Computer Vision Overview
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Questions and Answers

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.

True (A)

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.

<p>8-bit color</p> Signup and view all the answers

Match the following types of images with their characteristics:

<p>Binary Images = Only two colors: black and white Grayscale Images = Shades of gray, no color Color Images = Contains multiple colors 24-Bit Color Images = Supports over 16 million colors</p> Signup and view all the answers

What is the primary goal of computer vision?

<p>To derive meaningful information from visual inputs (C)</p> Signup and view all the answers

Computer vision is the same as computer graphics.

<p>False (B)</p> Signup and view all the answers

Name one application of computer vision.

<p>Autonomous vehicles</p> Signup and view all the answers

In the context of image processing, the ______ filter is commonly used for noise reduction and edge detection.

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

Match the following image processing techniques with their descriptions:

<p>Erosion = Removing pixels around the boundaries of objects Dilation = Adding pixels to the boundaries of objects Thresholding = Separating pixels based on intensity values Region growing = Combining similar neighboring pixels into larger regions</p> Signup and view all the answers

Which of the following is NOT a type of image category?

<p>Vector Images (B)</p> Signup and view all the answers

Image segmentation involves grouping pixels into regions of similarity.

<p>True (A)</p> Signup and view all the answers

What is a common use of edge detectors in computer vision?

<p>Identifying object boundaries</p> Signup and view all the answers

What is a feature in computer vision?

<p>A measurable characteristic useful for recognition (C)</p> Signup and view all the answers

Corners are not considered points of interest in an image.

<p>False (B)</p> Signup and view all the answers

What is the purpose of feature selection in the computer vision pipeline?

<p>To reduce dimensionality with the least amount of information loss.</p> Signup and view all the answers

In manufacturing, computer vision is used to ensure that products are being positioned correctly on the ______.

<p>assembly line</p> Signup and view all the answers

Which of the following is an example of a computer vision application in the automotive industry?

<p>Object detection for safety (A)</p> Signup and view all the answers

Visual auditing in computer vision involves checking for visual compliance or deterioration.

<p>True (A)</p> Signup and view all the answers

Name one task that involves object detection in computer vision.

<p>Face recognition</p> Signup and view all the answers

Match the computer vision tasks with their descriptions:

<p>Social commerce = Finding similar homes that are for sale Object detection = Detecting patterns within the image Medical image processing = Detecting tumors Public safety = Automated license-plate reading</p> Signup and view all the answers

What is the first step in a typical computer vision pipeline?

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

Segmentation is an optional step in a computer vision pipeline.

<p>False (B)</p> Signup and view all the answers

Which of the following is a method used for feature matching?

<p>Euclidean Distance (D)</p> Signup and view all the answers

What type of data needs to be processed to help a computer recognize a tire?

<p>Images of tires and tire-related items</p> Signup and view all the answers

Computer vision and computer graphics are the same field.

<p>False (B)</p> Signup and view all the answers

The process of enhancing contrast or reducing noise in images is called ______.

<p>pre-processing</p> Signup and view all the answers

Match each phase of the computer vision pipeline with its function:

<p>Image acquisition = Creating a digital image from sensors Pre-processing = Preparing an image for analysis Detection/segmentation = Identifying relevant image points Feature extraction = Extracting specific characteristics from images</p> Signup and view all the answers

What is the primary input and output of computer vision?

<p>Input: images; Output: description of environments.</p> Signup and view all the answers

Image processing involves the manipulation of __________.

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

What allows a computer to learn about visual data without explicit programming?

<p>Algorithmic models (D)</p> Signup and view all the answers

Image segmentation helps in selecting regions of interest in an image.

<p>True (A)</p> Signup and view all the answers

Match the following distance metrics with their descriptions:

<p>Euclidean = Straight-line distance in Cartesian coordinates Manhattan = Distance measured along axes at right angles Chessboard = Distance measured as maximum coordinate difference Cosine = Measure of similarity between two non-zero vectors</p> Signup and view all the answers

What is the main objective of feature extraction in computer vision?

<p>To extract specific characteristics from images</p> Signup and view all the answers

Which classification technique is categorized under supervised learning?

<p>k NEAREST NEIGHBOR (C)</p> Signup and view all the answers

Image processing techniques are seldom used in computer vision systems.

<p>False (B)</p> Signup and view all the answers

Describe what it means for computer graphics to be the opposite operation of computer vision.

<p>Computer graphics generates images from descriptions, whereas computer vision interprets images to provide descriptions.</p> Signup and view all the answers

Flashcards

Computer Vision

A field of AI that enables computers to understand and make decisions from visual inputs like images and videos.

Image Processing

A method of manipulating digital images to extract useful information.

Linear Filter

A filter that applies a weighted average to a pixel's neighborhood.

Median Filter

A non-linear filter that replaces a pixel value with the median of its neighborhood.

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Edge Detector

A technique to identify edges in an image.

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Image Morphology

A set of image processing operations based on shapes and structures.

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Image Segmentation

Dividing an image into meaningful regions or segments.

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Thresholding

A method of image segmentation, dividing pixels based on a threshold value.

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Computer Graphics

Creating images from descriptions. Turning information into visual form.

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Feature Matching

Identifying similar features in different images to find correspondences.

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Harris/Plessy Corner Detector

Algorithm to detect corners in images.

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Supervised Learning

Machine learning method using labeled data to train models.

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Computer Vision Goal

To enable computers to understand and interpret images and videos, similar to how humans do.

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Why Data is Key in Computer Vision

Computer vision systems require massive amounts of data to learn patterns and recognize objects accurately.

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Machine Learning in Computer Vision

Using algorithms that allow computers to learn from data without explicit programming, enabling them to identify objects in images.

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Image Acquisition

The initial step in computer vision where a digital image is captured by sensors.

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Pre-processing

Preparing an image for further processing by enhancing its quality or removing unwanted elements.

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Detection/Segmentation

Identifying and isolating specific areas of interest within an image.

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Feature Extraction

Extracting relevant information from an image, such as edges, shapes, or colors, to be used for analysis.

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Segmentation Purpose

Helps to identify important parts of a picture and isolate them for further analysis.

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Feature

A measurable characteristic of input data helpful for image recognition, like edges, corners, or changes in brightness.

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Feature Selection

Choosing a subset of features that best represent the information in an image, reducing redundancy and complexity.

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High-Level Processing

Analyzing the extracted features to classify objects, estimate their size, or identify other characteristics.

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Object Detection

Identifying specific objects within an image.

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Computer Vision Applications

Practical uses of computer vision, such as automating tasks in manufacturing, healthcare, or retail.

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Computer Vision Tasks

Specific problems addressed by computer vision, like object detection, recognition, or image analysis.

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Content-based Image Retrieval

Finding images from a database based on the image's visual content like colors, shapes, and textures, without using keywords or descriptions.

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Optical Character Recognition (OCR)

Converting handwritten or printed text into a digital format that can be edited and searched.

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What is a 2D Cartesian Coordinate System?

A system that uses two perpendicular lines (x-axis and y-axis) to represent points on a flat surface. Each point is defined by its coordinates (x, y).

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What is a 3D Cartesian Coordinate System?

A system that uses three perpendicular lines (x-axis, y-axis, and z-axis) to represent points in three-dimensional space. Each point is defined by its coordinates (x, y, z).

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What is Color Depth?

The number of bits used to represent the color of a pixel in a digital image. Higher color depth means more possible colors and smoother gradients.

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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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Computer Vision - Lecture 1 - Introduction Overview PDF

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.

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