S
Shahriar Negahdaripour
Researcher at University of Miami
Publications - 137
Citations - 5009
Shahriar Negahdaripour is an academic researcher from University of Miami. The author has contributed to research in topics: Motion estimation & Sonar. The author has an hindex of 30, co-authored 137 publications receiving 4676 citations. Previous affiliations of Shahriar Negahdaripour include University of Hawaii & University of Hawaii at Manoa.
Papers
More filters
Journal ArticleDOI
Closed-form solution of absolute orientation using orthonormal matrices
TL;DR: In this paper, a closed-form solution to the least square problem for three or more points is presented, which requires the computation of the square root of a symmetric matrix, and the best scale is equal to the ratio of the root-mean-square deviations of the coordinates in the two systems from their respective centroids.
Journal ArticleDOI
Direct Passive Navigation
TL;DR: This correspondence presents two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation.
Journal ArticleDOI
Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis
TL;DR: A revised definition of optical flow is proposed to overcome shortcomings in interpreting optical flow merely as a geometric transformation field and leads to a general framework for the investigation of problems in dynamic scene analysis, based on the integration and unified treatment of both geometric and radiometric cues in time-varying imagery.
Journal ArticleDOI
A new method for the extraction of fetal ECG from the composite abdominal signal
TL;DR: A wavelet transform-based method to extract the fetal electrocardiogram (ECG) from the composite abdominal signal using the modulus maxima in the wavelet domain, which exploits the most distinct features of the signal, leading to more robustness with respect to signal perturbations.
Journal ArticleDOI
Fast image blending using watersheds and graph cuts
TL;DR: A novel approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion is presented, which allows the efficient creation of large mosaics, without user intervention.