In this paper an evaluation of image keypoints detectors and descriptors is presented
when used for building panoramic image. The descriptors: (SIFT, SURF, BRIEF, ORB,
BRISK, and FREAK) were discussed, when used with the appropriate keypoints detec
tors
on database taken indoors by RGB-D camera. Crosscheck and RANSAC (RANdom
Sample Consensus) algorithms were used to find transform matrix between images. The
speed of keypoints detectors and descriptors, the matching speed, the average of extracted
keypoints, recall and precision were investigated. Oxford dataset was used to find the best
descriptor for dealing with rotation and illumination changes that might occur due to
changes in illumination angle.
The obtained results showed that SIFT was the keypoint descriptor with the highest
performance in non-real time applications. The SURF/BRISK was the best
detector/descriptor which can be used in real time applications with comparable SIFT's
results.
The purpose of this article is to shed light on the mechanism
and the procedures of a neuro-fuzzy controller that classifies an
input face into any of the four facial expressions, which are
Happiness, Sadness, Anger and Fear. This program works
a
ccording to the facial characteristic points-FCP which is taken
from one side of the face, and depends, in contrast with some
traditional studies which rely on the whole face, on three
components: Eyebrows, Eyes and Mouth.
