Low Complexity Approaches for Motion Estimation using FPGAs

Abstract

This thesis presents new approaches to image registration which have low levels of computational complexity and their implementations on FPGAs.

Firstly, a multi-pass image interpolation algorithm (MP-I2A) for image registration is proposed. By combining the optic flow estimation technique using the original image interpolation approach (I2A) with the least square estimate, the global motion between two images is estimated over several iterations. This new algorithm can estimate the affine motion model from the translation information computed for corresponding pairs of non-overlapping patches in two images. In addition to its insensitivity to noise, its performance is better than that of the Lucas-Kanade (LK) algorithm in terms of the number of iterations required for successful registration and the success rate.

Secondly, a modified version of the MP-I2A, called the kernel-warping algorithm (KWA), is developed. It can estimate parameters of the affine motion model and requires fewer iterations than the original and LK algorithms which increases the feasibility of its implementation on FPGAs. A two-FPGA system is developed to implement it for the case of translation.

Thirdly, the KWA is further developed to operate with binary (one-bit-per-pixel) images, whereby computations are much simpler than those of the original KWA as they use logic operations, such as AND, XOR, OR and NOT, instead of addition and multiplication. This significant reduction in computational complexity results in considerable savings in the hardware resources when implementing it on FPGAs.

Finally, experiments to determine the optimal parameters for an adaptive low complexity global motion estimation algorithm, which are important for its successful implementation on FPGAs, are performed based on MATLAB simulations. The resulting FPGA implementation has improved processing speed and reduced hardware requirement when compare with those of previous approaches.