Computer Vision: A Comprehensive Guide for Beginners

Question 1

In computer vision, image noise can hinder accurate analysis. Which image preprocessing technique is specifically used to reduce image noise?

Accepted Answer

Median filtering

Answer

Histogram equalization

Answer

Thresholding

Answer

Dithering

Question 2

Which step is NOT essential in image registration?

Accepted Answer

Image enhancement

Answer

Image warping

Answer

Feature matching

Answer

Homography estimation

Question 3

Which of the following is **not** a necessary step in image registration for aligning images?

Accepted Answer

Brightness adjustment

Answer

Image warping

Answer

Feature extraction

Answer

Image transformation

Question 4

Which computer vision process definitively identifies an object's unique identity?

Accepted Answer

Object classification

Answer

Image segmentation

Answer

Feature extraction

Answer

Image registration

Question 5

In computer vision, which techniques focus on identifying and describing unique patterns within images?

Accepted Answer

Feature Detection and Description

Answer

Image Preprocessing and Color Correction

Answer

Image Alignment and Registration

Answer

Object Recognition and Classification

Question 6

In computer vision, which mathematical model describes how 3D world coordinates transform into 2D image coordinates through a camera's lens?

Accepted Answer

Camera Calibration Matrix

Answer

Homography Matrix

Answer

Stereo Disparity Map

Answer

Vanishing Point

Question 7

Within a computer vision pipeline, which step is typically NOT considered a core component?

Accepted Answer

Data Labeling

Answer

Image Acquisition

Answer

Image Preprocessing

Answer

Feature Extraction

Question 8

In a computer vision pipeline, the initial step is capturing the image data. Which of the following options best describes this step?

Accepted Answer

Image Acquisition

Answer

Image Preprocessing

Answer

Feature Extraction

Answer

Object Recognition

Question 9

Which of the following is NOT a significant challenge in computer vision?

Accepted Answer

Overcoming human biases in computer vision algorithms

Answer

Managing image noise

Answer

Dealing with varying illumination conditions

Answer

Addressing object occlusion and visibility issues

Question 10

In computer vision, which technique is commonly used for feature detection?

Accepted Answer

Edge detection

Answer

Region growing

Answer

Histogram equalization

Answer

Clustering

Question 11

Which technique is not commonly used for image segmentation?

Accepted Answer

Principal component analysis

Answer

K-means clustering

Answer

Thresholding

Answer

GrabCut

Question 12

Which of the following is **not** a direct subfield of computer vision?

Accepted Answer

Geographic Information Systems (GIS)

Answer

Camera Calibration

Answer

Image Restoration

Answer

Object Tracking

Question 13

Which of these techniques is NOT used in image segmentation?

Accepted Answer

Object recognition

Answer

Thresholding

Answer

Clustering

Answer

Edge detection

Question 14

In computer vision, which technique is used to divide an image into separate regions based on similarities between pixels?

Accepted Answer

Clustering

Answer

Histogram equalization

Answer

Edge detection

Answer

Median filtering

Question 15

Which step is not a fundamental component of a computer vision pipeline?

Accepted Answer

Data Summarization

Answer

Image Acquisition and Preprocessing

Answer

Feature Extraction and Analysis

Answer

Object or Scene Recognition

Question 16

In image segmentation, which step involves grouping neighboring pixels with similar characteristics into separate regions?

Accepted Answer

Region growing

Answer

Image filtering

Answer

Edge detection

Question 17

Which of the following is a boundary-based image segmentation method?

Accepted Answer

Edge detection

Answer

Region growing

Answer

Thresholding

Answer

Clustering

Question 18

In the typical sequence of steps used in computer vision, which stage involves dividing an image into separate regions or segments to assist in identifying objects and understanding the scene?

Accepted Answer

Image segmentation

Answer

Image acquisition

Answer

Feature extraction

Answer

Object classification

Question 19

In Structure-from-Motion (SfM), which technique utilizes geometric relationships between 2D images to infer 3D structural information?

