Computer Vision in ML Quiz With Answers

Computer MCQs 360April 4, 2024

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Welcome to the Computer Vision in ML MCQs with Answers. In this post, we have shared Computer Vision in ML Online Test for different competitive exams. Find practice Computer Vision in ML Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Computer Vision in ML.

Computer Vision in ML Online Quiz

By presenting 3 options to choose from, Computer Vision in ML Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Computer Vision in ML Questions and Answers and click over the correct answer.

Test Name: Computer Vision in ML MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

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Computer Vision in ML

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1 / 40

Histogram of Oriented Gradients (HOG) is used for ________ in images.

Object detection

Image classification

Feature extraction

2 / 40

________ methods analyze the spatial arrangement of colors in images.

Color histogram

Histogram equalization

Feature extraction

3 / 40

The primary advantage of using convolutional layers in CNNs is ________.

Memory usage

Global connectivity

Local connectivity

4 / 40

________ is a technique that estimates the 3D structure of an object from a single image.

Depth estimation

Image registration

Structure from motion

5 / 40

Image stitching combines multiple images into a ________ image.

Time-lapse

Panoramic

6 / 40

________ is used to detect edges in images by identifying sudden changes in pixel intensity.

Histogram equalization

Feature extraction

Edge detection

7 / 40

Region-based CNNs (R-CNNs) use ________ to propose regions likely to contain objects.

Selective search

Principal component analysis

Gradient descent

8 / 40

________ is used to detect and correct distortion in images.

Geometric calibration

Feature extraction

Histogram equalization

9 / 40

Transfer learning in computer vision involves ________.

Random initialization

Using pre-trained models

Dimensionality reduction

10 / 40

________ are used to transform images into a more manageable and informative representation.

Segmentation methods

Histogram equalization

Feature descriptors

11 / 40

Semantic segmentation aims to assign ________ to each pixel in an image.

Features

Class labels

Coordinates

12 / 40

The Haar Cascade classifier is used for ________.

Image segmentation

Object detection

Image classification

13 / 40

The IoU (Intersection over Union) metric is used to evaluate ________.

Image segmentation

Image classification

Object detection

14 / 40

The process of reducing the resolution of an image is called ________.

Image compression

Image downsampling

Image upsampling

15 / 40

________ techniques are used to align images for comparison or processing.

Image segmentation

Image registration

Image enhancement

16 / 40

In image classification, the term "softmax" refers to a ________ function.

Activation

Probability

Loss

17 / 40

In image segmentation, each pixel is assigned a ________.

Probability score

Feature vector

Class label

18 / 40

The ________ algorithm is used for feature matching between images.

SVM

PCA

SIFT

19 / 40

In image processing, the Laplacian of Gaussian (LoG) filter is used for ________.

Edge detection

Noise reduction

Image enhancement

20 / 40

________ is a technique used to classify images into predefined categories.

Image segmentation

Object detection

Image classification

21 / 40

Convolutional Neural Networks (CNNs) are primarily used for ________ tasks.

Image recognition

Regression

Natural language processing

22 / 40

The process of transforming images into numerical data for machine learning is called ________.

Image preprocessing

Feature extraction

Data augmentation

23 / 40

________ algorithms are used to identify keypoints and descriptors in images.

Image classification

Image segmentation

Feature detection

24 / 40

The OpenCV library is commonly used for ________ tasks.

Computer vision

Natural language processing

Reinforcement learning

25 / 40

Optical Character Recognition (OCR) is used to ________.

Convert images to text

Classify images

Segment images

26 / 40

Depth estimation in computer vision is used to determine ________.

Image orientation

Image brightness

Distance from camera

27 / 40

The term "bounding box" refers to a ________ that encloses an object in an image.

Rectangular region

Polygonal region

Circular region

28 / 40

In image classification tasks, the softmax function is used in the ________ layer.

Hidden

Output

Input

29 / 40

In computer vision, the term "feature extraction" refers to ________.

Identifying meaningful parts

Data compression

Training a model

30 / 40

________ techniques adjust image brightness and contrast automatically.

Histogram equalization

Edge detection

Gamma correction

31 / 40

________ techniques are used to align images based on common features.

Image enhancement

Image registration

Image segmentation

32 / 40

________ is a technique used to preprocess images by altering their appearance without changing their content.

Histogram equalization

Image augmentation

Feature scaling

33 / 40

Object detection algorithms aim to ________ objects within images.

Localize and classify

Segment and classify

Classify and categorize

34 / 40

The process of removing noise and unwanted details from images is called ________.

Feature extraction

Image denoising

Data augmentation

35 / 40

________ is used to describe the shape and structure of objects in images.

Contour analysis

Feature extraction

Texture analysis

36 / 40

________ algorithms analyze the relationship between pixels in an image.

Feature extraction

Edge detection

Segmentation

37 / 40

________ is a technique for enhancing image details at different scales.

Multi-scale analysis

Gaussian blur

Feature scaling

38 / 40

Feature maps in CNNs represent ________ extracted from images.

Filters

Labels

Features

39 / 40

The purpose of data augmentation in computer vision is to ________.

Reduce overfitting

Increase dataset diversity

Speed up training

40 / 40

________ methods analyze the statistical properties of textures in images.

Edge detection

Texture analysis

Feature extraction

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Computer MCQs 360April 4, 2024

0 14 1 minute read