We propose to look at light fields from a dual space point of view. The advantage, in addition to revealing some new insights, is a framework that combines the benefits of many existing works. Using the well known two-plane-parameterization, we derive the duality between the 4-D light field and the 3-D world space. In the dual light field, rays become hyper points. We introduce the concept of hyperline. Then, cameras appear as hyperlines - camera hyperline (CHL) - mostly heterogeneous in color; scene points also appear as hyperlines - geometry hyperline (GHL) - mostly homogeneous in color. CHL's and GHL's are independent. The existence of one does not require or replace the other. When both exist, they cross each other at the dual ray hyper points. Both CHL and GHL-based light field rendering results are presented.

This paper presents a summary of a comprehensive research project involving development of a completely autonomous multirobot system for railway track maintenance applications. The specifications of this "real-world" application required us to consider a multirobot system conceptualization, rather than a single robot system.

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.

In this paper real-time implementation of multirobot convoying behavior utilizing neuro-fuzzy control is presented. With only nine rules, the follower convoys the leader closely and smoothly by varying its speed and changing its direction. When the leader stops, the follower stops very close to the specified safe distance (within an inch). The follower backs up quickly when the leader moves backward toward it (herding behavior).

A controller with blend of neural networks and fuzzy logic is proposed for a nonlinear control problem. Fuzzy logic membership functions are utilized to fuzzify input parameters; neural network interpolates the fuzzy rule set; after defuzzification, the output is used to train a smaller size of neural network; the weights of the later neural network can be adjusted to fine tune the controller. This controller successfully balances balls with one third of the required 27 rules. With learning capability, it approaches its goal more frequently in general.