Computer Vision: Master Image and Video Processing Techniques

Question 1

Which technique is NOT applicable to the field of Computer Vision?

Accepted Answer

Predicting stock market trends through linear equations

Answer

Detecting objects within images

Answer

Segmenting images into regions

Answer

Recognizing faces in photographs

Question 2

For categorizing images effectively, which deep learning architecture is commonly leveraged?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Transformer Network

Answer

Recurrent Neural Network (RNN)

Answer

Generative Adversarial Network (GAN)

Question 3

In Computer Vision,supervised and unsupervised learning approaches have fundamental differences. What is the key distinction between them?

Accepted Answer

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

Answer

Unsupervised learning is used for classification, whereas supervised learning is used for regression.

Answer

Supervised learning models are inherently more accurate than unsupervised learning models.

Answer

Unsupervised learning models can handle larger datasets than supervised learning models.

Question 4

Which of the following is NOT a common application of computer vision?

Accepted Answer

Predictive analytics

Answer

Medical imaging

Answer

Self-driving cars

Answer

Video game development

Question 5

Which of the following is not a fundamental task in computer vision?

Accepted Answer

Natural language understanding

Answer

Object detection

Answer

Image classification

Answer

Facial recognition

Question 6

What is the process of identifying and localizing objects within an image known as?

Accepted Answer

Object detection

Answer

Feature extraction

Answer

Image segmentation

Answer

Image classification

Question 7

Which type of neural network is commonly employed for image classification tasks?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Recurrent Neural Network (RNN)

Answer

Generative Adversarial Network (GAN)

Answer

Long Short-Term Memory (LSTM)

Question 8

What is the key distinction between image classification and image segmentation?

Accepted Answer

Image classification assigns a single label to the entire image, while image segmentation labels each pixel.

Answer

Image segmentation assigns a single label to the entire image, while image classification labels each pixel.

Answer

Both image classification and image segmentation label each pixel.

Answer

Neither image classification nor image segmentation labels each pixel.

Question 9

What is the name of the algorithm commonly used to detect edges in images?

Accepted Answer

Canny Edge Detector

Answer

Harris Corner Detector

Answer

SIFT Descriptor

Answer

SURF Detector

Question 10

Which of the following is not a challenge in computer vision?

Accepted Answer

Achieving 100% accuracy

Answer

Variations in illumination

Answer

Occlusions

Answer

Limited training data

Question 11

Which of the following is NOT a common application of computer vision in the medical field?

Accepted Answer

Weather prediction

Answer

Medical image analysis

Answer

Tumor detection

Answer

Surgical planning

Question 12

What is the name of the edge detection algorithm that uses a multi-stage approach involving Gaussian smoothing and gradient calculation?

Accepted Answer

Canny edge detector

Answer

Sobel operator

Answer

Hough transform

Answer

Laplacian operator

Question 13

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

Accepted Answer

Partitioning an image into distinct regions based on their visual properties

Answer

Classifying individual pixels in an image

Answer

Extracting high-level features from an image

Answer

Tracking objects in motion

Question 14

Which type of neural network architecture is commonly used for object detection in computer vision?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Recurrent Neural Network (RNN)

Answer

Generative Adversarial Network (GAN)

Answer

Transformer Neural Network

Question 15

Identify a key challenge associated with image segmentation:

Accepted Answer

Separating objects with similar appearances

Accepted Answer

Segmenting objects with intricate shapes

Accepted Answer

Segmenting objects in scenes with partial occlusions

Answer

Detecting objects in dimly lit conditions

Question 16

What is the main purpose of using Convolutional Neural Networks (CNNs) in object detection?

Accepted Answer

To identify characteristic patterns in images

Answer

To classify each pixel in an image

Answer

To separate foreground from background in images

Answer

To perform facial recognition

Question 17

What is the objective of image segmentation in computer vision?

Accepted Answer

Dividing an image into meaningful parts

Answer

Identifying objects within an image

Answer

Classifying images into different categories

Answer

Tracking objects in a video sequence

Question 18

What is the fundamental difference between supervised and unsupervised learning in computer vision?