Accepted Answer

Epipolar Geometry

Answer

Kalman Filtering

Answer

Principal Component Analysis (PCA)

Answer

Clustering

Question 20

Which topic is not part of the Computer Vision unit?

Accepted Answer

Web Programming

Answer

Image Processing

Answer

Object Detection

Answer

Feature Extraction

Question 21

In computer vision, which of these is a common use of Machine Learning?

Accepted Answer

Image classification

Answer

Image compression

Answer

Camera calibration

Question 22

In computer vision, which technique is commonly used to estimate the motion of objects in a video sequence for object tracking?

Accepted Answer

Kanade-Lucas-Tomasi (KLT) tracking

Answer

Harris corner detection

Answer

Mean-shift algorithm

Answer

Random sample consensus (RANSAC)

Question 23

Which approach is not commonly used for feature matching in object recognition tasks within computer vision?

Accepted Answer

Bag-of-Visual-Words (BoVW)

Answer

Scale-Invariant Feature Transform (SIFT)

Answer

Speeded-Up Robust Features (SURF)

Question 24

In computer vision, which technique is commonly employed to detect and track moving objects within a video sequence?

Accepted Answer

Optical Flow

Answer

Edge Detection

Answer

Histogram Equalization

Answer

Clustering

Question 25

In computer vision, which technique divides an image into sections with comparable pixel values or characteristics?

Accepted Answer

Segmentation

Answer

Preprocessing

Answer

Registration

Question 26

Within the realm of computer vision, which technique is primarily employed to align multiple images or an image sequence?

Accepted Answer

Image Registration

Answer

Image Segmentation

Answer

Feature Detection

Answer

Optical Flow

Question 27

In computer vision, which techniques are commonly used to segment objects based on their motion?

Accepted Answer

Background Subtraction

Accepted Answer

Optical Flow

Answer

Edge Detection

Answer

Image Registration

Question 28

In computer vision, feature points play a crucial role. What is the primary advantage of using feature points in image analysis?

Accepted Answer

Feature points simplify image complexity, enabling efficient processing.

Answer

Feature points aid in calculating the camera's exact depth.

Answer

Feature points help predict human emotions based on facial expressions.

Question 29

Which image preprocessing technique focuses on removing noise while retaining important image features?

Accepted Answer

Image Denoising

Answer

Image Enhancement

Answer

Image Segmentation

Answer

Edge Detection

Question 30

Which of the following is NOT a core subtopic of computer vision?

Accepted Answer

Computer-Aided Manufacturing

Answer

Image Segmentation

Answer

3D Reconstruction

Answer

Motion Analysis

Question 31

In object recognition, which step is crucial for distinguishing between objects by identifying and analyzing their unique characteristics?

Accepted Answer

Feature Extraction

Answer

Image Acquisition

Answer

Object Classification

Answer

Image Segmentation

Question 32

In computer vision, which approach is NOT commonly used for image segmentation?

Accepted Answer

Template matching

Answer

Region-based segmentation

Answer

Boundary-based segmentation

Question 33

In computer vision, homography is a transformation model that maps points between two images. Which of the following is a key assumption when using homography?

Accepted Answer

At least four corresponding points must be identified in both images.

Answer

The images must be captured with the same camera settings.

Answer

The images must be the same size and resolution.

Answer

The images must be taken from the same viewpoint.

Question 34

In a computer vision system, which component is essential for extracting meaningful information from images?

Accepted Answer

Image Processing

Answer

Natural Language Processing

Answer

Database Management

Question 35

In computer vision, which techniques are commonly used to encode the visual characteristics of a feature point, enabling its effective matching and retrieval?

Accepted Answer

Histogram of Oriented Gradients (HoG)

Accepted Answer

Scale-Invariant Feature Transform (SIFT)

Answer

Mean Shift

Question 36

What is the first step in a computer vision pipeline?

Accepted Answer

Image acquisition

Answer

Image preprocessing

Answer

Object detection

Question 37

In computer vision, what is the primary function of image alignment techniques?

Accepted Answer

Correct distortions from camera motion or object movement, enabling further analysis like image stitching or 3D reconstruction.

