Lecture 4 - Color and Texture

Question 1

What is a color space?

Answer 1

A color space is a specific organization of colors, allowing for the reproducible representation of color in both digital and physical formats. Examples include RGB, HSV, and CIE.

Question 2

Define color constancy.

Answer 2

Color constancy is the ability to perceive the colors of objects invariant to the color of the light source.

Question 3

What is surface reflectance?

Answer 3

Surface reflectance describes how a surface reflects light, depending on its material properties and surface texture.

Question 4

Explain illumination invariant color features.

Answer 4

Illumination invariant color features are those that remain consistent under varying lighting conditions, allowing for reliable color analysis regardless of light changes.

Question 5

What are Fourier features in texture analysis?

Answer 5

Fourier features analyze the frequency content of an image, useful for identifying periodic patterns and textures.

Question 6

Describe co-occurrence matrices.

Answer 6

Co-occurrence matrices capture the spatial relationships between pixel intensities, used to extract texture features such as contrast, energy, and homogeneity.

Question 7

What are filter banks?

Answer 7

Filter banks are sets of filters, such as Laws, Gabor, and eigenfilters, used to analyze and extract texture features from images.

Question 8

Define a stochastic model in the context of texture analysis.

Answer 8

A stochastic model uses probabilistic methods to describe and analyze textures, accounting for randomness and variability in texture patterns.

Question 9

What is the CIE color space?

Answer 9

The CIE color space is a color space defined by the International Commission on Illumination, providing a standard for color representation and comparison.

Question 10

Describe the concept of chromaticity coordinates.

Answer 10

Chromaticity coordinates represent color without including brightness, used to specify the hue and saturation of a color.

Question 11

Explain the significance of the Bidirectional Reflectance Distribution Function (BRDF).

Answer 11

The BRDF describes how light is reflected at an opaque surface, providing a comprehensive model of surface reflectance properties for different viewing and illumination angles.

Question 12

What are Gabor filters?

Answer 12

Gabor filters are linear filters used for texture analysis, which capture both spatial and frequency information, making them useful for edge detection and texture analysis.

Question 13

Write the formula for the convolution of an image I(x, y) with a filter h(x, y) .

Answer 13

Question 14

Provide the formula for the Fourier Transform of an image.

Answer 14

Question 15

What is the formula for calculating the co-occurrence matrix?

Answer 15

Question 16

Write the formula for the Gabor filter.

Answer 16

Question 17

How does the RGB color model represent colors?

Answer 17

he RGB color model represents colors as combinations of red, green, and blue light intensities, with each color being a vector in a 3D color space.

Question 18

What is the purpose of using a histogram in color analysis?

Answer 18

Histograms represent the distribution of pixel intensities in an image, useful for contrast adjustment and image segmentation.

Question 19

Describe the role of the Laws texture energy measures.

Answer 19

Laws texture energy measures use filter masks to analyze and quantify texture patterns in an image, based on the convolution of the image with different masks.

Question 20

Explain the concept of color histogram equalization.

Answer 20

olor histogram equalization adjusts the intensity values of an image to enhance contrast, making the distribution of colors more uniform across the available range.

Question 21

What are eigenfilters and how are they used in texture analysis?

Answer 21

Eigenfilters are derived from the principal component analysis (PCA) of texture samples, used to capture the most significant texture patterns for classification and segmentation.

Question 22

How does the HSV color model differ from the RGB model?

Answer 22

The HSV (Hue, Saturation, Value) color model represents colors based on their shade (hue), intensity (saturation), and brightness (value), making it more intuitive for human perception.

Question 23

What is the significance of the Nyquist theorem in image sampling?

Answer 23

The Nyquist theorem states that to avoid aliasing, the sampling rate must be at least twice the highest frequency present in the image, ensuring accurate representation of the image details.

Question 24

Explain how Fourier features can be used for texture segmentation.

Answer 24

Fourier features analyze the frequency content of an image, allowing for the identification and segmentation of regions with distinct texture patterns based on their spectral characteristics

Question 25

What is the impact of quantization on image quality?

Answer 25

Quantization reduces the number of intensity levels in an image, potentially leading to banding artifacts and loss of subtle details, especially in areas with smooth gradients.

Question 26

Describe the process of using Gabor filters for texture analysis.

Answer 26

Gabor filters are applied to an image to extract features corresponding to different orientations and scales, useful for analyzing and classifying textures based on their spatial frequency content.

Question 27

How does the CIE chromaticity diagram aid in color analysis?

Answer 27

The CIE chromaticity diagram visualizes the relationships between colors in terms of their hue and saturation, helping in the comparison and analysis of color properties.

Question 28

What is the purpose of using filter banks in texture analysis?

Answer 28

Filter banks apply multiple filters to an image to extract a comprehensive set of features, capturing different aspects of texture patterns for robust analysis and classification.