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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)
Anti-angiogenesis represents a promising therapeutic strategy for the treatment of various malignancies. Isthmin (ISM) is a gene highly expressed in the isthmus of the midbrain-hindbrain organizer in Xenopus with no known functions. It encodes a secreted 60 kD protein containing a thrombospondin type 1 repeat domain in the central region and an(More)
Identification of the factors critical to the tumor-initiating cell (TIC) state may open new avenues in cancer therapy. Here we show that the metabolic enzyme glycine decarboxylase (GLDC) is critical for TICs in non-small cell lung cancer (NSCLC). TICs from primary NSCLC tumors express high levels of the oncogenic stem cell factor LIN28B and GLDC, which are(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)
—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)
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)
The high dimensionality of hyperspectral imagery challenges image processing and analysis. It has been shown that hyperspectral compression can be achieved by principal component analysis (PCA) for spectral decorrelation followed by the JPEG2000-based coding. This approach, referred to as PCA+JPEG2000, provides superior rate-distortion performance and can(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)
Decision fusion has been used to increase classification accuracy of remotely sensed images beyond the level achieved by individual classifiers. The main reason for the use of multiple classifiers is that some classifiers may perform better in terms of accuracies for some classes, while others possibly provide better results for other classes. If the best(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)