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Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise(More)
Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to(More)
At present, there are many methods for frontal view face recognition. However, few of them can work well when only one example image per class is available. In this paper, we present a new method based on SVD perturbation to deal with the 'one example image' problem and two generalized eigenface algorithms are proposed. In the first algorithm, the original(More)
Recently, a new technique called 2-dimensional principal component analysis (2DPCA) was proposed for face representation and recognition. The main idea behind 2DPCA is that it is based on 2D matrices as opposed to the standard PCA, which is based on 1D vectors. Although 2DPCA obtains higher recognition accuracy than PCA, a vital unresolved problem of 2DPCA(More)
In this paper, a new feature extraction algorithm is developed based on canonical correlation analysis (CCA), called Local Discrimination CCA (LDCCA). The method considers a combination of local properties and discrimination between different classes. Not only the correlations between sample pairs but also the correlations between samples and their local(More)
Recently, a method called (PC) 2 A was proposed to deal with face recognition with one training image per person. As an extension of the standard eigenface technique, (PC) 2 A combines the original face image with its first-order projection and then performs principal component analysis (PCA) on the enriched version of the image. It was reported that (PC) 2(More)