TransHist: Occlusion-robust shape detection in cluttered images
Author(s)
Author(s)
Han, C.
Sinn, L. T.
Wong, T. T.
Date Issued
2018
Publisher
Springer
Journal
Computational Visual Media
Volume
4
Issue
2
Start page
161
End page
172
Abstract
Shape matching plays an important role in various computer vision and graphics applications such as shape retrieval, object detection, image editing, image retrieval, etc. However, detecting shapes in cluttered images is still quite challenging due to the incomplete edges and changing perspective. In this paper, we propose a novel approach that can efficiently identify a queried shape in a cluttered image. The core idea is to acquire the transformation from the queried shape to the cluttered image by summarising all point-to-point transformations between the queried shape and the image. To do so, we adopt a point-based shape descriptor, the pyramid of arc-length descriptor (PAD), to identify point pairs between the queried shape and the image having similar local shapes. We further calculate the transformations between the identified point pairs based on PAD. Finally, we summarise all transformations in a 4D transformation histogram and search for the main cluster. Our method can handle both closed shapes and open curves, and is resistant to partial occlusions. Experiments show that our method can robustly detect shapes in images in the presence of partial occlusions, fragile edges, and cluttered backgrounds.
SFU Affiliated Publication
No
Availability at SFU Library
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