scholarly journals Poisson Denoising for Astronomical Images

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Fahad Shamshad ◽  
M. Mohsin Riaz ◽  
Abdul Ghafoor
Keyword(s):  
Principal Component Analysis ◽  
Computational Efficiency ◽  
Color Space ◽  
Principal Component ◽  
Color Images ◽  
Temporal Correlations ◽  
Image Patches ◽  
Quantitative Manner ◽  
Simulation Results ◽  
Astronomical Images

A denoising scheme for astronomical color images/videos corrupted with Poisson noise is proposed. The scheme employs the concept of Exponential Principal Component Analysis and sparsity of image patches. The color space RGB is converted to YCbCr and K-means++ clustering is applied on luminance component only. The cluster centers are used for chromatic components to improve the computational efficiency. For videos, the information of both spatial and temporal correlations improves the denoising. Simulation results verify the significance of proposed scheme in both visual and quantitative manner.

Reliability Parameters Forecasting for Transmission Lines Based on Principal Component Regression

2013 ◽  
Vol 291-294 ◽  
pp. 2381-2386 ◽  
Author(s):  
Wen Xia Liu ◽  
Ji Kai Xu ◽  
Hong Yuan Jiang ◽  
Yong Tao Shen
Keyword(s):  
Principal Component Analysis ◽  
Transmission Lines ◽  
Principal Component Regression ◽  
Principal Component ◽  
Forecasting Accuracy ◽  
Factors Affecting ◽  
Reliability Parameters ◽  
Impacting Factors ◽  
Simulation Results

It is the foundation for evaluating the reliability of transmission lines to obtain and analyze the original reliability parameters. However, these parameters depend on long- term statistic and calculation. In the case of lacking such parameters in a new project , this paper proposes a method of Principal Component Analysis to obtain the principal component of the impacting factors ,in which various factors affecting reliability parameters are taken into account. Through this method, we can use PCR to obtain the failure rate of the unknown transmission lines on the base of the known credible lines’ rates. The simulation results show that the proposed approach possesses higher forecasting accuracy and provides references for the power system dispatching departments and transmission lines maintenance departments.

scholarly journals A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134828 ◽  
Author(s):  
Mahdi Maktabdar Oghaz ◽  
Mohd Aizaini Maarof ◽  
Anazida Zainal ◽  
Mohd Foad Rohani ◽  
S. Hadi Yaghoubyan
Keyword(s):  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Heuristic Search ◽  
Color Space ◽  
Principal Component ◽  
Component Analysis ◽  
Skin Detection ◽  
Analysis Technique

Near Field Acoustic Holography for Cyclostationary Sound Field and its Partial Source Decomposition Procedure

2005 ◽  
Vol 127 (6) ◽  
pp. 542-546 ◽  
Author(s):  
Quan Wan ◽  
W. K. Jiang
Keyword(s):  
Principal Component Analysis ◽  
Sound Pressure ◽  
Near Field ◽  
Sound Field ◽  
Principal Component ◽  
Acoustic Holography ◽  
Sound Sources ◽  
Decomposition Procedure ◽  
Spectrum Density ◽  
Simulation Results

The cyclostationary near field acoustic holography (NAH) technique is proposed to overcome the limitations of the current NAH in analyzing cyclostationary sound field. The proposed technique adopts the cyclic spectrum density as the reconstructed physical quantity, instead of the spectrum of sound pressure. Moreover, introducing the principal component analysis into the technique, a partial source decomposition procedure is suggested to decompose the sound field radiated by multiple sound sources into some incoherent partial fields. More information about cyclostationary sound field can be shown clearly on the hologram of the proposed technique than NAH can, which is validated by the simulation results.

scholarly journals Ekstraksi Ciri Orde Pertama dan Metode Principal Component Analysis untuk Mengidentifikasi Jenis Telur Ayam Kampung dan Ayam Arab

2019 ◽  
Vol 9 (2) ◽  
pp. 133
Author(s):  
Oky Dwi Nurhayati ◽  
Dania Eridani ◽  
Ajik Ulinuha
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Color Space ◽  
Principal Component ◽  
Component Analysis ◽  
Domestic Chicken ◽  
Image Size ◽  
First Order ◽  
Pca Method ◽  
Chicken Eggs

Chicken eggs become one of the animal proteins commonly used by people, especially in Indonesia. Eggs have high economic value and have diverse benefits and a high nutritional content. Visually to distinguish between domestic chicken eggs and arabic chicken eggs has many difficulties because physically the shape and color of eggs have similarities. This research was conducted to develop applications that were able to identify the types of domestic chicken eggs and Arab chicken eggs using the Principal Componenet Analysis (PCA) method and first order feature extraction. The application applies digital image processing stages, namely resizing image size, RGB color space conversion to HSV, contrast enhancement, image segmentation using the thresholding method, opening and region filling morphology operations, first order feature extraction and classification using the PCA method. The results of identification of types of native domestic chicken eggs and Arabic chicken eggs using the Principal Component Analysis method showed the results of 95% system accuracy percentage, consisting of 90% accuracy of success in the type of domestic chicken eggs and 100% accuracy of success in the type of Arabic chicken eggs.

An Improved KPCA Method of Fault Detection Based on Wavelet Denoising

2011 ◽  
Vol 467-469 ◽  
pp. 1427-1432 ◽  
Author(s):  
Xiao Qiang Zhao ◽  
Zhan Ming Li
Keyword(s):  
Principal Component Analysis ◽  
Nonlinear Systems ◽  
Fault Detection ◽  
Principal Component ◽  
Wavelet Denoising ◽  
Kernel Principal Component Analysis ◽  
Random Disturbance ◽  
Kernel Matrix ◽  
Denoising Method ◽  
Simulation Results

For complicated nonlinear systems, the data inevitably have noise, random disturbance, Traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix K for fault detection with large sample sets. So an improved KPCA method based on wavelet denoising is proposed. First, wavelet denoising method is used for data processing, then the improved KPCA method can reduce calculational complexity of fault detection. The proposed method is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.

COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES

2005 ◽  
Vol 19 (07) ◽  
pp. 873-893 ◽  
Author(s):  
PEICHUNG SHIH ◽  
CHENGJUN LIU
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Color Space ◽  
Principal Component ◽  
Face Image ◽  
Color Images ◽  
Grand Challenge ◽  
Color Spaces ◽  
The Face ◽  
Different Color

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

Sign in / Sign up

Export Citation Format

Share Document