Local Symmetry Detection in Natural Images Using a Particle Filtering Approach

Abstract

In this paper, we propose an algorithm to detect smooth local symmetries and contours of ribbon-like objects in natural images. The detection is formulated as a spatial tracking task using a particle filtering approach, extracting one part of a structure at a time. Using an adaptive local geometric model, the method can detect straight reflection symmetries in perfectly symmetrical objects as well as smooth local symmetries in curved elongated objects. In addition, the proposed approach jointly estimates spine and contours, making it possible to generate back ribbon objects. Experiments for local symmetry detection have been conducted on a recent extension of the Berkeley segmentation data sets. We also show that it is possible to retrieve specific geometrical objects using intuitive prior structural information.

Linear Span Network for Object Skeleton Detection

“…Early skeleton extraction methods treat skeleton detection as morphological operations [12,25,14,9,7,23,11]. One hypothesis is that object skeletons are the subsets of lines connecting center points of super-pixels [9].…”

“…Such line subsets could be explored from super-pixels using a sequence of deformable discs to extract the skeleton path [7]. In [23], The consistence and smoothness of skeleton are modeled with spatial filters, e.g., a particle filter, which links local skeleton segments into continuous curves. Recently, learning based methods are utilized for skeleton detection.…”

“…Levinshtein [9] 0.174 0.217 -0.134 Lee [7] 0.223 0.252 -0.135 Lindeberg [11] 0.277 0.227 0.360 0.138 Particle Filter [23] 0.334 0.226 -0.129 MIL [21] 0.365 0.392 0.362 0.174 HED [24] 0.743 0.542 0.427 0.369 FSDS [18] 0.769 0.623 0.467 0.418 SRN [5] 0.780 0.632 0.446 0.443 LSN (ours) 0.797 0.633 0.480 0.425…”

Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

“…Reflection symmetry algorithms fall into two different categories depending on whether they detect sparse symmetries (straight lines or curves) [22,34,36,41,46,76] or a dense heatmap [15,16,67]. The most common sparse approach to detect reflection symmetry is to match up symmetric points or contours in the image to determine midpoint and direction of the symmetry axis [3,38,46,49,50,73].…”

“…Skeletonization, a related problem to reflection detection, has attracted a lot of attention recently [37,58,67,76]. Shen et al [59] use a deep CNN to learn symmetry at multiple scales and fuse the final output together.…”