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Remote sensing image registration is still a challenging task because of diverse image types and the lack of a consistent transformation. To improve image registration in remote sensing, this paper develops a robust and accurate feature point matching framework. A modified scale-invariant feature transform (SIFT) method is first introduced for feature detection and pair matching. Based on the properties...
A scheme of feature analysis in synthetic aperture radar (SAR) imaging based on compressed sensing (CS) is presented. First, a conventional imaging algorithm, such as range-Doppler algorithm or chirp-scaling algorithm, is used to acquire the complex image of the target scene. Then, a greedy algorithm is applied to this complex image to locate the scattering coefficients of the target, where the research...
This paper presents a kernel-based principal component analysis (kernel PCA) to extract critical features for improving the performance of a stock trading model. The feature extraction method is one of the techniques to solve dimensionality reduction problems (DRP). The kernel PCA is a feature extraction approach which has been applied to data transformation from known variables to capture critical...
Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder which is quite common in childhood, with an estimated prevalence of 5–8%, and often persists into adolescence and adulthood. It is further characterized as inappropriate developmentally symptoms of inattention, impulsiveness, motor over-activity and restlessness. The aim of this study is to evaluate the feasibility of diagnosing...
Recently, several gaussian like image representations are proposed as an alternative of the bag-of-word representation over local features. These representations are proposed to overcome the quantization error problem faced in bag-of-word representation. They are shown to be effective in different applications, the Extended Hierarchical Gaussianization reached excellent performance using single feature...
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