A multistage hierarchical algorithm for hand shape recognition

This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm

Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multi-dimensional grids

This paper presents a hand-shape recognition algorithm based on using multi-dimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the non-linearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different hand-shapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size

A Multistage Hierarchical Algorithm for Hand Shape Recognition

Abstract—This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm. I

Interferon lambda 3 genotype predicts hepatitis C virus RNA levels in early acute infection among people who inject drugs: The InC³ Study

Background and objectives: Hepatitis C virus (HCV) RNA level in acute HCV infection is predictive of spontaneous clearance. This study assessed factors associated with HCV RNA levels during early acute infection among people who inject drugs with well-defined acute HCV infection. Study design: Data were from International Collaboration of Incident HIV and Hepatitis C in Injecting Cohorts (InC3) Study, an international collaboration of nine prospective cohorts studying acute HCV infection. Individuals with available HCV RNA levels during early acute infection (first two months following infection) were included. The distribution of HCV RNA levels during early acute infection were compared by selected host and virological factors. Results: A total of 195 individuals were included. Median HCV RNA levels were significantly higher among individuals with interferon lambda 3 (IFNL3, formerly called IL28B) CC genotype compared to those with TT/CT genotype (6.28 vs. 5.39. log. IU/mL, respectively; P= 0.01). IFNL3 CC genotype was also associated with top tertile HCV RNA levels (≥6.3. logIU/mL; vs. TT/CT genotype; adjusted Odds Ratio: 4.28; 95%CI: 2.01, 9.10; P < 0.01). Conclusions: This study indicates that IFNL3 CC genotype predicts higher HCV RNA levels in early acute HCV infection