An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image

Effective Hyper-Spectral Image Segmentation Using Multi-scale Geometric Analysis

The wide availability of multispectral images has fostered the development of new algorithms for remote sensing applications. These applications range from agricultural and environmental to military use. Nevertheless, the analysis of such voluminous data requires advanced analysis and computational methodologies as well as advanced hardware and computational methods. In this paper we introduce ...

A Fast and Accurate Algorithm for Facial Feature Segmentation

A facial feature segmentation algorithm for head and shoulder sequences is proposed. The method is based on simple spatial and temporal heuristics techniques for the purpose of practical implementation. Facial feature are segmented using two common cues i) regular and high image intensity variation in temporal domain due to various motion like talking and moving, ii) high edge density around th...

Hill-Manipulation: An Effective Algorithm for Color Image Segmentation

In cluster-based image segmentation techniques, clusters may be viewed as hills in a histogram. These techniques may suffer from large hills that dominate the smaller hills and thus they result in a loss of image details in the segmentation process. In this paper, we propose a novel approach, called Hill-Manipulation algorithm, to solve the problems of the traditional cluster-based image segmen...

A Method of Segmentation for Hyper spectral & Medical Images Based on Color Image Segmentation

The paper propose an original and simple segmentation strategy based on the EM approach for hyper spectral images. In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified. The spatial con...

FLIC: Fast & Lossless Image Compression Algorithm for Hyper-Spectral Images