All Exams >
Class 10 >
Artificial Intelligence for Class 10 >
All Questions
All questions of Computer Vision for Class 10 Exam
Which computer vision task involves assigning a single label to an input image from a predefined set of categories?- a) Object Detection
- b) Classification
- c) Instance Segmentation
- d) Classification Localisation
Correct answer is option 'B'. Can you explain this answer?
Which computer vision task involves assigning a single label to an input image from a predefined set of categories?
a)
Object Detection
b)
Classification
c)
Instance Segmentation
d)
Classification Localisation
|
Arvind bajaj answered |
Understanding Image Classification
Image classification is a fundamental task in computer vision where the goal is to assign a single label to an input image from a predefined set of categories. This task is crucial for various applications, such as image retrieval, scene understanding, and automated tagging.
Key Features of Image Classification
- Single Label Assignment: In classification, each image is associated with only one label, representing the most relevant category from a defined set. For example, an image of a dog would be labeled as "dog" and not "cat" or "bird."
- Predefined Categories: The categories are predefined, meaning that the classification model has been trained on a specific set of labels. For example, categories could include "cat," "dog," "car," and "tree."
- Model Training: Classification models are trained using labeled datasets, where each image is paired with the correct label. Techniques such as convolutional neural networks (CNNs) are often used for feature extraction and classification.
Comparison with Other Tasks
- Object Detection: This task involves identifying and localizing multiple objects within an image, assigning bounding boxes and labels to each detected object.
- Instance Segmentation: This is more advanced than object detection as it not only detects objects but also delineates their precise boundaries at the pixel level.
- Classification Localization: This task combines classification with localization, where the model identifies the presence of an object and indicates its location in the image.
Conclusion
In summary, option 'B' (Classification) is the correct answer because it specifically involves assigning a single label to an image from a set of categories, distinguishing it from other tasks in the computer vision domain.
Image classification is a fundamental task in computer vision where the goal is to assign a single label to an input image from a predefined set of categories. This task is crucial for various applications, such as image retrieval, scene understanding, and automated tagging.
Key Features of Image Classification
- Single Label Assignment: In classification, each image is associated with only one label, representing the most relevant category from a defined set. For example, an image of a dog would be labeled as "dog" and not "cat" or "bird."
- Predefined Categories: The categories are predefined, meaning that the classification model has been trained on a specific set of labels. For example, categories could include "cat," "dog," "car," and "tree."
- Model Training: Classification models are trained using labeled datasets, where each image is paired with the correct label. Techniques such as convolutional neural networks (CNNs) are often used for feature extraction and classification.
Comparison with Other Tasks
- Object Detection: This task involves identifying and localizing multiple objects within an image, assigning bounding boxes and labels to each detected object.
- Instance Segmentation: This is more advanced than object detection as it not only detects objects but also delineates their precise boundaries at the pixel level.
- Classification Localization: This task combines classification with localization, where the model identifies the presence of an object and indicates its location in the image.
Conclusion
In summary, option 'B' (Classification) is the correct answer because it specifically involves assigning a single label to an image from a set of categories, distinguishing it from other tasks in the computer vision domain.
What is the significance of the RGB values in digital color representation?- a) They determine the brightness of an image.
- b) They are combined to create the final color of a pixel.
- c) They are only used in black and white images.
- d) They represent the intensity of yellow in a pixel.
Correct answer is option 'B'. Can you explain this answer?
What is the significance of the RGB values in digital color representation?
a)
They determine the brightness of an image.
b)
They are combined to create the final color of a pixel.
c)
They are only used in black and white images.
d)
They represent the intensity of yellow in a pixel.
|
EduRev Class 10 answered |
The RGB values are crucial in digital color representation as they are combined to formulate the final color of a pixel. Each pixel in a digital image can be represented by three values corresponding to the intensities of red, green, and blue light. By adjusting these values, a wide spectrum of colors can be produced. For instance, if a pixel has RGB values of 100, 150, and 200, the blue component is most dominant, giving the pixel a bluish hue. This method of color mixing is fundamental to how digital displays render images.
