Podcast
Questions and Answers
Computer graphics and computer vision both produce images from data.
Computer graphics and computer vision both produce images from data.
False (B)
What is the role of feature matching in computer vision?
What is the role of feature matching in computer vision?
To identify corresponding features between different images.
In machine learning, __________ learning uses labeled data to train models.
In machine learning, __________ learning uses labeled data to train models.
Supervised
Match the following machine learning algorithms with their type:
Match the following machine learning algorithms with their type:
Which of the following is a common similarity measure used in feature matching?
Which of the following is a common similarity measure used in feature matching?
Image processing solely focuses on analyzing the content of images.
Image processing solely focuses on analyzing the content of images.
Name one key difference between computer vision and image processing.
Name one key difference between computer vision and image processing.
Which of the following best represents the main focus of computer vision?
Which of the following best represents the main focus of computer vision?
Computer vision can be considered a subset of artificial intelligence.
Computer vision can be considered a subset of artificial intelligence.
What are the primary tasks of computer vision?
What are the primary tasks of computer vision?
The process of grouping pixels into similar regions in computer vision is known as __________.
The process of grouping pixels into similar regions in computer vision is known as __________.
Match the following computer vision tasks with their corresponding descriptions:
Match the following computer vision tasks with their corresponding descriptions:
What distinguishes computer vision from computer graphics?
What distinguishes computer vision from computer graphics?
Distance metrics are unnecessary in feature matching methods within computer vision.
Distance metrics are unnecessary in feature matching methods within computer vision.
Name one type of non-linear filter used in image processing.
Name one type of non-linear filter used in image processing.
What is the first step in the computer vision pipeline?
What is the first step in the computer vision pipeline?
Machine learning enables computers to learn from programming alone without needing data.
Machine learning enables computers to learn from programming alone without needing data.
What is the main purpose of feature extraction in the computer vision pipeline?
What is the main purpose of feature extraction in the computer vision pipeline?
The process of grouping pixels with similar characteristics in an image is called ______.
The process of grouping pixels with similar characteristics in an image is called ______.
Which of the following is a role of algorithms in machine learning for computer vision?
Which of the following is a role of algorithms in machine learning for computer vision?
Match the following terms with their descriptions:
Match the following terms with their descriptions:
Computer vision only works with two-dimensional images.
Computer vision only works with two-dimensional images.
Name a method commonly used for detecting objects in computer vision.
Name a method commonly used for detecting objects in computer vision.
Flashcards
Computer Vision
Computer Vision
Recognizing environments through images to help machines see.
Computer Graphics
Computer Graphics
Creating images from descriptions of objects.
Image Processing
Image Processing
Manipulating images.
Harris/Plessy Corner Detector
Harris/Plessy Corner Detector
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SIFT Detectors
SIFT Detectors
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Euclidean Distance
Euclidean Distance
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Manhattan Distance
Manhattan Distance
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Feature Matching
Feature Matching
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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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Machine Learning
Machine Learning
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Image Data
Image Data
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Computer Vision Pipeline
Computer Vision Pipeline
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Grayscale Image
Grayscale Image
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Edge Detection
Edge Detection
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Morphological Operations
Morphological Operations
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Image Segmentation
Image Segmentation
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Thresholding
Thresholding
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Gaussian Filter
Gaussian Filter
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Study Notes
Computer Vision Overview
- Computer vision is a field of artificial intelligence (AI).
- It enables computers and systems to derive meaningful information from visual inputs (images, videos).
- It allows computers to "see," observe, and understand visual information.
Computer Vision vs. Computer Graphics
- Computer vision recognizes environments using images.
- Input: Images.
- Output: Description (e.g., object locations, dimensions).
- Computer graphics generates synthetic images from described information.
- Input: Description.
- Output: Images.
Computer Vision Pipeline
- Image acquisition: digital images from sensors (cameras, radar, etc.).
- Pre-processing: essential steps to prepare the image (noise reduction, contrast enhancement).
- Segmentation: partitioning the image into regions of similarity.
- Feature extraction: identifying measurable characteristics (edges, corners).
- Feature selection: prioritizing relevant features.
- High-level processing: classifying objects and estimating properties.
Computer Vision Tasks
- Object detection and recognition: identifying and classifying patterns in images.
- Face recognition: identifying and classifying human faces.
- Content-based image retrieval: finding images based on their content (color, shape).
- Optical character recognition (OCR): converting handwritten text to digital text.
Image Processing
- Image processing manipulates images to enhance quality or prepare for analysis.
- Input: Images.
- Output: Images.
- Image processing techniques are often used as steps in computer vision systems.
Filters
- Linear filters (e.g., uniform, triangular, Gaussian): reduce noise by averaging pixel values.
- Non-linear filters (e.g., median, max, min): suppress certain types of noise and preserve edges.
Coordinates and Images
- 2D coordinates: two numbers specify a location on a plane.
- 3D coordinates: three numbers specify a location in 3D space.
- Right-handed and left-handed coordinate systems are used in 3D.
- Digital images represent an image as a finite set of digital values, called picture elements or pixels.
- Color depth: the number of bits to store a color value of a pixel.
Image Categories
- Binary images: only two values (black or white).
- Grayscale images: shades of gray.
- Color images: RGB color space (combination of red, green, and blue).
- Multispectral images: capture intensities outside the visible portion of the electromagnetic spectrum.
Digital Filters
- An image can be filtered in the spatial or frequency domain.
- Spatial filtering involves convolving the image with a filter kernel.
- Convolution: a mathematical operation fundamental to many image processing operators.
Image Noise
- Noise degrades image quality (e.g., dust on lens).
- Smoothing operations can be used to reduce noise.
- Types of noise: salt and pepper, Gaussian.
Computer Vision Applications
- Manufacturing (correct product positioning).
- Visual auditing (detecting issues in equipment such as trucks, planes, etc.).
- Medical image processing (tumor detection).
- Automotive industry (object detection, parking assistance).
- Social commerce (finding similar homes based on image).
- Social listening (detecting product logos in social media).
- Retail (finding an item's price in various stores).
- Education (locating similar educational materials).
- Public safety (license plate reading).
Grading Policy
- Quizzes (practical): 10%.
- Assignments: 10%.
- Project: 10%.
- Midterm exam (theoretical): 30%.
- Final exam (theoretical): 40%.
- Total: 100%.
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Description
Test your knowledge of computer vision and image processing concepts. This quiz covers topics like distance metrics, feature matching, and the primary tasks in computer vision. Challenge yourself to distinguish between different algorithms and their applications in these fields.