Experts have designed these Class 10 AI Important Questions Chapter 5 Computer Vision Class 10 Important Questions and Answers NCERT Solutions Pdf for effective learning.
Computer Vision Class 10 Important Questions
Class 10 AI Computer Vision Important Questions
Important Questions of Computer Vision Class 10 – Class 10 Computer Vision Important Questions
Very Short Answer Type Questions (1 mark each)
Question 1.
What is a domain of AI-related to data systems and processes, in which the system collects numerous data, maintains data sets and derives meaning/ sense out of them.
Answer:
Data Science
Question 2.
The information extracted through data science can be used to make a decision about it. Is it True or False?
Answer:
True
Question 3.
Give one example of data science?
Answer:
Price Comparison websites
Question 4.
What is the full form of CV in terms of AI?
Answer:
Computer Vision
Question 5.
Name the domain of AI that depicts the capability of a machine to get and analyse visual information and afterwards predict some decisions about it.
Answer:
Computer Vision/CV
Question 6.
Which domain of AI translates digital visual data into descriptions and then turned into a computerreadable language to aid the decision-making?
Answer:
Computer Vision
Question 7.
What is the importance of CV?
Answer:
AI enables computer vision to understand, recognise, and analyse all types of visual data.
Short Answer Type Questions (2 marks each)
Question 1.
Define computer vision and explain its two main applications.
Answer:
Computer vision is a field of Artificial Intelligence that enables computers to interpret and understand visual information from the real world through digital images and videos. It involves techniques like image recognition, object detection, and image segmentation.
Two main applications of computer vision include:
Image classification: Identifying and classifying objects, scenes, or activities within images. For example, recognising faces in photographs or identifying the type of animal in a picture.
Object detection: Locating and identifying specific objects within an image or video. For example, detecting pedestrians and vehicles in self-driving cars or identifying suspicious objects in security cameras.
Question 2.
What is OpenCV?
Answer:
OpenCV or Open-Source Computer Vision Library is that tool that helps a computer to extract these features from the images. It is capable of processing images and videos to identify objects, faces, or even handwriting.
Question 3.
Explain the difference between supervised and unsupervised learning in the context of computer vision.
Answer:
Supervised learning: In supervised learning, the computer vision system is trained on a dataset of labelled images. Each image is associated with a label that identifies the object or scene it depicts. During training, the system learns to associate the features of the image with the corresponding label.
This allows it to accurately classify and identify objects in new, unseen images.
Unsupervised learning: In unsupervised learning, the computer vision system is not provided with any labelled data. It must learn to identify patterns and relationships in the data on its own. This can be useful for tasks such as image segmentation, where the goal is to group similar pixels together without any prior knowledge about the image content.
Question 4.
Explain the difference between image recognition and object detection.
Answer:
Image recognition: This refers to the task of identifying the content of an entire image. It involves classifying the image into a predefined category, such as “landscape,” “portrait,” or “cat.”
Object detection: This refers to the task of locating and identifying specific objects within an image or video. It involves identifying the bounding boxes around each object and classifying the object within each box.
For example, an image recognition system might classify an image as a “cat,” while an object detection system might identify and locate two cats within the same image.
Question 5.
Give two examples of how computer vision is used in everyday life.
Answer:
Facial recognition in smartphones: Used to unlock smartphones and authenticate users for various applications.
Self-driving cars: Cameras are used to detect and track objects like pedestrians, vehicles, and traffic signals, enabling autonomous driving.
Question 6.
How does the face lock system work in a smartphone?
Answer:
The face lock system in a smartphone detects and captures the image.
It saves the features of the face at the beginning or the first time when the lock is applied.
After that whenever the features match it will unlock the smartphone.
Question 7.
Why do pixel values have numbers?
Answer:
Computer systems only work in the form of ones and zeros or binary systems. Each bit in a computer system can have either a zero or a one. Each pixel uses 1 byte of an image each bit can have two possible values which tells us that the 8 bits can have 255 possibilities of values that start from 0 and ends at 255.
Question 8.
What is kernel?
Answer:
A kernel is a matrix or a small matrix used for blurring, sharpening, and many more which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner.
Question 9.
What is the difference between convolution and pooling layer?
Answer:
The major difference is if you include a large stride in the convolution filter, you are changing the types of features you extract in the algorithm, whereas if you change it in the pooling layer, you are simply changing how much the data is down-sampled.
Long Answer Type Questions (4 marks each)
Question 1.
