Document Type
Thesis
Date of Award
9-30-1990
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Nirwan Ansari
Second Advisor
Chung H. Lu
Third Advisor
Irving Y. Wang
Abstract
In this thesis, we introduce a new thethod to achieve partial shape recognition by means of a modified Hopfield neural network. To recognize partially visible object, we represent each object by a set of "landmarks." The landmarks of an object are points of interest relative to the object that have important shape attributes. Given a scene consisting of partially occluded objects, a model object in the scene is hypothesized by matching the landmarks of the model with those in the scene. A measure of similarity between two landmarks, one from a model and the other from the scene, is needed to perform this matching. A local shape measure, known as the sphericity of a triangular transformation, is used. The hypothesis of a model object in a scene is completed by matching the model landmarks with the scene landmarks. The landmark matching task is performed by a Modified Hopfield Neural Network. The location of the model in the scene is estimated with a least squares fit among the matched landmarks. A heuristic measure is then computed to decide if the model is in the scene.
Recommended Citation
Li, Kuowei, "Landmark-based shape recognition by a modified hopfield neural network" (1990). Theses. 2827.
https://digitalcommons.njit.edu/theses/2827
