We propose a framework for simultaneous phase unwrapping and multipath interference cancellation (SPUMIC) in homodyne time-of-flight (ToF) cameras. Our multi-frequency acquisition framework is based on parametric modeling of the multipath interference phenomena. We use robust spectral estimation methods with low computational complexity to detect and estimate multipath parameters. Using simulations and analysis we demonstrate that our proposed solution is implementable in real-time on existing ToF cameras without requiring any hardware modifications.

We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of Interval Analysis this method converges to the global solution with mathematical certainty and arbitrary accuracy and the only input information it requires from the user are a set of point correspondences and a search interval. The cost function is based on the Huang-Faugeras constraint of the essential matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, experimental results are presented.

Existing autocalibration techniques use numerical optimization algorithms that are prone to the problem of local minima. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of interval analysis this method is guaranteed to converge to the global solution with mathematical certainty and arbitrary accuracy, and the only input information it requires from the user is a set of point correspondences and a search box. The cost function is based on the Huang-Faugeras constraint of the fundamental matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, some experimental results on synthetic images are presented.

We have designed and implemented a system for real-time detection of 2-D features on a reconfigurable computer based on Field Programmable Gate Arrays (FPGA's). We envision this device as the front-end of a system able to track image features in real-time control applications like autonomous vehicle navigation. The algorithm employed to select good features is inspired by Tomasi and Kanade's method. Compared to the original method, the algorithm that we have devised does not require any floating point or transcendental operations, and can be implemented either in hardware or in software. Moreover, it maps efficiently into a highly pipelined architecture, well suited to implementation in FPGA technology. We have implemented the algorithm on a low-cost reconfigurable computer and have observed reliable operation on an image stream generated by a standard NTSC video camera at 30 Hz.

An expression in closed form is derived for the recognition error vs. rejection rate of optical character or word recognition systems. This expression allows to define a lower bound for the error rate of any recognition system employing a rejection process based on the definition of a confidence threshold. This relation has also proved to be useful to make a quantitative comparison between two confidence computation methods implemented in a system for reading USA Census '90 hand-written forms. The newly proposed method is based upon a confidence model integrating single-character confidence levels, digram statistics and other information from the dictionary matching phase. At a 50% rejection rate, the field error rate calculated using the new confidence computation algorithm decreased from 47.7% to 44.6%, which represents a considerable improvement, given a theoretical lower bound of 40.8% on the error rate.