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

Basic Mathematical Tools Used in Digital Image Processing... a Image acquisition using a linear sensor stripb Image acquisition using a circular sensor strip... Basic Mathematical Tools

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

1 Image Sensing and Acquisition

2 Image Sampling and Quantization

3 Some Basic Relationships Between Pixels

4 Basic Mathematical Tools Used in Digital Image Processing

(a) Single sensing element

(b) Line sensor

(c) Array sensor

Combining a single sensing element with mechanical motion to generate a 2-D image.

(a) Image acquisition using a linear sensor strip

(b) Image acquisition using

a circular sensor strip

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.

➢ 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

1 Image Sensing and Acquisition

2 Image Sampling and Quantization

3 Some Basic Relationships Between Pixels

4 Basic Mathematical Tools Used in Digital Image Processing

➢ 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)

(a) Continuous image projected onto a sensor array

(b) Result of image sampling and quantization

(a) Image plotted as a surface (b) Image displayed as a visual intensity array (c) Image shown as a 2-D

numerical array (The numbers 0, 5, and 1 represent black, gray, and white, respectively.)

Coordinate convention used to represent digital images

Because coordinate values are integers, there is a one-to-one

correspondence between x and

y and the rows (r) and columns

(c) of a matrix.

➢ The number of intensity levels: L

where k is an integer.

➢ The discrete levels are equally spaced and that they are integers in the range

[0, L-1]

➢ The number, b, of bits required to store a digital image is

➢ When M = N:

Number of megabytes required to store

images for various

values of N and k

➢ coordinate indexing or subscript indexing (x, y) vs linear indexing ()

Illustration of column scanning for generating linear indices Shown are several

2-D coordinates (in parentheses) and their corresponding linear indices.

➢ Spatial resolution is a measure of the

smallest discernible detail in an image.

➢ Dots per unit distance is a measure of

image resolution used in the printing and

publishing industry In the U.S., this

measure usually is expressed as dots per

inch (dpi)

➢ Intensity resolution is the number of bits

used to quantize intensity.

Effects of reducing spatial resolution The images shown are at: (a) 930 dpi, (b) 300 dpi, (c) 150 dpi, and (d) 72 dpi.

(a) 256-level image (b)-(d) Image displayed in 128, 64, and 32 intensity levels, while keeping the image size constant (e)-(h) Image displayed in 16, 8, 4, and 2 intensity levels.

(a) Image with a low level of detail (b) Image with a medium level of detail (c) Image with a relatively large amount of detail.

➢ Observe that isopreference curves tend

to become more vertical as the detail in

the image increases

➢ This result suggests that for images with

a large amount of detail only a few

intensity levels may be needed.

Representative isopreferencecurves for the three types of images

➢ Interpolation is the process of using known data to estimate values at unknown

locations

➢ Nearest neighbor interpolation

➢ Bilinear interpolation

➢ Bicubic interpolation

(a) Image reduced to 72 dpi and zoomed back to its original 930 dpi using nearest neighbor interpolation (b) Image reduced to 72 dpi and zoomed using bilinear interpolation (c) Same as (b) but using bicubic interpolation.

1 Image Sensing and Acquisition

2 Image Sampling and Quantization

3 Some Basic Relationships Between Pixels

4 Basic Mathematical Tools Used in Digital Image Processing

➢ A pixel p at coordinates (x, y)

➢ N4(p): 4-neighbors of p

➢ ND(p): 4 diagonal neighbors of p

➢ N8(p): N4(p) together with ND(p)

➢ Let V be the set of intensity values used to define adjacency.

➢ V = {1}

(a) An arrangement of pixels (b) Pixels that are 8-adjacent (adjacency is shown by dashed lines) (c)

m-adjacency (d) Two regions (of 1’s) that are 8-adjacent (e) The circled point is on the boundary of the 1-valued pixels only if 8-adjacency between the region and background is used (f) The inner boundary of the 1-valued

region does not form a closed path, but its outer boundary does

➢ A digital path (or curve) from pixel p with coordinates (x0, y0) to pixel q with

coordinates (xn, yn) is a sequence of distinct pixels with coordinates:

➢ If (x0, y0) = (xn, yn) the path is a closed path

n is the length of the path

Let S represent a subset of pixels in an image

➢ Two pixels p and q are said to be connected in S if there exists a path between

them consisting entirely of pixels in S.

➢ The set of pixels that are connected to p in S is called a connected component of

S.

➢ If it only has one component, and that component is connected, then S is called a

connected set.

Let R represent a subset of pixels in an image

➢ We call R a region of the image if R is a connected set.

➢ Two regions, Ri and Rj are said to be adjacent if their union forms a connected

set.

➢ Regions that are not adjacent are said to be disjoint.

➢ Foreground: the union of all the K disjoint regions (Ru)

➢ Background: the complement of the set Ru: (Ru)c

foreground

➢ The boundary (also called the border or contour) of a region R is the set of pixels

in R that are adjacent to pixels in the complement of R.

intensity discontinuities closed paths

➢ For pixels p, q, and s, with coordinates (x, y), (u, v), and (w, z), respectively, D is a

distance function or metric if:

➢ The Euclidean distance between p and q is defined as:

The distance D4, (city-block distance) The distance D 8 , (chessboard distance)

1 Image Sensing and Acquisition

2 Image Sampling and Quantization

3 Some Basic Relationships Between Pixels

4 Basic Mathematical Tools Used in Digital Image Processing

➢ An elementwise operation involving one or more images is carried out on a

pixel-by-pixel basis.

➢ The elementwise product of these two images is

➢ The matrix product of the images

➢ Consider a general operator, H, that produces an output image, g(x, y), from a

given input image, f(x, y):

➢ H is said to be a linear operator if

➢ An operator that fails to satisfy above equation is said to be nonlinear.

➢ Arithmetic operations between two images f(x, y) and g(x, y)

(a) Sample noisy image of the

Sombrero Galaxy

(b)-(f) Result of averaging 10, 50,

100, 500, and 1,000 noisy images,

respectively All images are of size pixels, and all were scaled so that their intensities would span the full [0, 255] intensity scale

(a) Infrared image of the Washington, D.C area (b) Image resulting from setting to zero the least significant

bit of every pixel in (a) (c) Difference of the two images, scaled to the range [0, 255] for clarity

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.

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

(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)

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)

(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)

(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.

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

Forming a vector from corresponding pixel values in three RGB component images.

➢ 2-D linear transforms

➢ Inverse transform

General approach for working in the linear transform domain.

(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.