Answer

Enhance image quality by removing noise or adjusting contrast.

Answer

Identify and extract specific objects or features within an image.

Answer

Convert images into a format compatible with specific software or algorithms.

Question 38

In computer vision, which technique focuses on identifying and extracting distinctive image features, representing them for matching and classification?

Accepted Answer

Feature Extraction

Answer

Image Enhancement

Answer

Object Recognition

Question 39

How do varying lighting conditions affect computer vision algorithms? Describe techniques used to handle illumination changes and enhance image data for improved performance.

Accepted Answer

Techniques for handling illumination changes include histogram equalization, white balancing, and adaptive thresholding.

Answer

Lighting conditions have no impact on the quality and accuracy of computer vision algorithms.

Answer

Computer vision algorithms can automatically handle illumination changes without additional techniques.

Answer

Image enhancement techniques like noise reduction and sharpening are not helpful for improving computer vision algorithm performance.

Question 40

In computer vision, metric learning techniques aim to achieve what primary goal? How do these techniques contribute to tasks like face recognition and object tracking?

Accepted Answer

Learn distance metrics that effectively measure similarity between image features, aiding in tasks like face recognition and object tracking.

Answer

Reduce the dimensionality of image data for more efficient processing and analysis.

Answer

Enhance the accuracy of deep learning models used in computer vision tasks.

Answer

Generate synthetic images for training computer vision models.

Question 41

In computer vision, what is the key difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning uses labeled data, while unsupervised learning does not.

Answer

Supervised learning is used for classification, while unsupervised learning is used for clustering.

Answer

Supervised learning approaches always outperform unsupervised learning approaches in terms of accuracy.

Answer

Unsupervised learning algorithms require more computational resources and training time than supervised learning algorithms.

Question 42

In computer vision, what is scene understanding and why is it crucial for autonomous systems like self-driving cars and robotic navigation?

Accepted Answer

Scene understanding involves interpreting images or videos to identify objects, their relationships, and the overall context.

Answer

Scene understanding is not essential for autonomous systems as they rely solely on sensor data.

Answer

Scene understanding is only relevant to human perception and has no significance in computer vision.

Question 43

In image segmentation, which step is essential for grouping pixels into meaningful regions?

Accepted Answer

Clustering

Answer

Image acquisition

Answer

Feature extraction

Answer

Object recognition

Question 44

Which of the following is NOT a fundamental step in the image processing stage of computer vision?

Accepted Answer

Object recognition

Answer

Noise reduction

Answer

Image enhancement

Answer

Image segmentation

Question 45

In computer vision, which advanced technique is specifically designed to create realistic images or enhance low-resolution images to high resolution?

Accepted Answer

Generative Adversarial Networks (GANs)

Answer

Convolutional Neural Networks (CNNs)

Answer

Support Vector Machines (SVMs)

Answer

Random Forests

Question 46

In computer vision, what is the main purpose of image segmentation?

Accepted Answer

Dividing an image into meaningful regions for object recognition and scene interpretation

Answer

Reducing noise and enhancing image quality

Answer

Extracting features and descriptors for object recognition and classification

Answer

Generating a depth map from a 2D image for 3D scene reconstruction

Question 47

In object detection, how do Convolutional Neural Networks (CNNs) contribute to the process? Describe their role in feature extraction and classification.

Accepted Answer

CNNs extract hierarchical features and classify objects through a layered architecture designed for object detection.

Answer

CNNs primarily extract low-level features and do not participate in object classification.

Answer

For object detection, CNNs classify objects using only globally extracted image features.

Answer

CNNs are rarely used for object detection due to their inability to handle complex object variations.

Question 48

In computer vision, how does image noise impact the performance of feature extraction algorithms?

Accepted Answer

Image noise can hinder feature extraction by creating false or misleading features.

Answer

Image noise enhances feature extraction by introducing additional distinctive features.

Answer

Image noise has a negligible effect on the accuracy of feature extraction algorithms.

