The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Direct recovering of surface structure characterized by an Nth degree polynomial equation using the UOFF approach
Author:	Salhi, Mazen A.
View Online:	njit-etd1991-035 ([vi], 81 pages ~ 6.3 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Master of Science
Program:	Electrical Engineering
Document Type:	Thesis
Advisory Committee:	Shi, Yun Q. (Committee chair) Shu, C.Q. (Committee member) Carpinelli, John D. (Committee member)
Date:	1991-10
Keywords:	Computer vision. Surfaces --Computer simulation. Image processing.
Availability:	Unrestricted
Abstract:	There are two different approaches for estimation of structure and/or motion of objects in the computer vision community today. One is the feature correspondence method, and the other is the optical flow method [1]. There are many difficulties and limitations encountered with the feature correspondence method, while the optical flow method is more feasible, but requires a substantial amount of extra calculations if the optical flow is to be computed as an intermediate step. Direct methods have been developed [2-4], that use the optical flow approach, but avoid computing the full optical flow field as an intermediate step for recovering structure and motion. The unified optical flow field theory was recently established in [5]. It is an extension of the optical flow (UOFF) [1] to stereo imagery. Based on the UOFF, a direct method is developed to reconstruct an Alpha shape surface structure characterized by an third degree polynomial equation, and a Sphere surface characterized by a second degree polynomial [6]. This thesis work uses the methods developed in [5,6], to reconstruct the third degree polynomial describing a surface. The main difference from the simulation results obtained in [6], is that in this case, one of the two surfaces tested is a third order, unbounded surface, and that tbe image gradients are computed directly from the image data, with no prior knowledge of the surface gray function distribution. Another important difference is that the gray levels of the surface are quantized in this work; i.e., the computations are done using integer image data, not the continuous gray levels as in [6]. These differences contribute to proving that the UOFF technique can be used in a practical manner, and with good results. Further discussions of the contributions of this work are included in the last chapter.