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Questions and Answers
What is the main difference between content-based image retrieval and traditional image retrieval methods?
What is the main difference between content-based image retrieval and traditional image retrieval methods?
- Uses image metadata for retrieval
- Utilizes text descriptions for searching
- Focuses solely on image size
- Relies on user’s image query based on contents (correct)
Optical character recognition (OCR) can convert hand-written text to a digital format.
Optical character recognition (OCR) can convert hand-written text to a digital format.
True (A)
What are the two types of Cartesian coordinate systems mentioned?
What are the two types of Cartesian coordinate systems mentioned?
2D and 3D Cartesian coordinate systems
In a _____ image, each pixel can be represented by 8 bits, giving 256 possible colors.
In a _____ image, each pixel can be represented by 8 bits, giving 256 possible colors.
Match the following types of images with their characteristics:
Match the following types of images with their characteristics:
What is the primary goal of computer vision?
What is the primary goal of computer vision?
Computer vision is the same as computer graphics.
Computer vision is the same as computer graphics.
Name one application of computer vision.
Name one application of computer vision.
In the context of image processing, the ______ filter is commonly used for noise reduction and edge detection.
In the context of image processing, the ______ filter is commonly used for noise reduction and edge detection.
Match the following image processing techniques with their descriptions:
Match the following image processing techniques with their descriptions:
Which of the following is NOT a type of image category?
Which of the following is NOT a type of image category?
Image segmentation involves grouping pixels into regions of similarity.
Image segmentation involves grouping pixels into regions of similarity.
What is a common use of edge detectors in computer vision?
What is a common use of edge detectors in computer vision?
What is a feature in computer vision?
What is a feature in computer vision?
Corners are not considered points of interest in an image.
Corners are not considered points of interest in an image.
What is the purpose of feature selection in the computer vision pipeline?
What is the purpose of feature selection in the computer vision pipeline?
In manufacturing, computer vision is used to ensure that products are being positioned correctly on the ______.
In manufacturing, computer vision is used to ensure that products are being positioned correctly on the ______.
Which of the following is an example of a computer vision application in the automotive industry?
Which of the following is an example of a computer vision application in the automotive industry?
Visual auditing in computer vision involves checking for visual compliance or deterioration.
Visual auditing in computer vision involves checking for visual compliance or deterioration.
Name one task that involves object detection in computer vision.
Name one task that involves object detection in computer vision.
Match the computer vision tasks with their descriptions:
Match the computer vision tasks with their descriptions:
What is the first step in a typical computer vision pipeline?
What is the first step in a typical computer vision pipeline?
Segmentation is an optional step in a computer vision pipeline.
Segmentation is an optional step in a computer vision pipeline.
Which of the following is a method used for feature matching?
Which of the following is a method used for feature matching?
What type of data needs to be processed to help a computer recognize a tire?
What type of data needs to be processed to help a computer recognize a tire?
Computer vision and computer graphics are the same field.
Computer vision and computer graphics are the same field.
The process of enhancing contrast or reducing noise in images is called ______.
The process of enhancing contrast or reducing noise in images is called ______.
Match each phase of the computer vision pipeline with its function:
Match each phase of the computer vision pipeline with its function:
What is the primary input and output of computer vision?
What is the primary input and output of computer vision?
Image processing involves the manipulation of __________.
Image processing involves the manipulation of __________.
What allows a computer to learn about visual data without explicit programming?
What allows a computer to learn about visual data without explicit programming?
Image segmentation helps in selecting regions of interest in an image.
Image segmentation helps in selecting regions of interest in an image.
Match the following distance metrics with their descriptions:
Match the following distance metrics with their descriptions:
What is the main objective of feature extraction in computer vision?
What is the main objective of feature extraction in computer vision?
Which classification technique is categorized under supervised learning?
Which classification technique is categorized under supervised learning?
Image processing techniques are seldom used in computer vision systems.
Image processing techniques are seldom used in computer vision systems.
Describe what it means for computer graphics to be the opposite operation of computer vision.
Describe what it means for computer graphics to be the opposite operation of computer vision.
Flashcards
Computer Vision
Computer Vision
A field of AI that enables computers to understand and make decisions from visual inputs like images and videos.
Image Processing
Image Processing
A method of manipulating digital images to extract useful information.
