This paper demonstrates a methodology of image enhancement that uses principle component analysis (PCA) in wavelet domain. PCA fully de-correlates the original data set so that the energy of the signal will concentrate on the small subset of PCA transformed dataset. The energy of random noise evenly spreads over the whole data set, we can easily distinguish signal from random noise over PCA dom...

Child growth is characterised by increases in height, and increases in maturational status. Functional data analysis provides a tool to separate these two sources of variation (registration) and differentiates between the variation in maturational tempo (temporal, or “phase” variation) and the variation in height (amplitude variation). We extended this concept by combining Principal Component A...

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pair-wise relationships between pixels in the image...

     Data reduction is used to aggregate or amalgamate the large data sets into smaller and manageable information pieces in order to fast and accurate classification of different attributes. However, excessive spatial or spectral data reduction may result in losing or masking important radiometric information. Therefore, we conducted this research to evaluate the effectiveness of the different...

The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was uti...

To optimize the exploitation of plant genetic resources for improvement of oilseed crops in Pakistan, seed quality characterization of germplasm of rapeseed is being executed at Nuclear Institute for Food and Agriculture (NIFA), Peshawar. The aim of this study was to determine the feasibility of using Near Infrared Spectroscopy (NIRS) to identify seed quality traits of oilseed rape germplasm. S...

A psychophysical experiment was performed to measure the visibility of chromatic noise. Through Principle Component Analysis (PCA) on the results of this experiment, an orthogonal color space with the luminance channel independent of chromatic channels was constructed. By transforming noise images into this space, the visibility of chromatic noise can be predicted. Comparison with other opponen...

Image fusion is the combination of two or more different images by using suitable algorithms to form an output image. It provides a useful tool to integrate multiple images into a composite image. In this paper, we present an approach that uses the principle component analysis (PCA) along with the selection of maximum pixel intensity to perform fusion. The entropy, mutual information and the un...

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undistur...

Magnetic Resonance (MR) Imaging has come up as widely accepted and revolutionary innovation in field of medical science and brain imaging especially. A new method is proposed here for MRI brain image classification using Polynomial Kernel Principle Component Analysis (KPCA) with Neural Network. In this paper, we are having various stages namely pre-processing, feature extraction, feature reduct...