Shape detection from line drawings with local neighborhood structure

✍ Scribed by Rujie Liu; Yuehong Wang; Takayuki Baba; Daiki Masumoto

• Publisher: Elsevier Science
• Year: 2010
• Tongue: English
• Weight: 742 KB
• Volume: 43
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2009.11.022

✦ Synopsis

An object detection method from line drawings is presented. The method adopts the local neighborhood structure as the elementary descriptor, which is formed by grouping several nearest neighbor lines/ curves around one reference. With this representation, both the appearance and the geometric structure of the line drawing are well described. The detection algorithm is a hypothesis-test scheme. The top k most similar local structures in the drawing are firstly obtained for each local structure of the model, and the transformation parameters are estimated for each of the k candidates, such as object center, scale and rotation factors. By treating each estimation result as a point in the parameter space, a dense region around the ground truth is then formed provided that there exist a model in the drawing. The mean shift method is used to detect the dense regions, and the significant modes are accepted as the occurrence of object instances.