Jacqueline Le Moigne

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One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. This leads to the fundamental problem of point matching: Given two sets of points, find the (affine) transformation that transforms one point set so that its distance from(More)
Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. We introduce a registration algorithm that combines a simple yet powerful search(More)
The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow(More)
Hyperspectral imagery, by definition, provides valuable remote sensing observations at hundreds of frequency bands. Conventional image classification (interpretation) methods may not be used without dimension reduction preprocessing. Automatic Wavelet Reduction has been proven to yield better or comparable classification accuracy, while achieving(More)
Received () Revised () Communicated by () Clustering is central to many image processing and remote sensing applications. isodata is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to isodata clustering, which achieves better(More)