Exploring northeast African metric craniofacial variation at the individual level: A comparative study using principal components analysis

2004 ◽  
Vol 16 (6) ◽  
pp. 679-689 ◽  
Author(s):  
S.O.Y. Keita
Keyword(s):  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Individual Level ◽  
Components Analysis ◽  
The Individual ◽  
Craniofacial Variation

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures DOI: 10.5585/exacta.v8i2.2000

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

Effects of Sampling Parameters on Principal Components Analysis of Raman Line Images

1996 ◽  
Vol 50 (6) ◽  
pp. 708-714 ◽  
Author(s):  
Charlene A. Hayden ◽  
Michael D. Morris
Keyword(s):  
Principal Components Analysis ◽  
Spectral Data ◽  
Principal Components ◽  
Slit Width ◽  
Data Sets ◽  
Polystyrene Spheres ◽  
Components Analysis ◽  
Scanned Images ◽  
The Individual ◽  
Spectrometer Slit

Raman images of the distribution of materials in a sample prepared from 10-μm-diameter polystyrene spheres embedded in epoxy were reconstructed from sets of line-scanned images, with the use of univariate and multivariate processing of the spectral data. Multiple sets of microscopic Raman spectral line images were acquired by using line-focused illumination with a cylindrical lens, a motorized translation stage to move the sample perpendicular to the illumination line, and a holographic imaging spectrograph equipped with a 2D charge-coupled device (CCD) detector. Repeat sets of data were obtained at different spectrometer slit width settings and different magnification. The raw spectral data were processed by using both a simple univariate method (single-band integration) and a more sophisticated multivariate method [principal components analysis (PCA) with eigenvector rotation] to generate 2D Raman images representing spatial distribution of the individual polymeric constituents. The repeat data sets were compared to ascertain the effects of the sampling parameters on the PCA method. The results indicated that spectrometer slit width and magnification affect the sampling depth and spatial resolution, but have little effect on the PCA. Moreover, digital sampling (i.e., number of PCA wavelength variables) could be significantly reduced with little or no degradation of the PCA-generated images, particularly if key bands were represented.

Location and Intensity Discrimination in the Leech Local Bend Response Quantified Using Optic Flow and Principal Components Analysis

2005 ◽  
Vol 93 (6) ◽  
pp. 3560-3572 ◽  
Author(s):  
Serapio M. Baca ◽  
Eric E. Thomson ◽  
William B. Kristan
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Optic Flow ◽  
Motion Tracking ◽  
Body Wall ◽  
Principal Component ◽  
The Body ◽  
Video Frames ◽  
Components Analysis ◽  
The Individual

In response to touches to their skin, medicinal leeches shorten their body on the side of the touch. We elicited local bends by delivering precisely controlled pressure stimuli at different locations, intensities, and durations to body-wall preparations. We video-taped the individual responses, quantifying the body-wall displacements over time using a motion-tracking algorithm based on making optic flow estimates between video frames. Using principal components analysis (PCA), we found that one to three principal components fit the behavioral data much better than did previous (cosine) measures. The amplitudes of the principal components (i.e., the principal component scores) nicely discriminated the responses to stimuli both at different locations and of different intensities. Leeches discriminated (i.e., produced distinguishable responses) between touch locations that are approximately a millimeter apart. Their ability to discriminate stimulus intensity depended on stimulus magnitude: discrimination was very acute for weak stimuli and less sensitive for stronger stimuli. In addition, increasing the stimulus duration improved the leech's ability to discriminate between stimulus intensities. Overall, the use of optic flow fields and PCA provide a powerful framework for characterizing the discrimination abilities of the leech local bend response.

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