BIBFRAME Work

Title

On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities

Type

  • Monograph
  • Text

Contribution

Spehr, Jens (author)

Subject

Language

Classification

Content

Summary

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.

Table of Contents

Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.

Authorized Access Point

Spehr, Jens. On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities

Admin Metadata

  • Date: 2026-09-11T20:21:04.038687+00:00
  • Agent: cbc
  • Status: c
  • Derived from: 21770637

Admin Metadata

  • Date: 2026-09-11
  • Cataloger id: Unknown
  • Description level: http://id.loc.gov/ontologies/bibframe-2-6-0/
  • Description language: eng
  • Description authentication: pcc

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