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This paper presents an optimized descriptor method for multispectral images. The method proposed is based on LGHD (Log-Gabor Histogram Descriptor)[1]. Initially, all feature points are detected from Long wave Infrared and Visible spectrum images, and descripted by LGHD, then PCA (Principal Component Analysis) is used to reduce the dimension of the two different descriptors, finally the optimized descriptors...
Holistic- and local-based methods are two-pronged in dorsal hand vein recognition, and the latter ones have become dominant recently due to their advanced performance. In this paper, we propose a novel approach to dorsal hand vein recognition using a global graph model which takes both the texture and shape cues into account. We first extend the basic graph model consisting of the minutiae of the...
We describe the method for segmentation of Left Ventricle (LV) in short axis cardiac MR Images in order to visibly identify the LV, and its outer wall. Segmentation of medical data is extremely time-consuming if done manually. Model based techniques represent one very promising approach. A model representing the object of interest is matched with unknown data. During the matching process the model's...
In this paper a new approach is proposed to enhance images that inspired by adaptive global and local processing of the human visual system. The method first uses a principal component analysis to the original image to provide orthogonality between channels and thus reduces the chromatic changes induced by the processing of luminance. Then a nonlinear method is applied to adjust the global dynamic...
In order to carry on the gait recognition fast and efficiently, a new representation scheme for feature description is proposed in this paper which utilizes nostationarity in the distribution of feature relationships. Firstly, edge pixels of silhouette are considered as low level features, then relationships among those features are characterized by two attributes, which are label of relative direction...
In this paper, we address the shape classification problem by proposing a new integrating approach for shape classification that gains both local and global image representation using Histogram of Oriented Gradient (HOG). In both local and global feature extraction steps, we use PCA to make this method invariant to shapes rotation. Moreover, by using a learning algorithm based on Adaboost we improve...
To recognize objects with similar shapes, a scheme for feature extraction and selection based on Multiscale transformation is proposed in this paper. Multiscale Geometric Analysis is characterized with directionality and anisotropy, and the subbands in different decomposed scales could present different classification capabilities. The scheme applies time-frequency-localized feature algorithm as well...
X-ray image segmentation is an important issue in medical image analysis. Due to inconsistent X-ray absorption, the intensities are usually unevenly distributed and noisy in the processed organ, thus the object segmentation becomes difficult. In this paper we propose a new segmentation method for patella from the lateral knee X-ray images based on the active shape model (ASM). At first, a patella...
The aim of this work is to learn a shape prior model for an object class and to improve shape matching with the learned shape prior. Given images of example instances, we can learn a mean shape of the object class as well as the variations of non-affine and affine transformations separately based on the thin plate spline (TPS) parameterization. Unlike previous methods, for learning, we represent shapes...
Micrographs of Chinese wines show floccule, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs. A semisupervised wine classification method based on kernel principal component analysis (KPCA) method and fuzzy C-means (FCM) algorithm using edge feature from micrographs was proposed in this paper. In this work, ten Chinese wines are determined or...
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