human action recognition is an important problem in computer vision. one of the methods that are recently used is sparse coding. conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that is available in the training set. but in this paper for solving this problem, we use a discriminative sparse code based on multi-manifolds. ...

Abstract Camera traps are a popular tool in terrestrial wildlife research due to their low costs, easy operability, and usefulness for studying wide array of species questions. The vast numbers images they generate often require multiple human data extractors, yet accuracy inter-observer variance rarely considered. We compared results from 10 observers who processed the same set multi-species c...

Abstract Cashmere and wool are common raw materials in the textile industry. The clothes made of cashmere popular because excellent comfort. A system that can quickly automatically classify two will improve efficiency fiber recognition We propose a classification method fibers based on feature fusion using maximum inter-class variance. First, target area is obtained by preprocessing algorithm. ...

OBJECT Magnetic resonance spectroscopic imaging (MRSI) is increasingly used in medicine and clinical research. Previous reliability studies have used small samples and focussed on limited aspects of variability; information regarding 1.5T versus 3T performance is lacking. The aim of the present work was to measure the inter-session, intra-session, inter-subject, within-brain and residual varian...

The random-effects model is often used for meta-analysis of clinical studies. The method explicitly accounts for the heterogeneity of studies through a statistical parameter representing the inter-study variation. We discuss several iterative and non-iterative alternative methods for estimating the inter-study variance and hence the overall population treatment effect. We show that the leading ...

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide 'activation' in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by test...