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
Edwin Hou
Third Advisor
Zoran Siveski
Abstract
In this thesis, we develop a Bitfirectional Associative Memory (BAM) based neural network to achieve partial shape recognition. To recognize objects which are partially occluded, 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. To achieve recognition, feature values (landmark values) of each model object are trained and stored in the network. Each memory cell is trained to store landmark values of a model object for all possible positions. Given a scene which may consist of several objects, landmarks in the scene are first extracted, and their corresponding landmark values are computed. Scene landmark values are entered to each trained memory cell. The memory cell is shown to be able to recall the position of model object in the scene. A heuristic measure is then computed to validate the recognition.
Recommended Citation
Liu, Xianjun, "Landmark-based partial shape recognition by a two-stage BAM neural network" (1990). Theses. 2832.
https://digitalcommons.njit.edu/theses/2832