Accepted Answer

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

Answer

Unsupervised learning uses labeled data, while supervised learning does not.

Answer

Both supervised and unsupervised learning require labeled data.

Answer

Neither supervised nor unsupervised learning requires labeled data.

Question 19

What is the primary aim of the Canny edge detection algorithm?

Accepted Answer

To detect sudden changes in intensity in an image

Answer

To smooth an image before edge detection

Answer

To segment an image into different regions

Answer

To extract features from an image

Question 20

How have advancements in hardware technology enhanced the capabilities and applications of computer vision systems?

Accepted Answer

Enabled real-time processing of complex computer vision tasks

Answer

Eliminated the need for specialized software in computer vision applications

Answer

Limited the use of computer vision to academic research

Question 21

What is the primary objective of computer vision?

Accepted Answer

To enable the automated analysis and interpretation of visual data

Answer

To create realistic images and videos using artificial intelligence

Answer

To simulate the human visual system for research purposes

Answer

To study the neural mechanisms underlying human vision

Question 22

Which technique in computer vision is specifically designed for detecting objects within images?

Accepted Answer

Object detection

Answer

Image classification

Answer

Facial recognition

Answer

Image segmentation

Question 23

In which domain is facial recognition technology primarily applied?

Accepted Answer

Security and personal identification

Answer

Healthcare and medical diagnosis

Answer

Entertainment and social media

Answer

Scientific research and exploration

Question 24

Which image processing technique involves dividing an image into distinct regions based on shared characteristics?

Accepted Answer

Image segmentation

Answer

Image enhancement

Answer

Image compression

Answer

Image registration

Question 25

What type of neural network architecture is commonly employed in computer vision tasks due to its ability to process spatial data?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Perceptron

Answer

Recurrent Neural Network (RNN)

Answer

Radial Basis Function Network (RBFN)

Question 26

What is the primary purpose of estimating vanishing points in computer vision?

Accepted Answer

To infer the 3D structure of a scene from 2D images

Answer

To determine the position and orientation of the camera

Answer

To measure the depth of objects within a scene

Answer

To classify objects based on their visual features

Question 27

Which of the following challenges in computer vision arises due to varying lighting conditions?

Accepted Answer

Illumination invariance

Answer

Motion blur

Answer

Object occlusion

Answer

Camera noise

Question 28

What is the main objective of using a Hough transform in computer vision?

Accepted Answer

To detect lines, circles, and other geometric shapes within images

Answer

To calculate the optical flow between consecutive frames

Answer

To segment an image into distinct objects

Answer

To recognize handwritten characters and digits

Question 29

In computer vision, what is the main purpose of feature descriptors?

Accepted Answer

Identifying and matching objects by extracting and describing their unique characteristics

Answer

Reducing image size for faster processing

Answer

Enhancing image visibility by adjusting contrast

Answer

Creating synthetic images for model training

Question 30

How do computer vision and computer graphics complement each other? Provide specific examples to illustrate their synergistic applications.

Accepted Answer

Computer vision provides raw data for computer graphics to create realistic images, such as special effects in movies and video games.

Answer

Computer graphics provides algorithms for computer vision to analyze images, like edge detection and object recognition.

Answer

Computer vision and computer graphics are completely separate fields with no overlap.

Answer

Computer graphics provides hardware components for computer vision, like cameras and scanners.

Question 31

Which of the following is not a common computer vision task?

Accepted Answer

Weather forecasting

Answer

Image classification

Answer

Facial recognition

Answer

Object detection

Question 32

What is the term for the process of identifying and locating specific objects in an image?

Accepted Answer

Object detection

Answer

Image segmentation

Answer

Image retrieval

Answer

Image enhancement

Question 33

Which of the following machine learning algorithms is primarily designed for image classification tasks?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Support Vector Machine (SVM)

Answer

Decision Tree

Answer

Linear Regression

Question 34

What is the primary purpose of image segmentation in computer vision?

