Image Segmentation MCQs With Answers

Computer MCQs 360April 5, 2024

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

Image Segmentation Online Quiz

By presenting 3 options to choose from, Image Segmentation 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 Image Segmentation Questions and Answers and click over the correct answer.

Test Name: Image Segmentation MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

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Image Segmentation

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1 / 40

Which technique is used for refining segmentation masks by considering pixel-wise dependencies?

Conditional Random Fields (CRF) (correct)

Atrous Convolution

U-Net

2 / 40

Which technique is used for handling class imbalance in image segmentation datasets?

Mean Squared Error

Dice Loss (correct)

Cross-Entropy Loss

3 / 40

Which evaluation metric is used for assessing the quality of segmentation masks in biomedical imaging?

Precision

Recall

Dice Coefficient (correct)

4 / 40

Which algorithm is known for its efficiency in medical image segmentation tasks?

FCN

U-Net (correct)

DeepLab

5 / 40

Which technique is used for refining object boundaries in image segmentation networks?

Boundary refinement (correct)

Feature extraction

Pixel classification

6 / 40

Which algorithm is known for its speed and accuracy in real-time video semantic segmentation?

DeepLab (correct)

YOLO

Mask R-CNN

7 / 40

Which algorithm is known for its ability to segment objects with irregular shapes?

U-Net

DeepLab (correct)

SegNet

8 / 40

What is the primary advantage of using CRF in post-processing for image segmentation?

Improving spatial coherence of segmentation (correct)

Enhancing color contrast

Reducing computational cost

9 / 40

What is the purpose of post-processing techniques like Conditional Random Fields (CRF) in image segmentation?

Feature extraction refinement

Enhancing model performance

Smoothing segment boundaries (correct)

10 / 40

What does CRF stand for in the context of post-processing in segmentation networks?

Conditional Random Field (correct)

Cross-Reference Framework

Convolutional Response Filter

11 / 40

Which algorithm is known for its ability to segment images while preserving fine details?

DeepLab (correct)

U-Net

FCN

12 / 40

What is the primary advantage of using FCN over traditional CNN architectures for segmentation?

Retaining spatial information (correct)

Faster computation

Higher memory efficiency

13 / 40

Which algorithm combines object detection and image segmentation?

Mask R-CNN (correct)

YOLO

SSD

14 / 40

Which technique is used for improving segmentation performance by capturing long-range dependencies?

Atrous Convolution (correct)

Depthwise Separable Convolution

Inception Module

15 / 40

What is the primary difference between semantic segmentation and instance segmentation?

Instance segmentation enhances image quality

Semantic segmentation labels objects

Instance segmentation separates individual objects (correct)

16 / 40

Which deep learning framework is commonly used for image segmentation tasks?

PyTorch

Keras

TensorFlow (correct)

17 / 40

What does FCN stand for in the context of semantic segmentation?

Fully Connected Network

Fast Convolutional Network

Fully Convolutional Network (correct)

18 / 40

Which algorithm is suitable for real-time semantic segmentation applications?

DeepLab (correct)

SegNet

U-Net

19 / 40

What is the purpose of dilated convolutions in image segmentation networks?

Enhancing image resolution

Accelerating training time

Enlarging the receptive field without increasing parameters (correct)

20 / 40

What does ENet stand for in the context of lightweight image segmentation networks?

Efficient Neural Network (correct)

Expanded Network

Enhanced Network

21 / 40

What is the purpose of skip connections in U-Net architecture?

Reducing model size

Facilitating gradient flow and preserving spatial information (correct)

Enhancing feature extraction

22 / 40

What is image segmentation in computer vision?

Enhancing image resolution

Dividing an image into meaningful segments (correct)

Extracting feature descriptors

23 / 40

Which technique is used for improving segmentation accuracy by refining boundaries?

Pixel classification

Feature extraction

Boundary refinement (correct)

24 / 40

Which algorithm is used for medical image segmentation with a focus on boundary delineation?

Boundary U-Net (correct)

DeepLab

PSPNet

25 / 40

Which technique is used for improving the efficiency of image segmentation networks by reducing redundant computations?

Depthwise Separable Convolution (correct)

Residual Connection

Dilated Convolution

26 / 40

Which technique is used for instance segmentation, where objects of the same class are individually segmented?

FCN

U-Net

Mask R-CNN (correct)

27 / 40

What is the primary advantage of using Mask R-CNN over traditional R-CNN for segmentation tasks?

Ability to generate instance-level masks (correct)

Faster inference time

Higher precision

28 / 40

Which algorithm is suitable for pixel-level segmentation with minimal training data?

DeepLab

FCN (Fully Convolutional Network) (correct)

U-Net

29 / 40

Which evaluation metric is commonly used to measure the accuracy of segmentation algorithms?

F1 score

Mean Absolute Error

Intersection over Union (IoU) (correct)

30 / 40

What is the purpose of semantic segmentation in computer vision?

Enhancing image contrast

Classifying each pixel into a category (correct)

Detecting objects

31 / 40

Which technique is used for reducing memory consumption in deep image segmentation networks?

Atrous Convolution

Residual Connection

Depthwise Separable Convolution (correct)

32 / 40

What does GAN stand for in the context of image segmentation enhancement?

Gradient Adjustment Network

Global Adjustment Network

Generative Adversarial Network (correct)

33 / 40

Which algorithm is known for its ability to segment images at multiple scales?

DeepLab (correct)

Mask R-CNN

SegNet

34 / 40

What is the primary advantage of using U-Net architecture for biomedical image segmentation?

High computational cost

Handling small datasets effectively (correct)

Low memory usage

35 / 40

What is the primary advantage of using DeepLabV3+ over DeepLabV3 in image segmentation?

Lower memory usage

Improved boundary delineation (correct)

Faster inference time

36 / 40

What is the primary use of PSPNet in image segmentation tasks?

Improving feature extraction

Capturing global context information (correct)

Enhancing image resolution

37 / 40

What does PSPNet stand for in the context of image segmentation?

Pyramid Scene Parsing Network (correct)

Pixel Segmentation Network

Patch Selection Perception Network

38 / 40

What is the primary use of instance segmentation in autonomous driving applications?

Identifying and tracking individual objects (correct)

Estimating scene depth

Enhancing road visibility

39 / 40

What is the primary challenge of semantic segmentation in natural scenes with varying lighting conditions?

Image noise

Object occlusion

Illumination variation (correct)

40 / 40

What does IoU measure in the context of image segmentation evaluation?

Mean Average Precision

Intersection over Union (correct)

Dice Coefficient

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Computer MCQs 360April 5, 2024

0 83 1 minute read