Image processing

Vision system(image processing)

By: karim ahmed abuamu

Image Representation

A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels

The image is stored in computer memory as 2D array of integers

Digital images can be created by a variety of input devices and techniques:

digital cameras, scanners, coordinate measuring machines etc.

Representation of Digital Images

Types of Images

Digital images can be classified according to number and nature of those samplesBinaryGrayscaleColor

Binary Images

A binary image is a digital image that has only two possible values for each pixel

Binary images are also called bi-level or two-level

Binary images often arise in digital image processing as masks or as the result of certain operations such as segmentation, thresholding.

Grayscale Image Binary Image

Grayscale Images

A grayscale digital image is an image in which the value of each pixel is a single sample.

Displayed images of this sort are typically composed of shades of gray, varying from black at the weakest intensity to white at the strongest.

The values of intensity image ranges from 0 to 255.

Grayscale Image

True color images

A true color image is stored as an m-by-n-by-3 data array that defines red, green, and blue color components for each individual pixel.

The RGB color space is commonly used in computer displays

True color Image

Image segmentation

In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels).

The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

Histograms

o A tool that is used often in image analysis .o The (intensity or brightness) histogram shows how

many times a particular grey level appears in an image.o For example, 0 - black, 255 – white.

0 1 1 2 4

2 1 0 0 2

5 2 0 0 4

1 1 2 4 1 0

1

2

3

4

5

6

7

0 1 2 3 4 5 6

image histogram

Histogram Features

An image has low contrast when the complete range of possible values is not used.  Inspection of the histogram shows this lack of contrast.

Histogram Equalization

Thresholding (image processing)

Threshold converts each pixel into black, white or unchanged depending on whether the original color value is within the threshold range.

Thresholding is usually the first step in any segmentation

Single value thresholding can be given mathematically as follows:

Tyxfif

Tyxfifyxg

),( 0

),( 1),(

Imagine a poker playing robot that needs to visually interpret the cards in its hand

Original Image Thresholded Image

If you get the threshold wrong the results can be disastrous

Threshold Too Low Threshold Too High

Basic Global Thresholding

Based on the histogram of an image Partition the image histogram using a single global threshold

The success of this technique very strongly depends on how well the histogram can be partitioned

The basic global threshold, T, is calculated as follows:

1. Select an initial estimate for T (typically the average grey level in the image)

2. Segment the image using T to produce two groups of pixels: G1 consisting of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T

3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2

4. Compute a new threshold value:

5. Repeat steps 2 – 4 until the difference in T in successive iterations is less than a predefined limit T∞

This algorithm works very well for finding thresholds when the histogram is suitable

221

T

Thresholding Example 1

Thresholding Example 2

Problems With Single Value Thresholding

Single value thresholding only works for bimodal histogramsImages with other kinds of histograms need more than a single threshold

Single value thresholding only works for bimodal histogramsImages with other kinds of histograms need more than a single threshold

Thank You