Explain the concept of image recognition in computer vision. How does it work? Discuss the role of Convolutional Neural Networks (CNNs) in image recognition. Additionally, provide two real-world applications of image recognition.
Answer:
Image recognition in computer vision refers to the ability of computers to identify and classify objects in images and videos. It involves several steps:
- Preprocessing: The image is first cleaned and resized to ensure proper format and size for processing.
- Feature extraction: Algorithm identifies key features that distinguish different objects, such as edges, shapes, and colours.
- Classification: The extracted features are compared to a database of known objects to identify the object in the image.
Convolutional Neural Networks (CNNs) play a crucial role in image recognition. These are deep learning algorithms inspired by the structure of the human brain. They learn to identify complex patterns in images by analysing them through layers of filters and performing computations.
Real-world applications of image recognition include:
- Facial recognition: Used in security systems, unlocking smartphones, and tagging people in photos.
- Medical imaging: Assists doctors in diagnosing diseases by analysing X-rays, CT scans, and MRIs.
- Self-driving cars: Identify objects and obstacles on the road, enabling autonomous navigation.
- Image search: Helps users find relevant images based on their queries.
Question 2.
What do you mean by Computer Vision?
Answer:
Open CV or Open-Source Computer Vision Library is that tool that helps a computer to extract these features from the images. It is capable of processing images and videos to identify objects, faces, or even handwriting.
Question 3.
Discuss the ethical implications of using vision technolog. Consider issues such as bias vision technology. Consider issues such as bias, privacy, and security. Provide suggestions for mitigating these risks.
Answer:
Ethical implications of computer vision technology include:
- Bias: Algorithms trained on biased data can perpetuate discrimination and unfair treatment.
- Privacy: Concerns arise regarding the collection and use of personal data through facial recognition and other surveillance technologies.
- Security: Malicious actors can exploit vulnerabilities in computer vision systems for cyberattacks or manipulation.
Suggestions for mitigating these risks include:
- Transparency: Openly communicating how algorithms are developed and deployed.
- Accountability: Holding developers and users responsible for the ethical implications of their work.
- Regulation: Implementing laws and standards to protect individual rights and privacy.
- Human oversight: Ensuring human control over* decision-making processes involving computer vision technology.
Question 4.
Describe the future of computer vision technology and its potential impact on various aspects of life. Discuss one emerging application of computer vision with its potential benefits and challenges.
Answer:
The future of computer vision is promising, with potential applications in various fields:
- Healthcare: Assisting with surgery, personalised medicine, and disease diagnosis.
- Education: Enhancing learning experiences, individualising instruction, and providing real-time feedback.
- Transportation: Optimising traffic flow, improving safety, and automating delivery services.
- Retail: Personalising shopping experiences, providing product recommendations, and automating checkout processes.
Emerging application:
- Smart homes: Using computer vision for object recognition, gesture control, and activity monitoring to enhance comfort, security, and energy efficiency.
Benefits:
- Improved convenience, safety, and security.
- Increased personalisation and customisation of services.
- Automation of tedious and dangerous tasks.
- Improved efficiency and productivity in various industries.
Challenges:
- Ethical concerns regarding privacy, bias, and transparency.
- Dependence on large amounts of data and computational resources.
- Job displacement due to automation.
- Potential for misuse by malicious actors. By addressing these challenges and promoting responsible development, computer vision has the potential to significantly improve our lives and transform various industries.
Question 5.
Explain the concept of image segmentation in AI image processing. What are the different types of image segmentation? Discuss the applications of image segmentation in various fields.
Answer:
Image segmentation is the process of partitioning an image into multiple segments or regions, based on certain features such as colour, texture, intensity, or depth. It aims to group pixels that belong together based on their similarities and separate them from pixels that belong to different objects or background.
Types of Image Segmentation:
- Thresholding: This is a simple and effective method that separates pixels based on a single threshold value. Pixels brighter than the threshold are classified as foreground, while those darker are classified as background.
- Edge Detection: This method identifies edges or boundaries between different objects in an image. This helps in separating objects and extracting their shapes.
- Region-growing: This method starts with a seed point and assigns neighboring pixels to the same region if they are similar in terms of intensity, colour, or texture. This process continues until no more pixels can be added to the region.
- Watershed: This method treats an image as a topographic surface, where pixels represent elevation values. Regions are formed by flooding basins (darker areas) and separating them by watersheds (ridges).
- Clustering: This method involves grouping pixels with similar features into clusters using various clustering algorithms, such as K-means and meanshift.
Applications of Image Segmentation:
- Medical imaging: Segmentation helps in identifying tumors, lesions, and other abnormalities in medical images.
