TITLE:
Segmenting Salient Objects in 3D Point Clouds of Indoor Scenes Using Geodesic Distances
AUTHORS:
Shashank Bhatia, Stephan K. Chalup
KEYWORDS:
Saliency Detection; 3D Image Analysis; Image Segmentation
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
16,
2013
ABSTRACT: Visual attention mechanisms allow
humans to extract relevant and important information from raw input percepts.
Many applications in robotics and computer vision have modeled human visual
attention mechanisms using a bottom-up data centric approach. In contrast, recent studies in cognitive
science highlight advantages of a top-down approach to the attention
mechanisms, especially in applications involving goal-directed search. In this
paper, we propose a top-down approach for extracting salient objects/regions of space. The
top-down methodology first isolates different objects in an unorganized point
cloud, and compares each object for uniqueness. A measure of saliency using the
properties of geodesic distance on the object’s surface is defined. Our method
works on 3D point cloud data, and identifies salient objects of high curvature
and unique silhouette. These being the most unique features of a scene, are
robust to clutter, occlusions and view point changes. We provide the details of
the proposed method and initial experimental results.