An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Few Steps Towards 3d Active Vision
A Few Steps Towards 3d Active Vision
Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Vision and Inertial Sensor Cooperation Using Gravity as a Vertical Reference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Two-View Epipolar Geometry Estimation and Motion Segmentation by 4D Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation Using Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of Vision and Inertial Data for Motion and Structure Estimation
Journal of Robotic Systems
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Multiple motion segmentation with level sets
IEEE Transactions on Image Processing
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Motion segmentation is about separating moving objects from the background in video. If the motion is captured by a stationary camera, the solution is trivial. However, if the motion is captured by a moving camera, then the problem is hard to solve. Algorithms proposed so far usually assume the motion of the scene and the camera are both unknown. Consequently, such algorithms are generally computationally expensive and can be fragile for real-time vision applications. In this work, we propose to simplify the problem by using inertial sensors to measure the camera motion directly. The problem can then be simplified considerably, requiring only a simple line fitting algorithm in order to discriminate between the background and the moving objects. We believe the move towards incorporating inertial sensors to vision applications is essential in supporting robust real-time vision applications in future.