Linear Filter
Linear Filter
A filter that applies a weighted average to a pixel's neighborhood.
Median Filter
Median Filter
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Edge Detector
Edge Detector
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Image Morphology
Image Morphology
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Image Segmentation
Image Segmentation
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Thresholding
Thresholding
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Computer Graphics
Computer Graphics
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Feature Matching
Feature Matching
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Harris/Plessy Corner Detector
Harris/Plessy Corner Detector
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Supervised Learning
Supervised Learning
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Computer Vision Goal
Computer Vision Goal
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Why Data is Key in Computer Vision
Why Data is Key in Computer Vision
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Machine Learning in Computer Vision
Machine Learning in Computer Vision
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Image Acquisition
Image Acquisition
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Pre-processing
Pre-processing
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Detection/Segmentation
Detection/Segmentation
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Feature Extraction
Feature Extraction
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Segmentation Purpose
Segmentation Purpose
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Feature
Feature
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Feature Selection
Feature Selection
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High-Level Processing
High-Level Processing
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Object Detection
Object Detection
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Computer Vision Applications
Computer Vision Applications
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Computer Vision Tasks
Computer Vision Tasks
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Content-based Image Retrieval
Content-based Image Retrieval
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Optical Character Recognition (OCR)
Optical Character Recognition (OCR)
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What is a 2D Cartesian Coordinate System?
What is a 2D Cartesian Coordinate System?
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What is a 3D Cartesian Coordinate System?
What is a 3D Cartesian Coordinate System?
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What is Color Depth?
What is Color Depth?
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Study Notes
Computer Vision
- Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from images, videos, and other visual inputs.
- It allows computers to "see," observe, and understand.
- Computer vision's goal is to simulate the human vision system.
Computer Vision vs. Computer Graphics
- Computer vision recognizes environments through images.
- Input: Images
- Output: Descriptions (e.g., locations of objects, dimensions).
- Computer graphics generates images synthetically from descriptive information.
- Input: Descriptions (e.g., locations of objects)
- Output: Images
What About Image Processing?
- Image processing manipulates images.
- Input: Images
- Output: Images
- Often a primary step in vision systems (e.g., enhancing quality).
- Sometimes hard to distinguish from computer vision.
How Does Computer Vision Work?
- Computer vision needs a lot of data.
- It runs analyses repeatedly to identify distinctions and recognize images.
- To recognize tires, for example, it needs lots of images.
- Machine learning enables self-teaching through algorithmic models.
- The computer learns to distinguish images by itself.
Computer Vision Pipeline
- Image Acquisition: Sensors (e.g., cameras, radar) produce digital images.
- Pre-processing: Enhances image quality (e.g., reduces noise).
- Segmentation: Divides the image into regions of similarity.
- Feature Extraction & Selection: Extracts meaningful features (e.g., edges).
- High-level Processing: Further analysis and classification based on features.
Coordinates and Images
- Coordinate systems: 2D (two real numbers) and 3D (three real numbers).
- Color Depth: The number of bits allocated to store color values of a pixel. - More bits mean more possible colors. - 1 bit: 2 colors - 2 bits: 4 colors - 24 bits: 16 million colors
Digital Image Categories
- Binary: Two possible values (e.g., black, white).
- Grayscale: Shades of gray.
- Color: Combination of red, green, and blue.
- Multispectral: Beyond the visible spectrum.
Computer Vision Tasks
- Object Detection and Recognition: Detecting patterns within an image (e.g., red eyes, faces).
- Content-based Image Retrieval: Retrieving images from a database based on user queries.
- Optical Character Recognition (OCR): Converting handwritten text to digital format.
Computer Vision Applications
- Manufacturing: Ensuring accurate product placement on assembly lines.
- Visual Auditing: Assessing the condition of equipment and assets.
- Insurance: Classifying claim images into categories.
- Medical Imaging: Detecting tumors.
- Automotive Industry: Detecting objects for safety.
- Social Commerce: Finding similar homes or products.
- Social Listening: Tracking mentions about a company.
- Retail: Finding product prices online.
- Public Safety: Automated license plate reading.
- Education Finding educational material related to images.
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Description
Explore the fascinating field of computer vision, which enables systems to interpret visual information from images and videos. Learn about its applications, how it differs from computer graphics, and its relationship with image processing. This quiz will test your understanding of the fundamental concepts and workings of computer vision.