Cover Image

Probabilistic and biologically inspired feature representations /

Bibliographic Details
Main Author: Felsberg, Michael (Author)
Corporate Author: Morgan & Claypool Publishers
Format: eBook
Language:English
Published: [San Rafael, California] : Morgan & Claypool, 2018.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on computer vision ; # 16.
Subjects:
Computer vision.
Pattern recognition systems.
channel representation
channel-coded feature map
feature descriptor
histogram
signature
Electronic books.
Online Access:Connect to the full text of this electronic book (PDF)
Table of Contents:
  • 1. Introduction
  • 1.1 Feature design
  • 1.2 Channel representations: a design choice
  • 2. Basics of feature design
  • 2.1 Statistical properties
  • 2.2 Invariance and equivariance
  • 2.3 Sparse representations, histograms, and signatures
  • 2.4 Grid-based feature representations
  • 2.5 Links to biologically inspired models
  • 3. Channel coding of features
  • 3.1 Channel coding
  • 3.2 Enhanced distribution field tracking
  • 3.3 Orientation scores as channel representations
  • 3.4 Multi-dimensional coding
  • 4. Channel-coded feature maps
  • 4.1 Definition of channel-coded feature maps
  • 4.2 The HOG descriptor as a CCFM
  • 4.3 The SIFT descriptor as a CCFM
  • 4.4 The SHOT descriptor as a CCFM
  • 5. CCFM decoding and visualization
  • 5.1 Channel decoding
  • 5.2 Decoding based on frame theory
  • 5.3 Maximum entropy decoding
  • 5.4 Relation to other de-featuring methods
  • 6. Probabilistic interpretation of channel representations
  • 6.1 On the distribution of channel values
  • 6.2 Comparing channel representations
  • 6.3 Comparing using divergences
  • 6.4 Uniformization and copula estimation
  • 7. Conclusions
  • Bibliography
  • Author's biography
  • Index.