A sequence of images in multiple views rather than a single image from a single view is of great advantage for robust visual recognition and pose estimation of 3D objects in noisy and visually not-so-friendly environments (due to texture, occlusion, illumination, and camera pose). In this project, we have been developing a particle filter based probabilistic method for recognizing an object and estimating its pose based on a sequence of images, where the probability distribution of object pose in 3D space is represented by particles. The particles are updated by consecutive observations in a sequence of images and are converged to a single pose.

* Multiple Evidence and Model Matching in a Sequence of Images by Particle Filtering