Principal Component Analysis (PCA) is a technique of multivariate data analysis widely used in various fields like biology, ecology or economy to reduce data dimensionality while retaining most important information. It is becoming a standard practice in multispectral/hyperspectral imaging since those multivariate data generally suffer from a high redundancy level.
Nevertheless, by definition, PCA is meant to be applied to a single multispectral/hyperspectral image at a time. When several images have to be treated, running a PCA on each image would generate specific reduced spaces, which is not suitable for comparison between results. Thus, we focus on two PCA based algorithms that could define common reduced spaces of representation. The first method arises from literature and is computed with the barycenter covariance matrix. On the contrary, we designed the second algorithm with the idea of correcting standard PCA using permutations and inversions of eigenvectors.
These dimensionality reduction methods are used within the context of a cosmetological study of a foundation make-up. Available data are in-vivo multispectral images of skin acquired on different volunteers in time series. The main purpose of this study is to characterize the make-up degradation especially in terms of texture analysis. Results have to be validate by statistical prediction of time since applying the product.
PCA algorithms produce eigenimages that separately enhance skin components (pores, radiance, vessels...). From these eigenimages, we extract morphological texture descriptors and intent a time prediction. Accuracy of common reduced spaces outperform classical PCA one. In this paper, we detail how PCA is extended to the multiple groups case and explain what are the advantages of common reduced spaces when it comes to study several multispectral images.
Dimensionality reduction (DR) using tensor structures in morphological scale-space decomposition (MSSD) for
HSI has been investigated in order to incorporate spatial information in DR.We present results of a comprehensive
investigation of two issues underlying DR in MSSD. Firstly, information contained in MSSD is reduced using
HOSVD but its nonconvex formulation implicates that in some cases a large number of local solutions can be
found. For all experiments, HOSVD always reach an unique global solution in the parameter region suitable to
practical applications. Secondly, scale parameters in MSSD are presented in relation to connected components
size and the influence of scale parameters in DR and subsequent classification is studied.
Stochastic watershed is a robust method to estimate the probability density function (pdf) of contours of a
multi-variate image using MonteCarlo simulations of watersheds from random markers. The aim of this paper is
to propose a stochastic watershed-based algorithm for segmenting hyperspectral images using a semi-supervised
approach. Starting from a training dataset consisting in a selection of representative pixel vectors of each spectral
class of the image, the algorithm calculate for each class a membership probability map (MPM). Then, the MPM
of class k is considered as a regionalized density function which is used to simulate the random markers for the
MonteCarlo estimation of the pdf of contours of the corresponding class k. This pdf favours the spatial regions
of the image spectrally close to the class k. After applying the same technique to each class, a series of pdf are
obtained for a single image. Finally, the pdf's can be segmented hierarchically either separately for each class or
after combination, as a single pdf function. In the results, besides the generic spatial-spectral segmentation of
hyperspectral images, the interest of the approach is also illustrated for target segmentation.
A new integrated lensless bio-photonic sensors is being developed.
It replaces the ordinary slide supporting the DNA spots, and the
complex, large and expensive hybridisation and the scanner reading
system, by a sandwich of well defined chemical and optical layers
grafted onto a CCD sensor. The upper layer of the new biochip
performs the biological function.
Due to the architecture of the biochip leading to a lensless imaging
of the spots directly on the sensor pixels, the images produced will
have novel characteristics beyond the analysis capacity of reading
software packages of microarray analysis. In this framework,
specific image processing and statistical data analysis algorithms
have been developed in order to assess and to quantify these images.
In this paper, we present the results of the extension of the mathematical morphology to color images by treating multi-channel data as vectors. The approach presented here uses the HSI and related color spaces (intuitives). A modification of the lexicographical order for vectorial processing is developed. The importance of this new method lies on automatic selection of elements of the HSI and related color spaces to form an ordering structure. The achievement of the algorithm is realized through the introduction of a weight factor to reduce the high preference of the first component of the classic lexicographical order. Experimental results demonstrate the improvement of this new method.