7 accepted papers
We propose a simple yet effective single-stage video text detection framework, termed CVTD (Conditional convolutions for Video Text Detection), which, to the best of our knowledge, is the first end-to-end single-stage video text detection framework.Most existing video text detection methods adopt te…
Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixati…
Trauma PXR are essential for instantaneous pelvic bone fracture detection. However, small, pathologically critical fractures can be missed, even by experienced clinicians, under the very limited diagnosis times allowed in urgent care. As a result, fracture CAD has very high demands to save time and…
Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we p…
Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are inc…
In this paper, we study the problem of Cross-View Person Identification (CVPI), which aims at identifying the same person from temporally synchronized videos taken by different wearable cameras. Our basic idea is to utilize the human motion consistency for CVPI, where human motion can be computed by…
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 mo…