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Questions and Answers

What is the primary purpose of machine vision?

  • To enhance human eyesight through technology
  • To enable machines to interact with their environment like humans (correct)
  • To process spoken information for decision making
  • To replace human vision completely

Which of these is NOT mentioned as an application of machine vision?

  • Audio processing in telecommunications (correct)
  • Self-localization in robotics
  • Anomalous event detection in surveillance
  • Facial recognition in biometrics

Which skill is emphasized for students in relation to machine vision models?

  • Understanding complex mathematical theories
  • Writing papers on the history of machine vision
  • Conducting scientific research without practical applications
  • Implementing models using popular programming languages and frameworks (correct)

Which algorithmic task is NOT part of machine vision as described?

<p>Encryption (C)</p> Signup and view all the answers

What is included in the recommended learning outcomes for machine vision?

<p>Understanding algorithms and their applications (C)</p> Signup and view all the answers

Which of the following best describes 'human-machine interface' in machine vision?

<p>Natural interaction in applications like video games (B)</p> Signup and view all the answers

Which contact detail for the module leader is provided?

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

What should students do to prepare for their coursework and exams in machine vision?

<p>Revise their PowerPoint presentations before and after class (A)</p> Signup and view all the answers

What is one of the primary uses of machine vision in smart rooms?

<p>Assistance to elderly people (C)</p> Signup and view all the answers

Which block of a machine vision system is responsible for improving image quality?

<p>Low level image processing (C)</p> Signup and view all the answers

What challenge is associated with images in machine vision due to their high dimensionality?

<p>They cannot represent 3D structures. (A)</p> Signup and view all the answers

Which technique is NOT typically part of the feature extraction block in a vision system?

<p>Neural networks (C)</p> Signup and view all the answers

What is the ultimate goal of a decision-making block in a machine vision system?

<p>Deciding based on extracted features (A)</p> Signup and view all the answers

What type of technology is typically employed for estimating the depth of each point in a scene in machine vision?

<p>Pose estimation techniques (B)</p> Signup and view all the answers

In the context of machine vision, what does 'preprocessing' primarily refer to?

<p>Reducing background noise and improving image quality. (A)</p> Signup and view all the answers

Which of the following is a common area of application for machine vision in the entertainment industry?

<p>Augmented reality (AR) and special effects (D)</p> Signup and view all the answers

What is the main purpose of detecting edges in an image?

<p>To determine the boundaries between separate objects (A)</p> Signup and view all the answers

Which of the following describes an interest point?

<p>A local neighborhood rich in information and stable with respect to changes (A)</p> Signup and view all the answers

Which compression method is an example of lossy compression?

<p>Discrete Cosine Transform (A)</p> Signup and view all the answers

What is the primary goal of image segmentation?

<p>To categorize each pixel into predefined categories (C)</p> Signup and view all the answers

Which feature is NOT typically associated with edge detection?

<p>Uniform color regions (D)</p> Signup and view all the answers

What plays a critical role in background segmentation using motion properties?

<p>Differentiating between moving and stationary objects (A)</p> Signup and view all the answers

What aspect of textures aids in object recognition?

<p>Appearance pattern (C)</p> Signup and view all the answers

Which format is typically used for lossless compression in imagery?

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

What does object localization achieve?

<p>Identifies the precise location of objects (D)</p> Signup and view all the answers

What distinguishes instance segmentation from semantic segmentation?

<p>Only instance segmentation differentiates multiple objects of the same category (C)</p> Signup and view all the answers

Which of the following best describes panoptic segmentation?

<p>It combines semantic and instance segmentation (C)</p> Signup and view all the answers

What is a key requirement for action recognition in computer vision?

<p>Analysis of sequences over time (C)</p> Signup and view all the answers

What is the main purpose of object detection in computer vision?

<p>To identify the location and category of objects in an image (C)</p> Signup and view all the answers

Which task does NOT fall under the classification of semantic segmentation?

<p>Providing unique labels for multiple object instances (B)</p> Signup and view all the answers

Convolutional Neural Networks (CNN) are primarily used for which of the following tasks?

<p>Object and action recognition (D)</p> Signup and view all the answers

What is involved in the process of image preprocessing?

<p>Filtering, resizing, and enhancing images prior to analysis (B)</p> Signup and view all the answers

Which of the following does NOT accurately describe action classification?

<p>It relies on static image analysis to classify actions (C)</p> Signup and view all the answers

Which task specifically involves evaluating multiple objects at once?

<p>Object detection (D)</p> Signup and view all the answers

Flashcards

Object Recognition

Identifying and classifying objects in an image.

Action Recognition

Identifying actions performed by objects in images or videos.

Object Detection

Identifying the location of objects within an image.

Face Detection

Identifying faces in images.

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Semantic Segmentation

Classifying each pixel in an image into different categories.

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Instance Segmentation

Identifying and labeling each instance or individual object in an image.

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Panoptic Segmentation

Combining semantic and instance segmentation to identify and label objects while also segmenting the background.

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Object Localization

Determining the precise location of objects in an image, not categorizing them.

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Image Classification

Categorizing images into different classes or labels.

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Convolutional Neural Networks (CNNs)

A type of neural network that's effective for image recognition tasks.

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Machine Vision

Using machines to process visual information, enabling them to interact with the world like humans.

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Visual Data in MV

The input images or video frames that machine vision systems use for analysis.

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MV Applications

Real-world uses of machine vision, encompassing areas like robotics, surveillance, and biometrics.

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Robotics & MV

Machine vision helps robots navigate, map environments and perform tasks through visual input.

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Surveillance & MV

Identifying unusual events or situations in videos using machine vision analysis.

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Biometrics & MV

Using machine vision to identify people through facial recognition and other visual characteristics.

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MV Algorithms

Methods for processing visual information like classification or object detection.

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MV Model Development

Building and training machine vision models using programming languages and frameworks. (e.g., Python, TensorFlow).

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Machine Vision

Using computers to 'see' and understand images, allowing for tasks like object recognition and decision making.

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High Dimensional Data (Image)

Images are complex data sets, often represented as enormous numbers (e.g., 100x100 pixel image=10,000 data points).

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Preprocessing (Image)

Improving image quality for easier analysis, such as reducing noise or adjusting brightness.

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Feature Extraction

Identifying important parts (features) of an image for analysis, like edges or patterns.

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Image Processing Blocks

Steps in machine vision systems (e.g., preprocessing, feature extraction, decision making).

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Decision/Classification

The final goal of machine vision, including recognizing objects, granting access, or estimating values.

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Low-Level Feature Extraction

Identifying important characteristics of an image. Examples include edge detection and calculations of gradients within an image.

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3D Information

2D images lack the depth information required to fully understand the structure of what's being observed.

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Image Features

Characteristics (like points of interest, edges, textures) used to analyze images and make decisions about the scene.

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Interest Points

Key locations in an image that contain a lot of important information.

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Edge Detection

A method to find areas in an image with sharp changes in color or brightness.

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Image Compression

Reducing the storage space needed for images and videos.

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Lossy Compression

Image compression that loses some information to reduce file size.

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Lossless Compression

Image compression that maintains all the original information.

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Image Segmentation

Dividing an image into different parts based on their characteristics (like brightness or color).

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Background Segmentation

Identifying and separating the background from the foreground in an image or video.

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