Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video

09/22/2011
by   Alper Ayvaci, et al.
0

We describe an approach for segmenting an image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional, that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach.

READ FULL TEXT

page 1

page 3

page 7

page 9

page 13

page 14

research
05/16/2022

Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion

Motion, measured via optical flow, provides a powerful cue to discover a...
research
09/17/2019

Towards Object Detection from Motion

We present a novel approach to weakly supervised object detection. Inste...
research
03/29/2017

SeGAN: Segmenting and Generating the Invisible

Objects often occlude each other in scenes; Inferring their appearance b...
research
05/24/2016

Quickest Moving Object Detection

We present a general framework and method for simultaneous detection and...
research
12/19/2017

Tracking objects using 3D object proposals

3D object proposals, quickly detected regions in a 3D scene that likely ...
research
09/21/2017

SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

This paper presents SceneCut, a novel approach to jointly discover previ...

Please sign up or login with your details

Forgot password? Click here to reset