Catálogo de publicaciones - libros

This paper presents a vision-based control for positioning a camera with respect to an unknown piecewise planar object. We introduce a novel homography-based approach that integrates information from multiple homographies to reliably estimate the relative displacement of the camera. This approach is robust to image measurement errors and provides a stable estimate of the camera motion that is free from degeneracies in the task space. We also develop a new control formulation that meets the contradictory requirements of producing a decoupled camera trajectory and ensuring object visibility by only utilizing the homography relating the two views. Experimental results validate the efficiency and robustness of our approach and demonstrate its applicability.

Computer Vision theory is firmly rooted in Projective Geometry, whereby geometric objects can be effectively modeled by homogeneous vectors. We begin from Gauss’s 200 year old theorem of least squares to derive a generic algorithm for the direct estimation of homogeneous vectors. We uncover the common link of previous methods, showing that direct estimation is not an ill-conditioned problem as is the popular belief, but has merely been an ill-solved problem. Results show improvements in goodness-of-fit and numerical stability, and demonstrate that “data normalization” is unnecessary for a well-founded algorithm.

In this paper, we propose a novel Octantal Nearest-neighbor Structure and core points based fingerprint matching scheme. A novel fingerprint feature named the octantal nearest-neighbor structure (ONNS) is defined. Based on the ONNS, the minutiae pairing algorithm is conducted to find the corresponding minutiae pairs, and a novel algorithm is developed to evaluate the translational and rotational parameters between the input and the template fingerprints. Core point based orientation pairing is performed thereafter. Matching score is calculated. Experimental results on the FVC2004 fingerprint databases show the good performance of the proposed algorithm.

This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the performance of fingerprint verification. The proposed fusion algorithm combines decision induced match scores of minutiae, ridge, fingercode and pore based fingerprint verification algorithms and provides an improvement of at least 8.1% in the verification accuracy compared to the individual algorithms. Further, proposed fusion algorithm outperforms by at least 2.52% when compared with existing fusion algorithms. We also found that the use of Dempster’s rule of conditioning reduces the training time by approximately 191 seconds.

In this paper we proposed a new enhancement technique that is based on the integration of Decimation Free Directional responses of the Decimation Free Directional Filter Banks (DDFB), adaptive mean filtering and the eigen decomposition of the Hessian matrix. By decomposing the input fingerprint image into decimation free directional images, it is easy to remove the noise directionally by means of adaptive mean filtering and further eigen decomposition of the Hessian matrix was used for the segmentation purpose. As the input fingerprint image is not uniformly illuminated so we have used the bandpass filter for the elimination of non-uniform illumination and for the creation of frequency ridge image before giving it to DDFB. The final enhanced result is constructed on a block-by-block basis by comparing energy of all the directional images and picking one that provides maximum energy.