What is the primary function of the convolution layer in a convolutional neural network (CNN)?- a) To generate random noise for data augmentation
- b) To perform feature extraction from the input image
- c) To classify images into predefined categories
- d) To increase the resolution of the input images
Correct answer is option 'B'. Can you explain this answer?
What is the primary function of the convolution layer in a convolutional neural network (CNN)?
a)
To generate random noise for data augmentation
b)
To perform feature extraction from the input image
c)
To classify images into predefined categories
d)
To increase the resolution of the input images
|
|
EduRev Class 10 answered |
The convolution layer in a CNN is primarily responsible for feature extraction. It uses multiple small filters, known as kernels, to scan the input images and capture various features. This process generates a feature map, which highlights essential elements of the image while discarding irrelevant details. By focusing on critical features, such as edges and textures, CNNs can effectively analyze visual data, making them suitable for tasks like image recognition and classification. Interestingly, the ability of CNNs to learn hierarchical feature representations is one of the reasons they have become so popular in the field of computer vision.
What is the primary function of the Rectified Linear Unit (ReLU) in a Convolutional Neural Network (CNN)?- a) To reduce the size of the feature map
- b) To introduce non-linearity by modifying the feature map
- c) To normalize the feature map values
- d) To increase the dimensionality of the input data
Correct answer is option 'B'. Can you explain this answer?
What is the primary function of the Rectified Linear Unit (ReLU) in a Convolutional Neural Network (CNN)?
a)
To reduce the size of the feature map
b)
To introduce non-linearity by modifying the feature map
c)
To normalize the feature map values
d)
To increase the dimensionality of the input data
|
|
EduRev Class 10 answered |
The primary function of the Rectified Linear Unit (ReLU) in a Convolutional Neural Network (CNN) is to introduce non-linearity by modifying the feature map. The ReLU layer achieves this by setting all negative values to zero and retaining positive values as they are. This non-linear transformation is crucial for enhancing the distinctiveness of features, which improves the performance of subsequent layers in the network. Notably, the use of ReLU has been shown to lead to faster convergence during training compared to other activation functions.
What does the field of Computer Vision enable machines to do?- a) Perform calculations faster than humans
- b) Understand and analyze images or visual data
- c) Generate text based on visual inputs
- d) Replace human vision completely
Correct answer is option 'B'. Can you explain this answer?
What does the field of Computer Vision enable machines to do?
a)
Perform calculations faster than humans
b)
Understand and analyze images or visual data
c)
Generate text based on visual inputs
d)
Replace human vision completely
|
Kamna Science Academy answered |
The field of Computer Vision enables machines to understand and analyze images or visual data. By utilizing algorithms and specific methods, computers can process visual information similarly to how humans perceive and interpret their surroundings. This capability is crucial in various applications, including facial recognition, autonomous vehicles, and medical image analysis. An additional fact is that Computer Vision is a rapidly evolving area within AI, with ongoing research aimed at improving the accuracy and functionality of visual analysis systems.
What is the primary function of the pooling layer in a Convolutional Neural Network (CNN)?- a) To enhance the resolution of the image
- b) To reduce the spatial size of the convolved feature
- c) To apply color filters to the image
- d) To classify the image into different categories
Correct answer is option 'B'. Can you explain this answer?
What is the primary function of the pooling layer in a Convolutional Neural Network (CNN)?
a)
To enhance the resolution of the image
b)
To reduce the spatial size of the convolved feature
c)
To apply color filters to the image
d)
To classify the image into different categories
|
Nk Classes answered |
The pooling layer in a CNN is primarily responsible for reducing the spatial size of the convolved features. This reduction is crucial as it helps in making the computation more efficient and reduces the number of parameters, which can help mitigate overfitting. Pooling also retains important features while making the representation more manageable. An interesting fact about pooling is that it contributes to the translation invariance of the model, which means that small shifts in the input image will not significantly affect the output, making the network more robust.
What is the primary purpose of Artificial Intelligence in Data Sciences?- a) To replace human intelligence in all tasks
- b) To improve human decision-making through data analysis
- c) To teach computers to imitate human intelligence
- d) To eliminate the need for statistics in analysis
Correct answer is option 'C'. Can you explain this answer?
