The Experts below are selected from a list of 2742 Experts worldwide ranked by ideXlab platform

Mario Maican - One of the best experts on this subject based on the ideXlab platform.

N Shrikhande - One of the best experts on this subject based on the ideXlab platform.

  • Quadric Surface fitting for sparse range data
    Systems Man and Cybernetics, 1991
    Co-Authors: X Cao, N Shrikhande
    Abstract:

    The authors present a systematic comparison of three commonly used least-squares based methods that describes the relationship between noise levels, patch sizes and reliability of Surface classification in computer vision. The different methods were tested on several sets of synthetic and real data. Complete sets of Quadric Surfaces were tested. In each case the standard deviation of the Gaussian noise ranged from 0 to 0.05. Four different patch sizes were tested in each case representing data from the entire Surface, half Surface, quarter Surface and from a small patch of the Surface. Similar tests were made for two different types of real range data, a sphere and a cylinder. Both synthetic and real data showed improvement in the results obtained by using the M-estimate method in the case of gross outlying points. >

Yuri Tschinkel - One of the best experts on this subject based on the ideXlab platform.

  • stable rationality of Quadric Surface bundles over Surfaces
    Acta Mathematica, 2018
    Co-Authors: Brendan Hassett, Alena Pirutka, Yuri Tschinkel
    Abstract:

    We study rationality properties of Quadric Surface bundles over the projective plane. We exhibit families of smooth projective complex fourfolds of this type over connected bases, containing both rational and non-rational fibers.

  • spaces of sections of Quadric Surface fibrations over curves
    Contemporary mathematics, 2011
    Co-Authors: Brendan Hassett, Yuri Tschinkel
    Abstract:

    We consider Quadric Surface fibrations over curves, defined over algebraically closed and finite fields. Our goal is to understand, in geometric terms, spaces of sections for such fibrations. We analyze varieties of maximal isotropic subspaces in the fibers as P^1-bundles over the discriminant double cover. When the P^1-bundle is suitably stable, we deduce effective estimates for the heights of sections over finite fields satisfying various approximation conditions. We also discuss the behavior of the spaces of sections as the base of the fibration acquires singularities.

Xiukun Yang - One of the best experts on this subject based on the ideXlab platform.

  • fingerprint image segmentation based on Quadric Surface model
    Lecture Notes in Computer Science, 2005
    Co-Authors: Yilong Yin, Yanrong Wang, Xiukun Yang
    Abstract:

    It is essential to segment fingerprint image from background effectively, which could improve image processing speed and fingerprint recognition accuracy. This paper proposes a novel fingerprint segmentation method at pixel level based on Quadric Surface model. Three parameters, Coherence, Mean and Variance of each pixel are extracted and spatial distribution model of fingerprint pixels is acquired and analyzed. Our study indicates that the performance of fingerprint image segmentation with a linear classifier is very limited. To deal with this problem, we develop a Quadric Surface formula for fingerprint image segmentation and acquire coefficients of the Quadric Surface formula using BP neural network trained on sample images. In order to evaluate the performance of our proposed method in comparison to linear classifiers, experiments are performed on public database “FVC2000 DB2”. Experimental result indicates that the proposed model can reduce pixel misclassification rate to 0.53%, which is significantly better than the linear classifier's misclassification rate of 6.8%.

  • method based on Quadric Surface model for fingerprint image segmentation
    Sensors and Command Control Communications and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, 2004
    Co-Authors: Yilong Yin, Xiukun Yang, Xv Chen, Haiyang Wang
    Abstract:

    It is important to segment fingerprint image from background accurately, which could reduce time consumed on image preprocessing and improve the reliability of minutiae extraction. Methods for fingerprint image segmentation can be divided into two categories: at block level and at pixel level. This paper presents a method based on Quadric Surface model for fingerprint image segmentation, which belongs to method at pixel level. First, spatial distribution model of pixels based on Coherence, Mean and Variance is acquired and analyzed. 200 typical fingerprint images are selected from FVC2000 and FVC2002. The class of the pixel of these images, namely, fingerprint part or background part, is recorded manually. Coherence, Mean and Variance of each pixel are extracted and spatial distribution model of pixels is built by the use of different colors in displaying pixels of fingerprint part and pixels of background part. The model indicates that it is not linear apart and the performance of fingerprint image segmentation with a linear classifier is very limited. Second, a Quadric Surface formula is presented for fingerprint image segmentation and coefficients of the Quadric Surface formula are acquired by BP neural network. Last, in order to evaluate the performance of our method in comparison to a method using linear classifier, experiments are performed on FVC2000 DB2. Manual inspection shows that the proposed method provides accurate high-resolution segmentation results. Experimental result shows that only 0.97% of the pixels are misclassified by our method, and linear classifier misclassifies 6.8% of the pixels.

Moussa Seydou - One of the best experts on this subject based on the ideXlab platform.