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We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper,(More)
One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box(More)
Triptolide (TP), an active component isolated from Tripterygiumwilfordii Hook F, has therapeutic potential against rheumatoid arthritis (RA). However, the underlying molecular mechanism has not been fully elucidated. The aim of this study is to investigate the mechanisms of TP acting on RA by combining bioinformatics analysis with experiment validation. The(More)
Reconstructing 3D human poses from a single 2D image is an ill-posed problem without considering the human body model. Explicitly enforcing physiological constraints is known to be non-convex and usually leads to difficulty in finding an optimal solution. An attractive alternative is to learn a prior model of the human body from a set of human pose data. In(More)
Digitization of document images using OCR based systems is adversely affected if the image of the document contains distortion (warping). Often, costly and precisely calibrated special hardware such as stereo cameras, laser scanners, etc. are used to infer the 3D model of the distorted image which is used to remove the distortion. Recent methods focus on(More)
BACKGROUND Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. METHODS(More)
Astragalus polysaccharides (APS) possess multiple immunomodulatory activities. Due to its high molecular weight, orally administration of APS is not easily absorbed into the blood stream, and how APS exerts its capacity in vivo is still not well elucidated. We assume that enteric mucosal immune response might trigger the immune regulation of APS, and our(More)
This paper is focused on developing a new approach for video-based action detection where a set of temporally synchronized videos are taken by multiple wearable cameras from different and varying views and our goal is to accurately localize the starting and ending time of each instance of the actions of interest in such videos. Compared with traditional(More)
Intrusion alert analysis system correlates alerts that generated by one or more IDS(s), and yields a succinct attack scenario which reflects an intrusion process. This paper presents an intrusion alert analysis model consists of four modules: alert formalization, alert filtering, alert fusion and correlation, and scenario visualization. Alerts are fused and(More)
Emerging evidence indicates that the dysregulation of protein ubiquitination plays a crucial role in aging-associated diseases. Smad-dependent canonical BMP signaling pathway is indispensable for osteoblastic bone formation, which could be disrupted by the ubiquitination and subsequent proteasomal degradation of Smad1/5, the key molecules for BMP signaling(More)