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The purpose of this research is to provide a puzzle-based framework to study how global and local information is interacted on human's visual perception during the decision making process. Since the Deep Convolutional Neural Networks (DCNN) has shown the state of the art performance in image classification and object detection, DCNN can output scores to reflect the level of global information, which...
Several studies on autism spectrum disorder (ASD) show that there exists significant heterogeneity in phenotype of the disorder. Additionally, many published findings also suggested that ASD is defined by atypical local/global processing. In this paper, we designed a puzzled-based intervention to examine the sensitiveness to the information of local /global processing on individuals with ASD. Additionally,...
Motivated by fuzzy clustering incorporating spatial information, we present a spatially constrained fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes as cluster centers and adds a spatial regularizer into the fuzzy objective function. Formulation of the new fuzzy objective function is presented; and its iterative numerical solution, which minimizes the objective...
Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots...
Despite the widespread application of microarray imaging for biomedical research, barriers still exist regarding its reliability and reproducibility for clinical use. A critical problem lies in accurate spot segmentation and quantification of gene expression level (mRNA) from microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots...
Microarray imaging is now widely used to monitor the activities of thousands of genes simultaneously in biological samples. While there are a number of methods in use for the quantification of microarray images, barriers still exist towards its feasibility for clinical use. Among them, automated spot segmentation is critical for accurate and high throughput measurements of gene expression levels from...
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