Principal Component Analysis applied to digital image compression

Santo, Rafael do Espírito

doi:10.1590/s1679-45082012000200004

Cited by 36 publications

(20 citation statements)

References 2 publications

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“…e principal component of a set of data (dimensionality) is obtained by means of an analysis that consists in nding the eigenvalues of the covariance matrix [31]. Each eigenvector has a corresponding eigenvalue.…”

Section: Resultsmentioning

confidence: 99%

Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

Luiz

Bell

Rocha

et al. 2018

Journal of Spectroscopy

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is study focuses on detection of antimicrobial residues in milk through Fourier transform near-infrared spectroscopy. Simulated and real samples were considered. e simulated ones take into account veterinary drugs added in milk samples in the following concentrations: enrofloxacin 100 μg/L, terramycin 100 μg/L, and penicillin 4 μg/L. e statistical tool used to discriminate the samples was the principal component analysis (PCA). Our results show that, with this experimental procedure, it is possible to discriminate different types of antimicrobials dissolved in milk. Moreover, the methodology was able to detect real sample milked on different days after the injection of ceftiofur hydrochloride which is in principle a zero grace period antimicrobial. e methodology proved to be fast and accurate within the maximum residue limits allowed by European Agency for Medicinal Products and Ministry of Agriculture Livestock and Food Supply from Brazil.

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Section: Resultsmentioning

confidence: 99%

Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

Luiz

Bell

Rocha

et al. 2018

Journal of Spectroscopy

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“…PCA is used in digital image compression such as in the structural image of the brain obtained during magnetic resonance treatment (Santo, 2012). The way the number of main components affects the quality of the picture has been shown (the fewer principal components used in the characteristics vector, the more degraded the quality of the image recovered).…”

Section: Application Of Pca In Medical Sciencementioning

confidence: 99%

The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Milewska

Jankowska

Citko

et al. 2014

Studies in Logic, Grammar and Rhetoric

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Abstract. Principal Component Analysis is one of the data mining methodsthat can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the maintenance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cycles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.

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“…The explanation of PCA algorithm on image compression has been made extensively in [4] and [5] but few variances were observed in the papers. Since only gray image is involved in our work, the input image X is an M N × monochrome image whereby each element represents the intensity value.…”

Section: B the General Pca Algorithmmentioning

confidence: 99%

Medical image compression using block-based PCA algorithm

Lim

Yap

Manap

2014

2014 International Conference on Computer, Communications, and Control Technology (I4CT)

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PCA algorithm can be employed to aid in image compression. In this paper, two extended-PCA algorithms that manipulate the block information of the image are tested and compared. The first algorithm is termed as block-by-block PCA in which general PCA algorithm are applied on each block of the image. In the second algorithm-the block-to-row PCA, all block information are first concatenated into row before general PCA algorithm is then applied on the transformed matrix. Digital fundus image is used as the input image in this work. Using the newly-derived compression ratios, the result shows that block-to-row PCA outperforms block-by-block PCA in terms of image quality and compression rate. At equal block size and compression ratio, block-to-row PCA can achieve higher PSNR than block-to-block PCA. Blocking effects are discernible on the reconstructed image using block-to-block PCA with block size = 16 and compression ratio as low as 0.25 while no apparent distortion are seen on the reconstructed image using block-torow PCA with block size = 32 and compression ratio as high as 0.90.

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Principal Component Analysis applied to digital image compression

Cited by 36 publications

References 2 publications

Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Medical image compression using block-based PCA algorithm

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