This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is clos...
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This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is closest to it. Some attractive advantages of this learning algorithm are the robustness to noises and the scale of training samples, the straightforward way to handle multi-class tasks, and most of all the capability of processing high dimensional and nonlinear data. In our work, we deduce the NCH algorithm again basing on theories of the computational geometry, from which a simpler implementation of it is presented. Then we apply it to real-world remote problems and compare it with two other state-of-arts classifiers: K-NN and SVM. Experiments in this paper confirm the promising performance of NCH for remote sensing classification.
image quality evaluation is becoming essential in many imageprocessing problems. This paper proposes a new image quality evaluation approach based on decision fusion method of canonical correlation analysis (CCA). By...
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Locality Preserving Projection (LPP), as a linear manifold learning algorithm, has attracted much interests in recent years. LPP considers an n1× n2image as a vector in €n1×n2space, and thus is limited by th...
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How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is t...
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Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much e...
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In this article, we present the projective equation of a circle in a perspective view, which naturally encodes such important geometric entities as the projected circle center, the vanishing point of the normal direct...
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In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neighborhood relations between the data po...
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We present a method for tracking deformable surfaces in 3D using a stereo rig. Different from traditional recursive tracking approaches that provide a strong prior on the pose for each new frame, the proposed method t...
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Recently, gender classification from face images has attracted a great deal of attention. It can be useful in many places. In this paper, a novel hybrid face coding method by fusing appearance features and geometry fe...
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Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video...
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