This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ?g, ?s, etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.

A Robust Fuzzy Local Information C-Means Clustering Algorithm

In recent years, near-infrared (NIR) spectroscopy has gained wide acceptance in different fields by virtue of its advantages over other analytical techniques, the most salient of which is its ability to record spectra for solid and liquid samples with no prior manipulation. Also, developments in instrumentation have resulted in the manufacture of spectrophotometers capable of quickly providing spectra that are flexible enough for use in different situations; thus, portable equipment can record spectra on site or even at production lines. This article discusses the features of NIR spectroscopy that have driven forward its dramatic development in a wide range of analytical fields in the last few years.

NIR spectroscopy: a rapid-response analytical tool

Firstly, the paper proposes an axiomatic definition for the notion of "segmentation" in image processing, which is based on the idea of a maximal partition. Then a key theorem links segmentation with connection, on the one hand, and with connective criteria on the other one. A series of lattice properties are then developed. In a last part, two examples of segmentations are proposed.

http://ieeexplore.ieee.org/iel5/8824/27937/01246969.pdf

Image segmentation

Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

The use of genetic algorithms has been growing exponentially since Holland published the first papers about them. Thanks to the extraordinary increase in calculation power, nowadays it is possible to apply them to extremely complex problems. A considerable number of papers in which genetic algorithms have been applied have been published in several scientifical journals. This review is of course far from being a complete summary of the application of genetic algorithms to chemical problems; its goal is to show the reader the main fields of application of this technique, together with providing a list of references on the subject. Copyright © 2001 John Wiley & Sons, Ltd.

Genetic algorithms in chemometrics and chemistry: a review

Fuzzy C-means (FCM) clustering is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. The segmentation of the image in meaningful regions with FCM is based on spectral information only. The geometrical relationship between neighbouring pixels is not used. In this paper, a semi-supervised FCM technique is used to add geometrical information during clustering. The local neighbourhood of each pixel determines the condition of each pixel, which guides the clustering process. Segmentation experiments with the geometrically guided FCM (GG-FCM) show improved segmentation above traditional FCM such as more homogeneous regions and less spurious pixels.

Geometrically guided fuzzy C-means clustering for multivariate image segmentation

Multivariate image segmentation with cluster size insensitive fuzzy C-means

https://repository.ubn.ru.nl/bitstream/handle/2066/29596/29596.pdf

NIR - Remote-Sensing and Artificial Neural Networks for Rapid Identification of Post Consumer Plastics

http://automatica.dei.unipd.it/public/Schenato/PSC/2010_2011/gruppo4-Building_termo_identification/IdentificazioneTermodinamica20072008/Biblio/sdarticle1.pdf

Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS)

This paper describes a new approach to geometrically guided fuzzy clustering. A modified version of fuzzy C-means (FCM) clustering, conditional FCM, is extended to incorporate a priori geometrical information from the spatial domain in order to improve image segmentation. This leads to a new algorithm (GGC-FCM) where the cluster guidance is determined by the membership values of neighbouring pixels. GGC-FCM is tested on synthetic and real images to demonstrate the improved image segmentation compared to traditional FCM.

W.H.A.M. van den Broek

Papers

Geometrically guided fuzzy C-means clustering for multivariate image segmentation

Multivariate image segmentation with cluster size insensitive fuzzy C-means

NIR - Remote-Sensing and Artificial Neural Networks for Rapid Identification of Post Consumer Plastics

Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS)

Multivariate image segmentation based on geometrically guided fuzzy C-means clustering