Incremental principal component analysis for image processing

2006 ◽  
Vol 32 (1) ◽  
pp. 32 ◽  
Author(s):  
Yongkyu Kim
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis

scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

Principal component analysis in medical image processing: a study

2015 ◽  
Vol 1 (1) ◽  
pp. 65 ◽  
Author(s):  
Dibyadeep Nandi ◽  
Amira S. Ashour ◽  
Sourav Samanta ◽  
Sayan Chakraborty ◽  
Mohammed A.M. Salem ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Medical Image ◽  
Medical Image Processing ◽  
Principal Component ◽  
Component Analysis

Character Pattern Recognition Algorithm Based on Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5313-5318
Author(s):  
Feng Xian Tang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
Specific Work ◽  
Advantages And Disadvantages

With the help of the study on mathematical theory and its progress and the development of the computer techniques, digital image processing technology has more and more been applied in each field. The pattern recognition judges unknown things by substituting machine for human eyes, which has a high application value. Thus, it becomes the major branch in image processing fields. The character recognition technology has developed rapidly because of its broad application prospect. Until now, it has been applied successfully in OCR and vehicle license plate recognition. However, it has certain difficulty for the pattern recognition to meet the specific requirements related to specific work scenes. This essay discusses several Eigen value selecting approaches and analyzes the advantages and disadvantages of each. For the template matching methods with penalty factors, in design, character recognition algorithm based on the principal component analysis is realized where scattering matrix between classes is as produced matrix.

scholarly journals Laminar-Turbulent Transition Localization in Thermographic Flow Visualization by Means of Principal Component Analysis

2021 ◽  
Vol 11 (12) ◽  
pp. 5471
Author(s):  
Daniel Gleichauf ◽  
Felix Oehme ◽  
Michael Sorg ◽  
Andreas Fischer
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Turbulent Flow ◽  
Flow Regime ◽  
Principal Component ◽  
Flow Regimes ◽  
Component Analysis ◽  
Flow Transition ◽  
Turbulent Flow Regime ◽  
Laminar And Turbulent Flow

Thermographic flow visualization is a contactless, non-invasive technique to visualize the boundary layer flow on wind turbine rotor blades, to assess the aerodynamic condition and consequently the efficiency of the entire wind turbine. In applications on wind turbines in operation, the distinguishability between the laminar and turbulent flow regime cannot be easily increased artificially and solely depends on the energy input from the sun. State-of-the-art image processing methods are able to increase the contrast slightly but are not able to reduce systematic gradients in the image or need excessive a priori knowledge. In order to cope with a low-contrast measurement condition and to increase the distinguishability between the flow regimes, an enhanced image processing by means of the feature extraction method, principal component analysis, is introduced. The image processing is applied to an image series of thermographic flow visualizations of a steady flow situation in a wind tunnel experiment on a cylinder and DU96W180 airfoil measurement object without artificially increasing the thermal contrast between the flow regimes. The resulting feature images, based on the temporal temperature fluctuations in the images, are evaluated with regard to the global distinguishability between the laminar and turbulent flow regime as well as the achievable measurement error of an automatic localization of the local flow transition between the flow regimes. By applying the principal component analysis, systematic temperature gradients within the flow regimes as well as image artefacts such as reflections are reduced, leading to an increased contrast-to-noise ratio by a factor of 7.5. Additionally, the gradient between the laminar and turbulent flow regime is increased, leading to a minimal measurement error of the laminar-turbulent transition localization. The systematic error was reduced by 4% and the random error by 5.3% of the chord length. As a result, the principal component analysis is proven to be a valuable complementary tool to the classical image processing method in flow visualizations. After noise-reducing methods such as the temporal averaging and subsequent assessment of the spatial expansion of the boundary layer flow surface, the PCA is able to increase the laminar-turbulent flow regime distinguishability and reduce the systematic and random error of the flow transition localization in applications where no artificial increase in the contrast is possible. The enhancement of contrast increases the independence from the amount of solar energy input required for a flow evaluation, and the reduced errors of the flow transition localization enables a more precise assessment of the aerodynamic condition of the rotor blade.

Curve Representation Using Principal Component Analysis for Shape Optimization of Path Generating Mechanisms

2005 ◽  
Author(s):  
Nassim Khaled ◽  
Ahmad Smaili
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Principal Component ◽  
Component Analysis ◽  
Path Generation ◽  
Starting Point ◽  
Curve Representation ◽  
Generation Mechanisms

The focus of this paper is on the synthesis of path generation mechanisms based on shape optimization. The principle component analysis (PCA) technique used in image processing is employed to represent the desired coupler curve of the mechanism and simulated annealing is used as the optimization tool. PCA representation is invariant under rotation, translation, scaling, and starting point. Once a shape-optimized mechanism is found, it is translated, rotated, and scaled to its final form. An illustrative example is introduced to demonstrate the proposed method.

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