A novel spectral imaging ellipsometer based on a mono-axial power spectrograph has been developed for one-dimensional spectroscopic measurement of patterned structures. To obtain the imaging data of a patterned sample using ellipsometry can be realized by conventional ellipsometers with 2-dimesional (2D) scanning sample stage or 2D imaging ellipsometers with imaging optics. The former has major...

Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsuper-vised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segment...

The application of hyperspectral sensors in the development of machine vision solutions has become increasingly popular as the spectral characteristics of the imaged materials are better modeled in the hyperspectral domain than in the standard trichromatic red, green, blue data. While there is no doubt that the availability of detailed spectral information is opportune as it opens the possibili...

Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the Spatially Guided FCM (SGFCM) algorithm is presented which segments multi...

Schroedinger Eigenmaps (SE) has recently emerged as a powerful graph-based technique for semi-supervised manifold learning and recovery. By extending the Laplacian of a graph constructed from hyperspectral imagery to incorporate barrier or cluster potentials, SE enables machine learning techniques that employ expert/labeled information provided at a subset of pixels. In this paper, we show how ...

X-ray mapping images of polished sections are classified using two unsupervised clustering algorithms. The methods applied are the k-means algorithm and an extended spectral fuzzy c-means algorithm. The extentions include new types of memberships that are related to the contextual information. In addition to the traditional spectral membership we apply a spatial membership and a parental member...

For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very simil...

The spectroscopic content within optical coherence tomography (OCT) data can provide a wealth of information. Spectroscopic OCT methods are frequently limited by time-frequency trade-offs that limit high spectral and spatial resolution simultaneously. We present spectroscopic spectral-domain optical coherence microscopy performed with a multimodality microscope. Restricting the spatial extent o...

State of the art dimension reduction and classification schemes in multiand hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of...

The fusion of high-spectral/low-spatial resolution multispectral and low-spectral/high-spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. Recently, some studies showed that a wavelet-based image fusion method provides high quality spectral content in fused images. However, most wavelet-based methods yield fused results with spa...