rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Emotion classification in images using Amazon's Rekognition API
  name:
    name part: Bossetta, Michael
    role:
      role term: academic.
  type of resource: moving image
  genre: videorecording
  origin info:
    place:
      place term: enk
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (00:30:53)) : sound, colour
  abstract: Michael Bossetta, PhD, assistant professor of Communication and Media at Lund University, discusses and demonstrates emotion classification in images with Amazon's Rekognition API using the Pykognition tool, including how to install, how the script works, how confidence thresholds work, validation, and limitations.
  table of contents: Chapter 1: Michael Bossetta Discusses Image Analysis, Emotion Research, and Facial Action Codingicon angle down -- Chapter 2: Michael Bossetta Provides an Overview of Amazon's Recognition API and the Pykognition Toolicon angle down -- Chapter 3: Michael Bossetta Demonstrates How to Install the Pykognition Toolicon angle down -- Chapter 4: Michael Bossetta Explains the Script to Run Rekognition and Pykognitionicon angle down -- Chapter 5: Michael Bossetta Demonstrates Working With Images and Confidence Thresholds Using Pykognition and Rekognition Softwareicon angle down -- Chapter 6: Michael Bossetta Discusses the Pykognition Tool Spreadsheeticon angle down -- Chapter 7: Michael Bossetta Discusses Validation and Limitations of the Pykognition Tool and the Rekognition API for Emotion Classification in Images.
  target audience: specialized
  note: Academic, Michael Bossetta PhD.
  note: Includes bibliographical references and index.
  note: Closed-captioned in English.
  subject:
    topic: Image analysis
    topic: Computer programs
  subject:
    topic: Gesture recognition (Computer science)
  subject:
    topic: Human face recognition (Computer science)
  subject:
    topic: Analyse d'images
    topic: Logiciels
  subject:
    topic: Reconnaissance faciale (Informatique)
  subject:
    topic: Gesture recognition (Computer science)
  subject:
    topic: Human face recognition (Computer science)
  classification: TA1650 .E46 2022
  classification: 006.42
  identifier: 9781529604412
  identifier: 1529604419
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 220704
    record change date: 20240923170444.9
    record identifier: in00005496080
    language of cataloging:
      language term: eng