Local spatio-temporal representation using the 3D shearlet transform

Autor: Damiano Malafronte, Francesca Odone, Ernesto De Vito
Rok vydání: 2017
Předmět:
Statistics and Probability
Theoretical computer science
Relation (database)
Exploit
Point of interest
Computer science
02 engineering and technology
01 natural sciences
Electronic mail
0202 electrical engineering
electronic engineering
information engineering

Radiology
Nuclear Medicine and imaging

0101 mathematics
Mathematics
Signal processing
Algebra and Number Theory
business.industry
Applied Mathematics
Statistics
SIGNAL (programming language)
Representation (systemics)
Signal Processing
Statistics
Probability and Uncertainty

Analysis
Video sequence
Pattern recognition
Probability and statistics
Base (topology)
010101 applied mathematics
Computational Mathematics
Shearlet
Probability and Uncertainty
020201 artificial intelligence & image processing
Artificial intelligence
Unsupervised clustering
business
Shearlet transform
Zdroj: 2017 International Conference on Sampling Theory and Applications (SampTA).
DOI: 10.1109/sampta.2017.8024409
Popis: In this work we address the problem of analyzing video sequences and of representing meaningful space-time points of interest by using the 3D shearlet transform. We introduce a local representation based on shearlet coefficients of the video, regarded as 2D+T signal. This representation turns out to be informative to understand the local spatio-temporal characteristics, which can be easily detected by an unsupervised clustering algorithm.
Databáze: OpenAIRE