Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Title: An Analysis of Mammalian Visual System, Statistical Structures of Natural Scen es and Scale-invariant, Self-similar Transforms.


Presented by: Gokce Dane


Abstract: Natural images contain some characteristic statistical regularity that set them apart from purely random images. Understanding these regularities enable natural images to be coded more efficiently. Suggesting that a good objective for an efficie nt coding of natural scenes is to maximize the sparseness of the representation, it c an be shown that wavelet-like transforms are very effective in coding natural images, since they can produce sparse, informative representation of natural scenes.

In this study, we look at the question that why our visual system uses a wavelet-like strategy to represent the visual environment. We show that natural scenes are approx imately scale invariant with regards to their power and their phase spectra. Principa lly because of their phase spectra, wavelet-like transforms are capable of producing sparse, informative representation of these images. It is suggested that self-similar codes like wavelet are effective for many natural phenomena since these phenomena sh ow similar structures to those in natural scenes.