To the Graduate Council: I am submitting herewith a thesis written by ...

Chapter 6: Conclusions 82complexity. In the spirit of Claude Shannon's definition of information, this measurereflects the amount of shape information that an object surface possesses. Objects andscenes with nearly constant curvature contain relatively low values of shapeinformation, while other objects and scenes with significant variation in curvatureexhibit fairly large values. Though our idea of using curvature as a feature fordescription is not new, our attempt to quantify the perceptual complexity of a surfaceusing information theory is. Since we are describing surfaces using our approach,occluded scenes such as the one that was obtained real-time can also be represented witha good degree of confidence towards object detection.6.2 Directions for the FutureWe feel that the process of creating 3D models has scope for improvement. Using oursystem design, it takes nearly six hours to scan, fuse and integrate multiple views intoits complete 3D triangle mesh model. We did not consider optimizing views forminimizing scan time. We can approach view planning as a sensor placement problemfor better efficiency. The solution to the problem will also enhance the under vehiclescene modeling. Our system design also has a lot of scope for improvement towardsvehicle inspection. Instead of using a conveyer belt that houses the range sensor, itwould be better to have a calibrated setup to control the relative motion. We wouldalso like to have the scanning mechanism redesigned to be robot mountable toautomate the scanning process.In the typical context of aligning range scans of an object in order to create a completemodel of that object we would like to point out the possibility of application of ouralgorithm to surface registration. Surface registration is a feature dependent process.More features improve registration. By features, here we mean unique geometricinformation. We believe that representing a range scan as a cluster of shape measuresaround a neighborhood would help us recover the rigid transformation from anotherview of the same object that has some common information (overlap). A multi-scalehierarchical informational approach should be a good start for this process.Object recognition is an extremely difficult task with most current solutions limited to avery constrained and restricted problem domain. Although we do not claimcontributions in terms of recognition as yet, we are encouraged by the results in thisthesis that it might serve as a first step in the recognition pipeline. Shapiro andStockman [Shapiro, 2001] suggest commonly used paradigms for object recognitionwhere the method chosen depends heavily on the application. They discuss twoparadigms that use part (region) relationships to move away from a geometric definitionof an object to a more symbolic one. Our algorithm benefits the creation of such asymbolic graph representation from a mesh representation. We would like to performrigorous experiments on partial graph matching for threat detection and model-based