Image Processing Weeks 14 & 15

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Questions and Answers

What is the primary goal of image segmentation?

To convert RGB values to grayscale

To enhance the color quality of an image

To compress the image size without quality loss

To partition a digital image into multiple segments for easier analysis (correct)

Which of the following is NOT a type of image segmentation?

Edge-Based Segmentation

Cluster-Based Segmentation

Threshold-Based Segmentation

Contrast-Based Segmentation (correct)

Intensity slicing is particularly useful when:

Exact gray level values are known (correct)

The image needs to be transformed to grayscale

The segmentation needs to be performed on edges

The image requires color enhancement

Which segmentation technique would most likely rely on the pixel intensity values across an image?

Threshold-Based Segmentation Signup and view all the answers

In pseudo-color image processing, which aspect is generally enhanced?

The intensity and color attributes of pixels Signup and view all the answers

What is the primary purpose of using the first derivative in image segmentation?

To perform edge detection Signup and view all the answers

Which of the following statements about central difference is true?

It combines forward and backward differences. Signup and view all the answers

Which type of edge belongs to the category of ramp-edge?

A gradual change in pixel intensity Signup and view all the answers

What does the term 'Laplacian' refer to in the context of point detection?

An operator used to identify regions of rapid intensity change Signup and view all the answers

What is the outcome of applying a backward difference method in image processing?

It focuses on the past pixel's intensity compared to the current pixel. Signup and view all the answers

What does 'saturation' refer to in the context of color?

The intensity and vibrancy of color Signup and view all the answers

Which of the following best describes the term 'lightness' in color?

The degree of brightness of color Signup and view all the answers

When referring to color properties, what does 'hue' signify?

The color properties such as red, blue, and green Signup and view all the answers

Why are images often converted from the RGB model to the HSI model for processing?

RGB model cannot accommodate complex color manipulations Signup and view all the answers

In terms of color models, what is the difference between 'achromatic' and 'chromatic' colors?

Chromatic colors contain hue while achromatic colors do not Signup and view all the answers

What is one of the key reasons for using color in image processing?

Color enhances the human ability to distinguish details. Signup and view all the answers

Which color model is commonly utilized in image processing?

RGB Model Signup and view all the answers

What is a significant aspect of the human eye regarding color perception?

The human eye can distinguish thousands of color shades. Signup and view all the answers

Which mathematical method is NOT directly associated with image processing techniques mentioned?

Quantum Mechanics Signup and view all the answers

What is the weight of the final project presentations in the course?

6% Signup and view all the answers

What is one application of color image processing mentioned?

Facilitating color recognition by machines Signup and view all the answers

What is an encouraging aspect mentioned related to the upcoming programming activity?

Students will program for color image processing. Signup and view all the answers

What type of representation is highlighted in the context of image processing?

Both numeric and analytic representation Signup and view all the answers

Study Notes

Image Processing - Week 14&15

The topics covered in week 14 and 15 include color image processing and other related subjects.
Final project presentations are scheduled for week 16.
The presentation will be worth 6% of the total grade.
The template for the presentation is available in the appendices.
The presentation will consist of 10 minutes of presentation and 5 minutes for questions.

Color Fundamentals

The human eye perceives thousands of color shades and intensities compared to the fewer shades of gray.
The visible spectrum of light ranges from Gamma rays to Radio waves.

Color Models

Color models are mathematical systems to represent color. The CIE 1931 Standard (International Commission on Illumination) is a common color model.
This model uses wavelengths in nanometers.
Red = 700nm
Green = 546.1nm
Blue = 435.8nm

Color Models and Notes

The primary colors of light (RGB) are additive.
The subtractive color model (CMY) is used for printing inks.
RGB color model is used in displays like TVs and computer monitors.
CMYK is used in print media like printers.

Color Maturity

Color maturity has three elements: hue, lightness, and saturation.
Hue refers to color properties (e.g., red, blue, green).
Lightness describes the brightness of a color.
Saturation describes the vividness of a color; high values are for bright and strong colors.

HSI Model

The HSI color model is based on human perception of color.
Hue is a subjective measure of color. Humans perceive roughly 200 different colors.
Saturation is the relative purity of a color. Adding white to a color reduces saturation.
Intensity refers to the brightness or darkness of an object.

Converting Between RGB and HSI

Converting from RGB to HSI allows for better image manipulation of color.
The intensity (I) of a color in the RGB model is calculated as (R+G+B)/3.
Saturation (S) is calculated using this formula: S = 1 - min(R,G,B)/I
Chroma is defined as max(R,G,B)-min(R,G,B).
Hue (H) is determined based on the maximum color channel.

Pseudo-color Image Processing

Pseudo-color is also known as false color.
It assigns colors to gray-level values based on criteria (e.g., intensity slicing).
This technique is used to improve human visual interpretation of grayscale images.
Intensity slicing and gray levels to color transformations are used.

Image Segmentation

Image segmentation partitions a digital image into segments (sets of pixels) to simplify analysis.
This technique makes images easier to interpret and analyze.
Common applications include autonomous vehicles and agriculture.
Edge-Based Segmentation, Threshold-Based Segmentation, Region-Based Segmentation, Cluster-Based Segmentation, and Watershed Segmentation are different types.
Intensity changes are useful for detecting edges in images.

Edge Detection

Edge detection is a method used in image segmentation, based on changes in local intensities within an image.
Techniques employ first and second derivative results.
Some of the issues involved include the presence of noise which can create erroneous results. Filtering is often done before edge detection to mitigate the noise.
Three typical edge types are: Step Edge, Ramp Edge, Roof Edge

Point and Line Detection

Point and line detection techniques are methods that use image derivatives to find specific points and lines in an image.

Colorization

Colorization transforms grayscale images to color images using algorithms based on intensity and texture analysis.

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Description

This quiz covers the essential topics in image processing during weeks 14 and 15, focusing on color image processing techniques and color models. Explore how the human eye perceives color and the mathematical frameworks used to represent these colors. Prepare for upcoming final project presentations scheduled for week 16!