Answer

Image noise improves feature extraction by making features more distinct and easier to identify.

Question 49

Identify the primary distinction between region-based and contour-based image segmentation techniques.

Accepted Answer

Region-based techniques focus on identifying areas with similar properties, while contour-based techniques prioritize detecting object boundaries.

Answer

Region-based techniques are more accurate, while contour-based techniques are faster.

Answer

Region-based techniques are used for object segmentation, while contour-based techniques are used for image segmentation.

Question 50

Which of the following is NOT a fundamental step in computer vision?

Accepted Answer

Data storage

Answer

Image acquisition

Answer

Feature extraction

Answer

Object recognition

Question 51

What is the primary goal of image processing in computer vision?

Accepted Answer

To extract meaningful information

Answer

To improve visual quality

Answer

To remove noise and artifacts

Answer

To reduce image size for efficient storage

Question 52

In computer vision, which essential step involves extracting meaningful data from images?

Accepted Answer

Feature Extraction

Answer

Image Acquisition

Answer

Image Processing

Answer

Object Recognition

Question 53

What is the primary purpose of employing a Hough transform in computer vision?

Accepted Answer

Detecting Lines and Circles in Images

Answer

Segmenting Images into Distinct Regions

Answer

Enhancing Image Contrast

Answer

Extracting Keypoints

Question 54

In computer vision, what principle is associated with vanishing points?

Accepted Answer

Parallel Lines in a Scene Appear to Converge at a Point in the Image

Answer

Objects Farther Away Appear Smaller in the Image

Answer

Colors Appear More Saturated in the Center of the Image

Answer

The Image is Always in Focus

Question 55

Which of the following tasks is not considered fundamental in the field of computer vision?

Accepted Answer

Audio processing

Answer

Image processing

Answer

Feature extraction

Answer

Object recognition

Question 56

What is the primary purpose of feature extraction in computer vision?

Accepted Answer

To identify unique characteristics of objects

Answer

To reduce the dimensionality of an image

Answer

To enhance image contrast

Answer

To perform object detection

Question 57

Which machine learning algorithm is commonly employed for object recognition tasks?

Accepted Answer

Support vector machines

Answer

K-nearest neighbors

Answer

Decision trees

Answer

Linear regression

Question 58

Which type of neural network architecture is widely used in computer vision applications?

Accepted Answer

Convolutional neural network (CNN)

Answer

Recurrent neural network (RNN)

Answer

Long short-term memory (LSTM)

Answer

Generative adversarial network (GAN)

Question 59

What is the main limitation of traditional image processing techniques compared to deep learning approaches?

Accepted Answer

Reliance on manually designed features

Answer

Inability to handle complex images

Answer

Lower accuracy in object recognition

Answer

Computational inefficiency

Question 60

Which image processing technique is used to enhance the contrast of an image, making the light areas appear lighter and the dark areas appear darker?

Accepted Answer

Histogram equalization

Answer

Edge detection

Answer

Color space conversion

Answer

Gaussian filtering

Question 61

Which feature extraction technique is commonly used to detect corners in an image, where two edges or lines intersect?

Accepted Answer

Harris corner detector

Answer

Canny edge detector

Answer

SIFT descriptor

Answer

SURF descriptor

Question 62

What is the purpose of the Canny edge detector?

Accepted Answer

To detect edges in an image by applying a derivative filter, highlighting the boundaries of objects.

Answer

To remove noise from an image by applying a Gaussian filter, smoothing out the image while preserving edges.

Answer

To enhance the contrast of an image by applying histogram equalization, making the light areas appear lighter and the dark areas appear darker.

Answer

To convert an RGB image to a grayscale image, reducing the image to shades of gray.

Question 63

Which of the following is NOT a characteristic of the SIFT descriptor?

Accepted Answer

Computational efficiency

Answer

Scale invariance

Answer

Rotation invariance

Answer

Translation invariance

Question 64

Which of the following is NOT a fundamental operation in image processing?

Accepted Answer

Object detection

Answer

Histogram equalization

Answer

Edge detection

Answer

Image segmentation