What is the primary purpose of Artificial Intelligence in Data Sciences?
a)
To replace human intelligence in all tasks
b)
To improve human decision-making through data analysis
c)
To teach computers to imitate human intelligence
d)
To eliminate the need for statistics in analysis
|
|
Kamna Science Academy answered |
The primary purpose of Artificial Intelligence in Data Sciences is to teach computers to imitate human intelligence. This involves using methods such as statistics, data analysis, and machine learning to analyze real-world situations. By mimicking human cognitive processes, AI can help in understanding complex data patterns and making informed decisions based on that data. An interesting fact is that AI's ability to learn from data has led to significant advancements in various fields, from healthcare to finance, enhancing the capabilities of professionals in these areas.
What is the primary reason that patches E and F are easier to locate in an image?- a) They represent edges of a building.
- b) They depict corners of the building.
- c) They contain a consistent pattern along their length.
- d) They are located in the center of the image.
Correct answer is option 'B'. Can you explain this answer?
What is the primary reason that patches E and F are easier to locate in an image?
a)
They represent edges of a building.
b)
They depict corners of the building.
c)
They contain a consistent pattern along their length.
d)
They are located in the center of the image.
|
|
Nk Classes answered |
Patches E and F are easier to locate because they represent the corners of a building. Corners are distinctive features as they change appearance regardless of their position in the image, making them stand out compared to other features like edges that may look similar along their length. This uniqueness is crucial in image processing, as it allows for more accurate identification and analysis of specific locations. Interestingly, corners are often utilized in various applications, including computer vision and robotics, where precise navigation is required.
What color is produced when the red channel has a high intensity, the green channel has a moderate intensity, and the blue channel has a low intensity?- a)Green
- b)Blue
- c)Yellow or Orange
- d)Purple
Correct answer is option 'C'. Can you explain this answer?
What color is produced when the red channel has a high intensity, the green channel has a moderate intensity, and the blue channel has a low intensity?
a)
Green
b)
Blue
c)
Yellow or Orange
d)
Purple
|
|
EduRev Class 10 answered |
When the red channel is at a high intensity, the green channel at a moderate intensity, and the blue channel at a low intensity, the resulting color can be a shade of yellow or orange. For example, when the pixel values are R=255, G=200, and B=100, this combination leads to a bright orange color. This illustrates how varying intensities in the RGB color model can create a wide spectrum of colors based on the contribution of each channel.
Which of the following patches is considered a good feature in images due to its unique characteristics?- a) Blue Patch
- b) Black Patch
- c) Red Patch
- d) Green Patch
Correct answer is option 'C'. Can you explain this answer?
Which of the following patches is considered a good feature in images due to its unique characteristics?
a)
Blue Patch
b)
Black Patch
c)
Red Patch
d)
Green Patch
|
|
Kamna Science Academy answered |
The red patch is considered a good feature because it represents a corner, which is unique and distinguishable regardless of its position in the image. Corners are particularly valuable in computer vision as they provide critical points that are less likely to appear in repetitive or flat areas, making them easier to track and analyze in various applications. Interestingly, the detection of corner features plays a crucial role in algorithms used for object recognition and image matching.
What is one of the key applications of computer vision in smart cities and homes?- a) Object tracking for advertising
- b) Facial recognition for guest identification
- c) Predictive maintenance for utilities
- d) Traffic signal optimization
Correct answer is option 'B'. Can you explain this answer?
What is one of the key applications of computer vision in smart cities and homes?
a)
Object tracking for advertising
b)
Facial recognition for guest identification
c)
Predictive maintenance for utilities
d)
Traffic signal optimization
|
|
EduRev Class 10 answered |
One of the primary applications of computer vision in smart cities and homes is facial recognition for guest identification. This technology enhances security by allowing systems to identify individuals and maintain a log of visitors. It can be particularly useful in settings like schools, where it can also be used to monitor student attendance. An interesting fact is that facial recognition technology has evolved significantly and can now achieve high accuracy rates, reducing the chances of misidentification.
What is the primary function of a pixel in digital images?- a) It serves as a color filter.
