Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Point Correspondence in the Presence of Occlusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Feature Points: A New Algorithm
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Drift-correcting template update strategy for precision feature point tracking
Image and Vision Computing
Structured errors in reconstruction methods for Non-Cartesian MR data
Computers in Biology and Medicine
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Object tracking algorithms found extensively in the computer vision literature either are inhibited by various assumptions such as simplicity of motion and shape characteristics of objects or are overly sensitive to noise. We propose and successfully test two new weighting functions for a feature-based object-tracking algorithm to achieve superior performance in tracking motion of non-rigid objects under noisy conditions. We present the implications of using the weighting functions in real and synthetic image sequences to overcome the noise produced at acquisition source (charge coupled device-CCD), or in the background environment. We also present a mechanism for determining the optimal weighting function based on image parameters, more specifically the edge characteristics of objects in the image.