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Percorrer por autor "Lourenco, Rui"

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  • 4D Light Field Disparity Map estimation using Krawtchouk Polynomials
    Publication . Lourenco, Rui; Rivero-Castillo, Daniel; Thomaz, Lucas A.; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Faria, Sergio M. M. de
    This work presents an improved method to estimate disparity maps obtained from light field cameras using a novel edge detection algorithm based on Krawtchouk polynomials. The proposed method takes advantage of these polynomials to determine gradient information and find the edges based on automatically estimated weak and strong thresholds. The calculated edges in the gray scale epipolar plane image representation of a light field are then used to improve the accuracy of object boundaries in the the disparity map. The proposed method achieves better results when compared to other edge detection algorithms, both in terms of objective and subjective quality, specifically by reducing the mean squared error and the artifacts in the object boundaries. Furthermore, on average, the proposed method outperforms the state-of-the-art depth estimation algorithms, in terms of the objective quality of the final disparity map, namely for the commonly used HCI dataset.
    2020-11Comunicação em conferência Acesso aberto Ver mais
  • Light Field Disparity Map Enhancement with Morphological Filtering
    Publication . Lourenco, Rui; Thomaz, Lucas A.; Silva, Eduardo A. B. da; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Faria, Sergio M. M. de
    Light field disparity estimation algorithms are comprised of two steps: an initial estimation step and a global optimization step. The initial estimation is often noisy and may contain high amplitude artefacts. Global optimization techniques might inadequately propagate these artefacts, providing suboptimal results. In this paper, an iterative morphological filter is proposed as an intermediate step or replacement to global optimization techniques. This algorithm iteratively filters the disparity map with an average of Open followed by Close and Close followed by Open morphological operations, enabling the removal of artefacts and noise, without adversely affecting the structure of the disparity map. The iterative open-close close-open filter attenuates the effect of artefacts and noise from an initial disparity estimation, achieving improvements of up to 90%, and more than 30%, on average, in terms of mean square error, when applied to the a structure-tensor-based initial estimation. In addition, the proposed method proves to be competitive with another state of the art algorithm, in terms of mean square error, and superior in terms of percentage of bad pixels.
    2021-02Comunicação em conferência Acesso restrito Ver mais