Jiunn-Lin Wu

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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)
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)
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)
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