Fast Reservoir Parameter Estimation By Using Effect Of Principal Components Sensitivities And Discrete Cosine Transform

2011 ◽  
Author(s):  
Mohsen Dadashpour ◽  
Richard Wilfred Rwechungura ◽  
Jon Kleppe
Keyword(s):  
Parameter Estimation ◽  
Discrete Cosine Transform ◽  
Principal Components ◽  
Cosine Transform ◽  
Reservoir Parameter

Shape analysis of female torsos based on discrete cosine transform

2015 ◽  
Vol 27 (5) ◽  
pp. 677-691 ◽  
Author(s):  
Peng Li ◽  
Brian Corner ◽  
Steven Paquette
Keyword(s):  
Discrete Cosine Transform ◽  
Shape Analysis ◽  
Principal Components ◽  
Body Shape ◽  
Whole Body ◽  
Shape Variation ◽  
Linear Measurements ◽  
Body Scan ◽  
Cosine Transform ◽  
Content Type

Purpose – The purpose of this paper is to present results of shape analysis of female torso shape using the discrete cosine transform (DCT) from a three-dimensional (3D) whole body scan database. Design/methodology/approach – Torso shape is a central part of body shape and difficult to describe by linear measurements. In order to analyze body shape variation within a population the authors employed a DCT-based shape description method to compresses a dense 3D body scan surface into a small vector that preserves shape and removes size. The DCT-based shape descriptors of torso surfaces are further fed to principal component analysis (PCA) that decompose shape variation into constituent shape components. A visualization program was developed to observe principal components of torso shape and interpret their meanings. Findings – Extreme shapes of the first ten principal components summarize major shape variations and identify shapes that are difficult to capture with traditional anthropometric measurements. PCA results also help to find and retrieve similar shapes from a population-level database. Originality/value – Using the DCT for PCA of torso shape is a unique and original approach. It provides a basis for the description and classification of torso shape in 3D and the results from the shape analysis are potentially useful for designers of clothing and personal protective equipment.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

Fast odd discrete cosine transform algorithms

1990 ◽  
Vol 26 (8) ◽  
pp. 503
Author(s):  
S.C. Chan ◽  
K.L. Ho
Keyword(s):  
Discrete Cosine Transform ◽  
Cosine Transform
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