• No results found

Principal component analysis of image gradient orientations for face recognition

N/A
N/A
Protected

Academic year: 2020

Share "Principal component analysis of image gradient orientations for face recognition"

Copied!
6
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Fig. 1.(a)-(b) An image pair used in our experiment, (c) Image-based random number generator: histogram of 40,000 gradient orientationdifferences and (d) Histogram of 40,000 samples drawn from Matlab’srandom number generator.
Fig. 2 (a)-(b) show an image pair where P2 is the part of the
Fig. 3.The eigen-spectrum of natural images and the eigen-spectrum ofsamples drawn from Matlab’s random number generator.
Fig. 5.PCA-based reconstruction of gradient orientations. (a)-(b) Orig-orientations used in version 2 of our experiment
+2

References

Related documents

Table depicts Cumulative Abnormal Returns (CAR) for alternative asset pricing models (APMs) over 12, 36 and 60 month horizons. A firm’s monthly abnormal return conditioned by an APM

F1119 Opioid abuse with unspecified opioid-induced disorder OR F1129 Opioid dependence with unspecified opioid-induced disorder OR F1199 Opioid use, unspecified with

In this section, first we will illustrate the general solving procedure that has been applied to solve multi-objective optimization problems with PDE constraints, then we will

The incident beam momentum spread is analyzed on the target with a dispersion matching condition to a central focal plane, and the contribution from the momentum spread is

acknowledged said Instrument to bo tho free act and deed of said corporation, by each of them voluntarily executed as their free act and deed.. Tor the uses and purposes

The current study proposed and tested a model that examined how selected e-retailer attributes and behaviours, namely, affordable delivery cost, perceived transaction protection

Three optimisations steps are performed for each user type using separate multiple-criteria deci- sion models: (1) McdMD – MDM selection model, (2) McdD – dimensions optimisation

Hypothesis 1: Small firms networks evolve from identity based networks (strong ties) in early stage to calculative networks (weak ties) in the growth phase.. The Conceptual