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—Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded,(More)
—Band selection is often applied to reduce the di-mensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose a new(More)
Danger Theory is a novel method of BiologicalImmunology. Artificial Immune Systems researchers may extract benefits from the theory, especially in anomaly detection. The definition of danger signals is one of the most important problems in Danger Theory. For the distinction between danger and safety is fuzzy and precarious, the precise calculation method is(More)
Bovine viral diarrhea virus (BVDV) infections are endemic in cattle populations worldwide and cause major economic losses. Thus, an effective vaccine is needed against the transmission of BVDV. The glycoprotein Erns is one of the envelope proteins of this virus and shows BVDV-related immunogenicity. Here, we report the use of Panax ginseng as an alternative(More)
Normalization of cDNA and oligonucleotide microarray data has become a standard procedure to offset non-biological differences between two samples for accurate identification of differentially expressed genes. Although there are many normalization techniques available, their ability to accurately remove systematic variation has not been sufficiently(More)
—Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyper-spectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the(More)
—A decision fusion approach is developed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support-vector-machine-based supervised classification in class separation and the capability of an unsupervised classifier, such as K-means clustering , in reducing trivial spectral variation(More)
In this paper, we propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small(More)