Active skeleton for non-rigid object detection

By Xiang Bai, Xinggang Wang, Longin Jan Latecki, Zhuowen Tu. 2009

93 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “Nevertheless, recent works have started to refer to a specific type of error as "hallucination" in image captioning [13, 159] and object detection [4, 83], which denotes non-existing objects detected or localized incorrectly at their expected position.”
From a Set of Shapes to Object Discovery (2010, background): “Existing work that uses only shape cues for recognition in real-world images requires either a manually specified shape template [4, 5], or manually segmented training images to learn the object shape [6].”
Object Detection With Deep Learning: A Review (2018, methodology): “t http://www.cs.berkeley.edu/ rbg/rcnn . 1. Introduction Features matter. The last decade of progress on various visual recognition tasks has been based considerably on the use of SIFT [26] and HOG [7]. But if we look at performance on the canonical visual recognition task, PASCAL VOC object detect”
Latent hierarchical structural learning for object detection (2010, background): “An object is represented by a mixture of hierarchical tree models where the nodes represent object parts.”
Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs (2016, background): “Extracting object skeletons directly from natural images is of broad interests to many real applications including object recognition/detection [4, 30], text recognition [36], road detection and blood vessel detection [29].”