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AutoScale: Learning to Scale for Crowd Counting and Localization
Chenfeng Xu, Dingkang Liang, Yongchao Xu +4
Recent works on crowd counting mainly leverage CNNs to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gauss…
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Hao Wang, Pu Lu, Hui Zhang +6
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Differ…
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yongchao Xu, Mingtao Fu, Qimeng Wang +4
Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects su…
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Chenfeng Xu, Kai Qiu, Jianlong Fu +3
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…