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Orientation Field (OF) is one of the most significant characters to distinguish fingerprint images from non-fingerprint images. An effective definition of fingerprint OF pattern will not only benefit fingerprint enhancement, but also contribute to latent fingerprint detection and segmentation. The existing fingerprint OF models either require pre-knowledge of singular points, or cannot be generalized...
Fingerprint matching is one of the most widely used biometrics for personal identification. However, the performance of fingerprint identification system is insufficient for many applications. Lots of methods were proposed to improve system performance by introducing more information into matching process. In this paper, we introduced a new kind of information named prior knowledge and proposed a...
Support Vector Machines are the state-of-the-art tools in data mining. However, their strength are also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. Therefore, opening the black-boxor making SVMs explainable became more important and necessary in areas such as medical diagnosis and credit evaluation. Rule extraction from SVMs,...
In this paper, we present an interactive method for liver tumor segmentation from computed tomography (CT) scans. After some pre-processing operations, including liver parenchyma segmentation and liver contrast enhancement, the CT volume is partitioned into a large number of catchment basins under watershed transform. Then a support vector machines (SVM) classifier is trained on the user-selected...
In this paper, we present an algorithm for automatic liver segmentation from CT scans which is based on a statistical shape model. The proposed method is a hybrid method that combines three steps: 1) Localization of the average liver shape model in a test CT volume via 3D generalized Hough transform; 2) Subspace initialization of the statistical shape model; 3) Deformation of the shape model to adapt...
Classification of underwater objects remains challenging and significant problem because of the complexity of underwater environments. In this paper,a probabilistic neural network (PNN) is used as a classifier to the automatic classification of underwater objects. Firstly, a process of multi-field feature extraction is employed to construct a feature vector.The multi-field feature extraction involves...
In this paper, we propose a novel semi-supervised algorithm, which works under a two-view setting. Our algorithm, named kernel canonical component analysis graph (KC-GRAPH), can effectively enhance the performance and the parameter stability of traditional graph-based semi-supervised algorithms by taking the advantage of two views using kernel canonical component analysis (KCCA). Experiments have...
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