Principal Component Analysis applied to digital image compression
نویسندگان
چکیده
منابع مشابه
Principal Component Analysis applied to digital image compression.
OBJECTIVE To describe the use of a statistical tool (Principal Component Analysis - PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. METHODS The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in th...
متن کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
متن کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
متن کاملIndependent component analysis applied to digital image watermarking
The authors propose a new solution to the watermarking of images. This approach uses Independent Component Analysis (ICA) to project the image into a basis with its components as statistically independent as possible. The watermark is then introduced in this representation of the space. Thus, the change of basis is the key of the steganography problem. The algorithm applied to the fragile water...
متن کاملLow-Complexity Principal Component Analysis for Hyperspectral Image Compression
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral-image compression. However, the computational cost of determining the data-dependent PCA transform is high due to its traditional eigendecomposition implementation which requi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Einstein (São Paulo)
سال: 2012
ISSN: 1679-4508
DOI: 10.1590/s1679-45082012000200004