COMP9517 Computer Vision Week 3 Quiz

Questions and Answers

What are image features primarily used for in computer vision?

To modify colors in images.

To create larger images.

To represent important information in an image. (correct)

To reduce the file size of images.

Which of the following is NOT a major category of image features?

Texture features

Colour features

Size features (correct)

Shape features

Which feature descriptor is known for its use in texture analysis?

Scale-invariant feature transform

Local binary patterns

Harris corners

Haralick features (correct)

What is an example of an image feature?

Edges Signup and view all the answers

In computer vision applications, image matching and stitching rely on which aspect?

Feature representation Signup and view all the answers

What makes feature representation crucial in computer vision?

It ensures robustness, descriptiveness, and efficiency. Signup and view all the answers

What is the first step in the RANSAC algorithm for model fitting?

Sample the number of points required Signup and view all the answers

In RANSAC, what is done after sampling the required number of points?

Estimate the model parameters using the samples Signup and view all the answers

What does scoring in the context of RANSAC refer to?

Finding the fraction of inliers within a preset threshold Signup and view all the answers

What is the goal of repeating the sampling, solving, and scoring steps in RANSAC?

To find the best model with high confidence Signup and view all the answers

Which of the following is NOT part of the RANSAC algorithm process?

Determine the best fit line equation Signup and view all the answers

How does RANSAC ensure robustness in model fitting?

By focusing on inliers using random sampling Signup and view all the answers

What is the significance of the preset threshold in RANSAC?

It defines the criteria for scoring inliers Signup and view all the answers

What outcome does RANSAC aim to achieve through its iterative approach?

Finding a model that adequately represents the majority of data Signup and view all the answers

Which of the following is a commonly used image color feature?

Color moments Signup and view all the answers

What is the primary focus of shape features mentioned in the content?

Form and structure Signup and view all the answers

Which technique is used for matching image descriptors?

Descriptor matching Signup and view all the answers

Which of the following is not a texture feature according to the content?

HOG Signup and view all the answers

Which of the following methods is discussed for solving computer vision problems?

Bag-of-Words Signup and view all the answers

What role does RANSAC play in the context of feature representation?

Parameter scoring Signup and view all the answers

Which transformation is specifically related to spatial transformations in feature representation?

Image Rotation Signup and view all the answers

Which of the following techniques is aimed at clustering features within images?

K-means clustering Signup and view all the answers

What technique is used for refining keypoints in SIFT keypoint localization?

3D quadratic fitting in scale-space Signup and view all the answers

How are the orientations assigned to SIFT keypoints?

Making an orientation histogram of local gradient vectors Signup and view all the answers

What dimensionality does each SIFT keypoint descriptor have?

128D Signup and view all the answers

What is the purpose of using the nearest neighbour distance ratio (NNDR) in descriptor matching?

To reject matches that have a high distance ratio Signup and view all the answers

Which type of transformation is not classified as a rigid transformation?

Scaling Signup and view all the answers

Why is RANSAC used in fitting and alignment processes?

It provides outlier detection and rejection. Signup and view all the answers

What is the role of Hessian analysis in SIFT keypoint localization?

To reject low-contrast and edge points Signup and view all the answers

What is the purpose of creating additional keypoints from the second highest peak in the orientation histogram?

If the peak is greater than 80% Signup and view all the answers

Which transformation type allows for changing both shape and size of the object?

Nonrigid transformation Signup and view all the answers

What mathematical approach is used for least-squares fitting in alignment of keypoints?

Minimizing the squared error Signup and view all the answers

What method is used to find the best model with high confidence in the fitting and alignment process?

Random sample consensus Signup and view all the answers

In the context of transformation estimation, what does the equation 𝐵 = 𝐴 + 𝑡 represent?

Position transformation using translation vector 𝑡. Signup and view all the answers

Which mathematical operation is performed to solve for parameters in the least squares alignment method?

Matrix inversion Signup and view all the answers

What does the system of equations 𝐀𝐀𝐀𝐀 = 𝐛𝐛 represent in alignment by least squares?

The relationship between the input metrics and the desired output. Signup and view all the answers

What is the first step in the random sample consensus method for estimating transformation?

Select a random subset of matched points. Signup and view all the answers

Why is it essential to repeat the fitting process until the best model is found?

To find the model with the highest fraction of inliers. Signup and view all the answers

Which term describes the adjustment of mathematical models to minimize the difference between estimated and actual positions?

Least squares method Signup and view all the answers

What role do matched points 𝐴𝐴 and 𝐵𝐵 serve in the fitting and alignment process?

They form the basis for estimating the transformation. Signup and view all the answers

Study Notes

Feature Representation Overview

Feature representation is crucial for computer vision, enhancing robustness, descriptiveness, and efficiency.
Major image feature categories include colour, texture, and shape features.

Image Features

Image features are vectors representing significant information in images, examples include blobs, edges, corners, ridges, circles, ellipses, and lines.

Prominent Feature Descriptors

Haralick Features: Derived from gray-level co-occurrence matrices, useful for texture analysis.
Local Binary Patterns (LBP): Effective for texture classification due to its simplicity and efficiency.
Scale-Invariant Feature Transform (SIFT): Designed to detect keypoints that remain stable across various image scales and rotations.

SIFT Keypoint Localization

Keypoints are improved through 3D quadratic fitting in scale-space.
Low-contrast and edge points are rejected using Hessian analysis to refine keypoint selection.

SIFT Orientation Assignment

Keypoint orientations determined by local gradient vectors.
Orientation histograms are utilized to find dominant orientations and additional keypoints for significant peaks.

SIFT Keypoint Descriptor

Each keypoint represented by a 128-dimensional feature vector, structured as a 4x4 array of gradient histograms, facilitating detailed analysis.

Applications of SIFT

Image matching: Computes SIFT keypoints for overlapping images to establish correspondences.
Image stitching: Similar processes used along with spatial transformations to combine images seamlessly.

Spatial Transformation Types

Rigid Transformations: Include translation and rotation, maintaining the object's shape.
Non-Rigid Transformations: Such as scaling, affine, and perspective transformations that alter the shape.

Fitting and Alignment Techniques

Least Squares (LS) Fitting: Used to estimate transformation parameters by minimizing squared errors in matching points.
RANSAC (Random Sample Consensus): An iterative method to detect and reject outliers in fitting models.

RANSAC Process

Random samples taken to fit a model.
Parameters are solved for, then scored based on the inlier fraction within a threshold, iteratively refined until an optimal model emerges.

Summary of Computer Vision Techniques

Feature representation is key for resolving various computer vision challenges with techniques like:
- Colour features (e.g., moments, histogram)
- Texture features (e.g., Haralick, LBP, SIFT)
- Shape features (to be explored further).

Upcoming Topics

Future discussions will cover feature encoding techniques like Bag-of-Words, K-means clustering, shape matching, and sliding window detection.

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Description

This quiz focuses on the essential aspects of feature representation in computer vision. Students will explore the necessity of feature representation, major categories of image features, and understand various prominent feature descriptors. Prepare to demonstrate your knowledge of colour, texture, and shape features, as well as methods like Haralick features and local binary patterns.