- Object detection and tracking: Segmentation is used to detect and track objects in videos and images for various applications, such as autonomous vehicles and surveillance systems.
- Content-based image retrieval: Segmentation helps in retrieving images based on their content, such as specific objects or scenes.
- Image editing and manipulation: Segmentation allows for selective editing of specific objects or regions in an image.
- Remote sensing: Segmentation is used to analyse satellite images for land cover classification, crop monitoring, and disaster assessment.
Question 6.
Discuss the role of machine learning in AI image processing. Explain how different machine learning algorithms are used in various image processing tasks.
Answer:
Machine learning plays a crucial role in AI image processing by providing algorithms that can learn from data and make predictions on new data. Various machine learning algorithms are used for different image processing tasks:
- Supervised learning: This type of learning requires labelled datasets where each data point has a known output. Some examples of supervised learning algorithms used in image processing are:
- Image classification: Classifies images into predefined categories, such as cats vs. dogs.
- Object detection: Detects and localises objects in an image, such as identifying faces or cars.
- Unsupervised learning: This type of learning does not require labelled datasets. Some examples of unsupervised learning algorithms used in image processing are:
- Image clustering: Groups images based on thetr similarities without any prior knowledge of the categories.
- Dimensionality reduction: Reduces the dimensionality of image data for efficient storage and processing.
- Reinforcement learning: This type of learning involves an agent interacting with an environment to learn by trial and error. It can be used in image processing tasks such as image segmentation and image captioning.
Question 7.
Explain the ethical considerations and challenges in the use of AI image processing. How can we ensure responsible and ethical use of this technology?
Answer:
AI image processing, like any powerful technology, comes with ethical considerations and challenges that need to be addressed:
Bias and discrimination: AI algorithms can inherit biases from the data they are trained on, leading to discrimination against specific groups of people.
Privacy concerns: Al image processing can be used to extract sensitive information from images, raising concerns about privacy and data security.
Misuse and manipulation: AI image processing can be used to create fake images or videos for malicious purposes, such as spreading misinformation or propaganda.
To ensure responsible and ethical use of AI image processing, several measures can be taken:
Transparency and explainability: Developers should strive to create transparent and explainable AI models to understand how they make decisions and mitigate bias.
Data governance and privacy protection: Robust data governance frameworks and privacy protection measures are essential to ensure data is collected, used, and stored ethically.
Human oversight and accountability: Humans should be responsible for the decisions made by AI systems, and there should be clear, accountability mechanisms in place.
Public engagement and education: Educating the public about the capabilities and potential risks of AI image processing is crucial for building
Case Study Based Subjective Questions:
I. Read the following text and answer the following questions based on the same:
Smart Surveillance System
A residential complex installed a new AI-powered smart surveillance system. Cameras equipped with computer vision algorithms monitor common areas and individual apartments upon resident consent. The system automatically detects suspicious activities like trespassing, loitering, and unauthorised vehicle entry. It triggers alerts and sends real-time notifications to residents and security personnel.
Question 1.
Identify the computer vision applications used in the smart surveillance system.
Answer:
Computer vision applications:
- Object detection and recognition: Identifying individuals, vehicles, and suspicious objects.
- Motion tracking and analysis: Monitoring movement patterns and detecting anomalies.
- Scene understanding: Analysing the context and environment of the monitored area.
Question 2.
Analyse the potential benefits and drawbacks of implementing such a system.
Answer:
Potential benefits:
- Enhanced security and crime prevention.
- Improved situational awareness for residents and security personnel.
- Faster response time to incidents and emergencies.
Question 3.
Discuss the ethical considerations related to data privacy and potential biases in the computer vision algorithms.
Answer:
Potential drawbacks:
- Privacy concerns over constant monitoring and data collection.
- Risk of bias and discrimination in the algorithms leading to unfair profiling.
- Potential for misuse and abuse of the system by unauthorised individuals.
Ethical considerations:
- Transparency and accountability in data collection and usage.
- User consent and control over personal data.
- Algorithmic fairness and mitigating bias towards specific groups.
- Clear guidelines and regulations regarding system usage and data access.
II. Read the following text and answer the following questions based on the same:
AI-powered Medical Diagnosis
Hospitals are adopting AI-powered computer vision systems to assist doctors in medical diagnosis. These systems analyse medical images like X-rays, CT scans, and MRIs to identify abnormalities and suggest potential diagnoses. They can also detect subtle changes in images over time, aiding in early disease detection and treatment planning.
Question 1.
