.

1 Color Fundamentals

❖Hue, Saturation, and Brightness

➢ The characteristics generally used to distinguish one color from another are brightness, hue, and saturation.

➢ Brightness embodies the achromatic notion of intensity.

➢ Hue is an attribute associated with the dominant wavelength in a mixture of light waves Hue

represents dominant color as perceived by an observer.

➢ Saturation refers to the relative purity or the amount of white light mixed with a hue The pure

spectrum colors are fully saturated.

➢ Hue and saturation taken together are called chromaticity → a color may be characterized by its

1 Color Fundamentals

❖Hue, Saturation, and Brightness

➢ The amounts of red, green, and blue needed to form any particular color are called the

tristimulus values, and are denoted, X, Y, and Z, respectively

➢ A color is then specified by its trichromatic coefficients,

1 Color Fundamentals

❖CIE chromaticity diagram

CIE chromaticity diagram, which shows color composition as a function of x (red) and y (green) For any value of x and y, the corresponding value

of z (blue) is obtained from Eq (7-4) by noting that

z = 1 – (x + y) The point marked green in Fig 7.5 , for example, has approximately 62% green and 25% red content It follows from Eq (7-4) that the composition of blue is approximately 13%.

1 Color Fundamentals

❖Color gamut

Illustrative color gamut of color monitors (triangle) and color printing devices (shaded region).

Chapter 4 Color Image Processing

1 Color Fundamentals

2 Color Models

3 Pseudocolor Image Processing

4 Basics of Full-Color Image Processing

5 Color Transformations

6 Color Image Smoothing and Sharpening

2 Color Models

❖Color model

➢ The purpose of a color model (also called a color space or color system) is to facilitate the

specification of colors in some standard way.

➢ A color model is a specification of

(1) a coordinate system, and

(2) a subspace within that system, such that each color in the model is represented by a single

point contained in that subspace.

➢ RGB (red, green, blue) model: color monitors and a broad class of color video cameras

➢ CMY (cyan, magenta, yellow) and CMYK (cyan, magenta, yellow, black) models : printing

➢ HSI (hue, saturation, intensity) model : closely with the way humans describe and interpret color

2 Color Models

❖The RGB Color Model

Schematic of the RGB color cube Points along the main diagonal have gray values, from black

at the origin to white at point (1, 1, 1).

2 Color Models

❖The RGB Color Model

➢ When fed into an RGB monitor, these three images combine on the screen to produce a

composite color image.

➢ The number of bits used to represent each pixel in RGB space is called the pixel depth.

➢ Each RGB color pixel has a depth of 24 bits.

➢ The term full-color image is used often to denote a 24-bit RGB color image.

➢ The total number of possible colors in a 24-bit RGB image is (2 8 ) 3 = 16, 777, 216.

2 Color Models

❖The RGB Color Model

➢ EXAMPLE: Generating a cross-section of the RGB color cube and its thee hidden planes.

➢ CMY to CMYK:

▪ Let:

▪ If K = 1 then we have pure black, with no color contributions

2 Color Models

❖The CMY and CMYK Color Models

➢ cyan, magenta, and yellow are the secondary colors of light or, alternatively, they are the primary colors of pigments

where the assumption is that all RGB color values have been normalized to the range [0, 1]

2 Color Models

❖The CMY and CMYK Color Models

2 Color Models

❖The HSI Color Model

2 Color Models

❖The HSI Color Model

Hue and saturation in the HSI color model

The dot is any color point The angle from the red axis gives the hue The length of the vector is the saturation The intensity of all

colors in any of these planes is given by the

2 Color Models

❖The HSI Color Model

The HSI color model based on (a) triangular, and (b) circular color planes The triangles and

Color detection

2 Color Models

❖The HSI Color Model

➢ Converting Colors from RGB to HSI

2 Color Models

❖The HSI Color Model

➢ Converting Colors from HSI to RGB

RG sector (00  H < 1200): GB sector (1200  H < 2400): BR sector (2400  H < 3600):

2 Color Models

❖The HSI Color Model

➢ EXAMPLE: The HSI values corresponding to the image of the RGB color cube.

2 Color Models

❖The HSI Color Model

➢ Manipulating HSI Component Images

Chapter 4 Color Image Processing

3 Basics of Full-Color Image Processing

➢ Let c represent an arbitrary vector in RGB color space:

➢ In order for per-component-image and vector-based processing to be equivalent, two conditions

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 independent of the

other components

3 Basics of Full-Color Image Processing

Chapter 4 Color Image Processing

4 Color Transformations

❖Formulation

➢ Color transformations for multispectral images

where n is the total number of component images, ri

are the intensity values of the input component images,

si are the spatially corresponding intensities in the

output component images, and Ti are a set of

transformation or color mapping functions that operate

on ri to produce si

A full-color image and its various

4 Color Transformations

❖Formulation

Adjusting the intensity of an image using color transformations (a) Original image (b) Result of decreasing

its intensity by 30% (i.e., letting k = 0.7) (c) The required RGB mapping function (d)–(e) The required

4 Color Transformations

❖Color Complements EXAMPLE: Computing color image complements.

4 Color Transformations

❖Color Slicing

➢ Using a cube of width W

➢ Using a sphere of radius R0

EXAMPLE: Color slicing

Color-slicing transformations that detect (a) reds within an RGB cube of width centered at (0.6863, 0.1608, 0.1922), and (b) reds within an RGB sphere of radius 0.1765

centered at the same point Pixels outside the cube and

4 Color Transformations

❖Histogram Processing of Color Images

➢ EXAMPLE: Histogram equalization in the HSI color space.

Chapter 4 Color Image Processing

5 Color Image Smoothing and Sharpening

❖Color Image Smoothing

➢ The average of the RGB component vectors in this neighborhood is

5 Color Image Smoothing and Sharpening

❖Color Image Smoothing

➢ EXAMPLE: Color image smoothing by neighborhood averaging.

HSI components of the RGB color image (a) Hue (b) Saturation (c) Intensity

5 Color Image Smoothing and Sharpening

❖Color Image Sharpening

➢ EXAMPLE: Image sharpening using the Laplacian.