Relationship Between Facial Recognition, Color Spaces, and Basic Image Manipulation

2020 ◽  
pp. 844-853
Author(s):  
Giuliano Millan ◽  
Anas Salah Eddin ◽  
Jinjun Xiong ◽  
Wen-Mei Hwu ◽  
Mohamed El-Hadedy
Keyword(s):  
Facial Recognition ◽  
Image Manipulation ◽  
Color Spaces ◽  
Basic Image

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

Interacting in 3D Space: Comparison of a 3D Two-handed Interface to a Keyboard-and-mouse Interface for Medical 3D Image Manipulation

2009 ◽  
Author(s):  
F. Jacob Seagull ◽  
Peter Miller ◽  
Ivan George ◽  
Paul Mlyniec ◽  
Adrian Park
Keyword(s):  
Image Manipulation ◽  
3D Image ◽  
3D Space

PRNU-based Image Manipulation Localization with Discriminative Random Fields

2017 ◽  
Vol 2017 (7) ◽  
pp. 113-120 ◽  
Author(s):  
Sujoy Chakraborty ◽  
Matthias Kirchner
Keyword(s):  
Random Fields ◽  
Image Manipulation

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Fusion Based Edge Detection and Segmentation of Color Spaces

2017 ◽  
Author(s):  
Prof. S. H. Jawale ◽  
Prof. A. B. Bavaskar
Keyword(s):  
Edge Detection ◽  
Color Spaces
Sign in / Sign up

Export Citation Format

Share Document