Explain how computer vision algorithms are used for medical diagnosis.
Answer:
Computer vision for medical diagnosis:
- Image segmentation: Identifying and segmenting specific organs and tissues in the images.
- Feature extraction: Identifying and analysing relevant features like size, shape, and texture.
- Anomaly detection: Recognising abnormalities and deviations from normal patterns.
- Classification: Classifying the abnormalities into different disease categories.
Question 2.
Discuss the advantages and limitations of using AI in healthcare compared to traditional diagnostic methods.
Answer:
Advantages:
- Increased accuracy and early detection of diseases.
- Improved diagnostic consistency and reduced subjectivity.
- Potential cost reduction and resource optimisation. Limitations:
- Reliance on high-quality data and training models.
- Potential for misdiagnosis and reliance on human oversight.
- Ethical considerations regarding data privacy and algorithmic bias.
Question 3.
Analyse the role of AI in improving healthcare efficiency and accessibility.
Answer:
Role of AI in healthcare:
- Improved diagnostic accuracy and efficiency.
- Personalised medicine and tailored treatment plAnswer:”Increased access to healthcare services in remote areas.
- Automation of repetitive tasks and workload reduction for healthcare professionals.
III. Read the following text and answer the following questions based on the same:
Medical Diagnosis using AI Image Processing
In recent years, AI image processing has revolutionised the field of medical diagnosis. By analysing medical images such as X-rays, MRI scans, and CT scans, AI algorithms can detect abnormalities and diseases with remarkable accuracy. This technology has the potential to improve the early detection of diseases, leading to better treatment outcomes and saving lives.
Question 1.
Explain the two main types of AI image processing techniques used in medical diagnosis.
Answer:
Types of AI image processing techniques:
- eep learning: This technique uses artificial neural networks to analyse medical images and automatically identify patterns associated with specific diseases.
- Machine learning: This technique involves training algorithms on manually labelled datasets of medical images to recognise specific features and patterns.
Question 2.
Discuss the potential benefits and limitations of using AI for medical diagnosis.
Answer:
Benefits and limitations: Benefits:
- Improved accuracy and speed of diagnosis
- Early detection of diseases
- Reduced reliance on human expertise
Personalised treatment plans Limitations:
- Potential for bias in algorithms
- Lack of interpretability of results
- Ethical concerns regarding data privacy and algorithmic fairness
- Dependence on large datasets for training
Question 3.
Describe one specific case where AI image processing was used to successfully diagnose a disease.
Answer:
In 2020, researchers developed an AI algorithm that could detect early-stage lung cancer with 95 % accuracy using chest X-rays. This was significantly higher than the accuracy of human radiologists, who were able to detect the disease with only 85 % accuracy. This advancement has the potential to save thousands of lives by enabling early diagnosis and treatment of lung cancer.
IV. Read the following text and answer the following questions based on the same: Self-driving Cars and AI Image Processing
Self-driving cars are a rapidly emerging technology that has the potential to revolutionise transportation. These vehicles use a variety of sensors, including cameras, radar, and LiDAR, to perceive their environment and make decisions about how to navigate safely. AI image processing plays a crucial role in self-driving cars by enabling them to recognise objects, pedestrians, and other vehicles in real-time.
Question 1.
Explain the role of AI image processing in realtime object detection and recognition for selfdriving cars.
Answer:
Role of AI image processing:
- AI image processing algorithms analyse the data from cameras and other sensors to:
- Recognise and classify objects such as other vehicles, pedestrians, cyclists, and traffic signs.
- Track the movements of objects in real-time to predict their future trajectories.
- Estimate the distance and relative speed of objects to avoid collisions.
Question 2.
Discuss the ethical considerations and challenges involved in deploying self-driving cars on public roads.
Answer:
Ethical considerations and challenges:
- Safety concerns regarding potential accidents caused by self-driving cars.
- Liability issues in case of accidents involving selfdriving cars.
- Ethical dilemmas related to decision-making in critical situations.
- Data privacy concerns regarding the collection and use of sensor data.
Question 3.
Imagine you are a developer working on the AI system for a self-driving car. Describe a potential scenario where you would need to make a difficult decision regarding the car’s behaviour based on the image data it receives.
Answer:
Developer Scenario: The self-driving car is approaching a busy intersection. The image data shows a pedestrian crossing the street at a crosswalk, but also a truck running a red light.
The AI system needs to decide whether to proceed through the intersection or stop to avoid a potential collision. This is a difficult decision with potentially serious consequences, and the developer needs to carefully consider all available data and ethical implications before making a choice.