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—Classification for high-dimensional remotely sensed data generally requires a large set of data samples and enormous processing time, particularly for hyperspectral image data. In this paper, we present a fast two-stage classification method composed of a band selection (BS) algorithm with feature extrac-tion/selection (FSE) followed by a recursive maximum(More)
Diarrhea is an important public health problem in Taiwan. Climatic changes and an increase in extreme weather events (extreme heat, drought or rainfalls) have been strongly linked to the incidence of diarrhea-associated disease. This study investigated and quantified the relationship between climate variations and diarrhea-associated morbidity in(More)
We present a new algorithm for synthesizing temporal textures, which is simple and requires only a static texture image as input to produce a continuous varying stream of realistic images. We first introduce the basis sequence generation procedure in which the chosen patches from the input texture image are stitched on the output frame via blended alpha(More)
With the popularity of digital camera, digital image processing is getting more important. One of the most common problems in digital photographing is motion blur. The research in solving the problem of motion blur efficiently is called motion deblur. When taking a photograph, the shaking of camera is the reason causing blurred image. The blur process can(More)
Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the(More)
The k nearest neighbor is a lazy learning algorithm that is inefficient in the classification phase because it needs to compare the query sample with all training samples. A template reduction method is recently proposed that uses only samples near the decision boundary for classification and removes those far from the decision boundary. However, when class(More)