Computer Vision and Image Processing Quiz
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Questions and Answers

Computer graphics and computer vision both produce images from data.

False

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.

Supervised

Match the following machine learning algorithms with their type:

<p>Naïve Bayes = Supervised Learning k Nearest Neighbor = Supervised Learning K-Means Clustering = Unsupervised Learning Principal Component Analysis = Unsupervised Learning</p> Signup and view all the answers

Which of the following is a common similarity measure used in feature matching?

<p>All of the above</p> Signup and view all the answers

Image processing solely focuses on analyzing the content of images.

<p>False</p> Signup and view all the answers

Name one key difference between computer vision and image processing.

<p>Computer vision interprets images to extract information, while image processing manipulates images directly.</p> Signup and view all the answers

Which of the following best represents the main focus of computer vision?

<p>To allow computers to process and derive information from visual inputs</p> Signup and view all the answers

Computer vision can be considered a subset of artificial intelligence.

<p>True</p> Signup and view all the answers

What are the primary tasks of computer vision?

<p>Deriving information from visual inputs, making recommendations, and taking actions based on that information.</p> Signup and view all the answers

The process of grouping pixels into similar regions in computer vision is known as __________.

<p>image segmentation</p> Signup and view all the answers

Match the following computer vision tasks with their corresponding descriptions:

<p>Feature Detection = Identifying points of interest in images Image Logic Operations = Manipulating images using logical operations Image Morphology = Transforming images based on their structures Machine Learning Algorithms = Using data-driven approaches to improve vision tasks</p> Signup and view all the answers

What distinguishes computer vision from computer graphics?

<p>Computer vision interprets visual data, whereas computer graphics creates visual content.</p> Signup and view all the answers

Distance metrics are unnecessary in feature matching methods within computer vision.

<p>False</p> Signup and view all the answers

Name one type of non-linear filter used in image processing.

<p>Median filter</p> Signup and view all the answers

What is the first step in the computer vision pipeline?

<p>Image acquisition</p> Signup and view all the answers

Machine learning enables computers to learn from programming alone without needing data.

<p>False</p> Signup and view all the answers

What is the main purpose of feature extraction in the computer vision pipeline?

<p>To extract important image features for further analysis.</p> Signup and view all the answers

The process of grouping pixels with similar characteristics in an image is called ______.

<p>segmentation</p> Signup and view all the answers

Which of the following is a role of algorithms in machine learning for computer vision?

<p>Enable the machine to learn independently</p> Signup and view all the answers

Match the following terms with their descriptions:

<p>Image acquisition = Producing digital images using sensors Pre-processing = Enhancing image quality before analysis Segmentation = Identifying relevant regions of an image Feature extraction = Extracting significant attributes from an image</p> Signup and view all the answers

Computer vision only works with two-dimensional images.

<p>False</p> Signup and view all the answers

Name a method commonly used for detecting objects in computer vision.

<p>Segmentation</p> Signup and view all the answers

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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Related Documents

Computer Vision - Lecture Notes PDF

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.

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