3D computer vision Structure from motion using Kalman filter SFM Kalman V9a 1
SFM Kalman V9a 1 3D computer vision Structure from motion using Kalman filter
Aims To obtain structure and camera motion from an image sequence Utilize inter-picture dynamics of a sequence Such as constant speed acceleration of the camera etc SFMKalman vga 2
SFM Kalman V9a 2 Aims • To obtain structure and camera motion from an image sequence • Utilize inter-picture dynamics of a sequence – Such as constant speed, acceleration of the camera etc
Tracking methods Kalman filtering suitable for systems with Gaussian noise Condensation(or called particle filter) suitable for systems with Non-Gaussian noise SFMKalman vga
SFM Kalman V9a 3 Tracking methods • Kalman filtering, suitable for systems with Gaussian noise • Condensation (or called particle filter), suitable for systems with Non-Gaussian noise
Part O Basic concept of Kalman filter SFMKalman vga
SFM Kalman V9a 4 Part 0 Basic concept of Kalman filter
Introduction A system(e.g. radar tracking a plane)can be modelled by a system transition dynamic function using the Newtons' law (linear) Measurement may contain noise(assume Ga aussian Kalman filter predict and update the system to reduce the effect of noise SFMKalman vga
Introduction • A system (e.g. radar tracking a plane) can be modelled by a system transition dynamic function using the Newtons’ law (linear). • Measurement may contain noise (assume Gaussian) • Kalman filter predict and update the system to reduce the effect of noise. SFM Kalman V9a 5