Multispectral image compression by cluster-adaptive subspace representation

2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010Multispectral imaging has attracted much interest in color science area, for its ability in providing much more spectral information than 3-channel color images. Due to the huge data volume, it is necessary to compress multispectral images for efficient transmission. This paper proposes a framework for spectral compression of multispectral image by using clusteradaptive subspaces representation. In the framework, multispectral image is initially segmented by hierarchical analysis of the transform coefficients in the global subspace, and then ambiguous pixels are identified and classified into proper clusters based on linear discriminant analysis. The dimensionality of each adaptive subspace is determined by specified reconstruction error level, followed by further cluster splitting if necessary. The efficiency of the proposed method is verified by experiments on real multispectral images.Institute of Textiles and ClothingRefereed conference pape

Hybrid Techniques On Color And Multispectral Image For Compression

Image Compression is a technique to reduce the number of bits required to represent and store an image. This technique is also used to compress two dimensional color shapes without loss of data as well as quality of the Image. Even though Simple Principal Component Analysis can apply to make enough compression on multispectral image, it needs to extend another version called Enhanced PCA(E-PCA). The given multispectral image is converted into component image and transformed as Column Vector with help of E-PCA. Covariance matrix and eigen values are derived from vector. Multispectral images are reconstructed using only few principal component images with the largest variance of eigen value. Then the component image is divided into block. After finding block sum value, mean value, the number of bits required to represent an image can be reduced by E-BTC model. The features are extracted and constructed in Table form. The proposed algorithm is repeated for all multispectral images as well as color image in the database. Finally, compression ratio table is generated. This proposed algorithm is tested and implemented on various parameters such as MSE, PSNR. These experiments are initially carried out on the standard color image and are to be followed by multispectral imager using MATLAB

Detection algorithms for spatial data

This dissertation addresses the problem of anomaly detection in spatial data. The problem of landmine detection in airborne spatial data is chosen as the specific detection scenario. The first part of the dissertation deals with the development of a fast algorithm for kernel-based non-linear anomaly detection in the airborne spatial data. The original Kernel RX algorithm, proposed by Kwon et al. [2005a], suffers from the problem of high computational complexity, and has seen limited application. With the aim to reduce the computational complexity, a reformulated version of the Kernel RX, termed the Spatially Weighted Kernel RX (SW-KRX), is presented. It is shown that under this reformulation, the detector statistics can be obtained directly as a function of the centered kernel Gram matrix. Subsequently, a methodology for the fast computation of the centered kernel Gram matrix is proposed. The key idea behind the proposed methodology is to decompose the set of image pixels into clusters, and expediting the computations by approximating the effect of each cluster as a whole. The SW-KRX algorithm is implemented for a special case, and comparative results are compiled for the SW-KRX vis-à-vis the RX anomaly detector. In the second part of the dissertation, a detection methodology for buried mine detection is presented. The methodology is based on extraction of color texture information using cross-co-occurrence features. A feature selection methodology based on Bhattacharya coefficients and principal feature analysis is proposed and detection results with different feature-based detectors are presented, to demonstrate the effectiveness of the proposed methodology in the extraction of useful discriminatory information --Abstract, page iii

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided