Robust motion correction for myocardial T1 and extracellular volume mapping by principle component analysis-based groupwise image registration

2017 ◽  
Vol 47 (5) ◽  
pp. 1397-1405 ◽  
Author(s):  
Qian Tao ◽  
Pieternel van der Tol ◽  
Floris F. Berendsen ◽  
Elisabeth H.M. Paiman ◽  
Hildo J. Lamb ◽  
...  
Keyword(s):  
Image Registration ◽  
Principle Component Analysis ◽  
Motion Correction ◽  
Extracellular Volume ◽  
Component Analysis ◽  
Principle Component ◽  
Myocardial T1

scholarly journals Satellite Image Registration and Image Fusion by using Principle Component Analysis

2018 ◽  
Vol 7 (2.19) ◽  
pp. 106
Author(s):  
Gandla Maharnisha ◽  
Gandla Roopesh Kumar ◽  
R Arunraj
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Image Fusion ◽  
Digital Image Processing ◽  
Spatial Resolution ◽  
Digital Image ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Principle Component ◽  
Spectral Degradation

This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability. 

Feature extraction of underwater images using principle component analysis with image registration

2021 ◽  
Author(s):  
M. Islabudeen ◽  
P. Vigneshwaran ◽  
G. Sindhu Madhuri ◽  
B. Muthu Kumar ◽  
J. Ragaventhiran ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Principle Component

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

2003 ◽  
Vol 26 (6) ◽  
pp. 681-682
Author(s):  
Harry Howard
Keyword(s):  
Independent Component Analysis ◽  
Recent Work ◽  
Cognitive Neuroscience ◽  
Principle Component Analysis ◽  
Pattern Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Principle Component ◽  
Complementary Methods ◽  
Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

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