Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring

Abstract

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2015
Accession Number
AD1010906

Entities

People

  • Robert J. Dibiano

Organizations

  • Louisiana State University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Computer Vision
  • Databases
  • Detectors
  • Dimensionality Reduction
  • Feature Extraction
  • Image Recognition
  • Information Science
  • Kalman Filters
  • Machine Learning
  • Mobile Phones
  • Neural Networks
  • Supervised Machine Learning
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Defense Acquisition Program Management
  • Exercise and Sports Science.