Identification Of Partially Obscured Objects In Two Dimensions By Matching Of Noisy Characteristic Curves
This research explores a novel approach to identifying partially obscured objects in two dimensions. It focuses on matching noisy characteristic curves extracted from images to overcome the challenges posed by occlusion and imperfect data. The proposed method aims to improve the accuracy and robustness of object recognition systems in scenarios where objects are partially hidden or data is corrupted by noise, offering potential advancements in fields like robotics and image analysis.