Accepted Answer

Partitioning an image into distinct regions

Answer

Identifying patterns and relationships within an image

Answer

Compressing an image for efficient storage

Answer

Removing unwanted noise from an image

Question 35

What is the main objective of image enhancement techniques in computer vision?

Accepted Answer

Improving the visual quality of an image

Answer

Extracting specific features from an image

Answer

Reducing the file size of an image

Answer

Generating new images from existing ones

Question 36

Which of the following is a known challenge in computer vision related to variations in lighting conditions?

Accepted Answer

Illumination invariance

Answer

Image noise

Answer

Object occlusion

Answer

Background clutter

Question 37

What is the process of reconstructing the 3D structure of an object from multiple 2D images known as?

Accepted Answer

3D reconstruction

Answer

Image stitching

Answer

Image registration

Answer

Image interpolation

Question 38

Which of the following is an emerging trend that has significantly impacted the field of computer vision?

Accepted Answer

Deep learning

Answer

Model-based approaches

Answer

Thresholding and segmentation

Answer

Rule-based systems

Question 39

Which component is NOT essential for a comprehensive computer vision system?

Accepted Answer

Statistical modeling

Answer

Image processing

Answer

Image acquisition

Answer

Feature extraction

Question 40

Identify the technique specifically employed to detect specific objects within an image.

Accepted Answer

Object detection

Answer

Face recognition

Answer

Motion tracking

Answer

Image classification

Question 41

Which image processing technique is used to enhance the contrast of an image, improving visual clarity?

Accepted Answer

Histogram equalization

Answer

Edge detection

Answer

Noise reduction

Answer

Image segmentation

Question 42

Which machine learning algorithm is frequently utilized for image classification tasks?

Accepted Answer

Convolutional neural network (CNN)

Answer

K-nearest neighbors (KNN)

Answer

Support vector machine (SVM)

Answer

Decision tree

Question 43

What is the primary objective of image segmentation?

Accepted Answer

Partitioning an image into regions with similar characteristics

Answer

Enhancing the contrast of an image

Answer

Detecting objects within an image

Question 44

Choose the application of computer vision that is most relevant to the medical field.

Accepted Answer

Medical image analysis

Answer

Self-driving cars

Answer

Object detection

Answer

Facial recognition

Question 45

Describe the fundamental distinction between supervised and unsupervised learning in computer vision.

Accepted Answer

Supervised learning requires labeled data for training, unlike unsupervised learning.

Answer

Unsupervised learning yields more accurate results compared to supervised learning.

Answer

Supervised learning is used exclusively for object detection, while unsupervised learning is used for image classification.

Question 46

What are the anticipated future directions for the field of computer vision?

Accepted Answer

Advancements in deep learning and applications across various industries

Answer

Computer vision will eventually surpass human vision capabilities.

Answer

Computer vision is a niche field with limited potential for growth.

Question 47

Which of the following is NOT a common approach to object detection?

Accepted Answer

Rule-based systems

Answer

YOLO (You Only Look Once)

Answer

Deformable Part Models (DPMs)

Answer

Convolutional Neural Networks (CNNs)

Question 48

What is the primary purpose of image segmentation?

Accepted Answer

To divide an image into distinct regions of interest.

Answer

To classify objects in an image.

Answer

To generate realistic images from scratch.

Answer

To improve image quality for better visualization.

Question 49

Which of the following is a key difference between supervised and unsupervised learning in computer vision?

Accepted Answer

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

Answer

Supervised learning is inherently more accurate than unsupervised learning.

Answer

Unsupervised learning is always more computationally expensive than supervised learning.

Answer

Unsupervised learning is more commonly used in computer vision than supervised learning.

Question 50

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

Accepted Answer

To identify the most relevant and discriminative characteristics of an image.

Answer

To enhance the quality of an image before processing.

Answer

To reduce the computational cost of image processing tasks.

Answer

To compress an image for efficient storage.

Question 51

What is the role of transfer learning in computer vision?

Accepted Answer

To leverage knowledge from a pre-trained model to improve the performance of a new model.

Answer

To make models more interpretable.

Answer

To reduce the computational cost of training a model.

Answer

To generate synthetic data for training.