- b) It is the largest unit of information in a digital photograph.
- c) It is the smallest unit of information in a digital photograph.
- d) It acts as a compression tool for images.
Correct answer is option 'C'. Can you explain this answer?
What is the primary function of a pixel in digital images?
a)
It serves as a color filter.
b)
It is the largest unit of information in a digital photograph.
c)
It is the smallest unit of information in a digital photograph.
d)
It acts as a compression tool for images.
|
|
Nk Classes answered |
A pixel, short for "picture element," is indeed the smallest unit of information in a digital photograph. These tiny units are the building blocks of any digital image, as they combine to form the overall picture we see. The arrangement and color of thousands or millions of pixels determine the image's clarity and detail. An interesting fact is that the term "pixel" was first used in the 1960s as a blend of "picture" and "element," emphasizing its role in constructing visual data.
What is the primary function of the Fully Connected Layer (FCP) in a Convolutional Neural Network (CNN)?- a) To perform convolution operations on the input image
- b) To flatten the output of the previous layers into a single vector
- c) To classify the image based on the features extracted
- d) To apply pooling techniques to reduce dimensionality
Correct answer is option 'C'. Can you explain this answer?
What is the primary function of the Fully Connected Layer (FCP) in a Convolutional Neural Network (CNN)?
a)
To perform convolution operations on the input image
b)
To flatten the output of the previous layers into a single vector
c)
To classify the image based on the features extracted
d)
To apply pooling techniques to reduce dimensionality
|
|
Kamna Science Academy answered |
The Fully Connected Layer (FCP) serves the essential purpose of classifying images based on features extracted by prior convolution and pooling layers. After processing through these layers, the output is transformed into a single vector, where each value corresponds to the probability of the image belonging to a specific label. This final classification step is crucial for tasks such as identifying objects in images, and it relies on the effective extraction of relevant features by the CNN.
What is the range of pixel intensity values for grayscale images?- a) 0 to 255
- b) 1 to 100
- c) 0 to 100
- d) 0 to 512
Correct answer is option 'A'. Can you explain this answer?
What is the range of pixel intensity values for grayscale images?
a)
0 to 255
b)
1 to 100
c)
0 to 100
d)
0 to 512
|
|
EduRev Class 10 answered |
Grayscale images consist of pixels that can display varying intensities of gray, with values ranging from 0 (black) to 255 (white). This range allows for a total of 256 different shades of gray. Understanding this pixel intensity range is essential in digital imaging and computer graphics, as it directly influences how images are represented and processed.
What does a pixel value of 0 typically represent in an 8-bit grayscale image?- a) Full color
- b) No color (black)
- c) Maximum brightness
- d) Minimum brightness
Correct answer is option 'B'. Can you explain this answer?
What does a pixel value of 0 typically represent in an 8-bit grayscale image?
a)
Full color
b)
No color (black)
c)
Maximum brightness
d)
Minimum brightness
|
|
Kamna Science Academy answered |
In an 8-bit grayscale image, a pixel value of 0 signifies the absence of color, which is represented by black. Conversely, a pixel value of 255 indicates full brightness or white. The range of values from 0 to 255 allows for the representation of various shades of gray, with intermediate values creating different shades based on the mixture of red, green, and blue light. This binary coding system efficiently encodes image data for digital display and processing.
Chapter doubts & questions for Computer Vision - Artificial Intelligence for Class 10 2025 is part of Class 10 exam preparation. The chapters have been prepared according to the Class 10 exam syllabus. The Chapter doubts & questions, notes, tests & MCQs are made for Class 10 2025 Exam. Find important definitions, questions, notes, meanings, examples, exercises, MCQs and online tests here.
Chapter doubts & questions of Computer Vision - Artificial Intelligence for Class 10 in English & Hindi are available as part of Class 10 exam.
Download more important topics, notes, lectures and mock test series for Class 10 Exam by signing up for free.
Artificial Intelligence for Class 10
24 videos|64 docs|8 tests
|
|
© EduRev
|
Education Revolution
|
|
Signup to see your scores
go up
within 7 days!
within 7 days!
Takes less than 10 seconds to signup