共查询到19条相似文献,搜索用时 62 毫秒
1.
目的 多尺度方法的提出解决了传统HS(Horn Schunck)算法不能计算大位移光流的问题,但同时也增加了迭代运算的步数。为加快迭代收敛速度,研究大位移变分光流计算的快速算法,并分析其性能。方法 将用于加快变分图像处理迭代运算的Split Bregman方法、对偶方法和交替方向乘子法应用到大位移光流计算中。结果 分别进行了精度、迭代步数、运行时间的对比实验。引入3种快速方法的模型均能够在保证精度的同时,在较少时间内计算出图像序列的光流场,所需时间为传统方法的11%~42%。结论 将3种快速方法应用到大位移变分光流计算中,对于不同图像序列均可以较大地提高计算效率。 相似文献
2.
3.
HS(Horn&Schunck)方法是光流计算中的经典方法之一。在经典HS方法中,图像中两点间的灰度变化被假定为线性的,而实际上灰度变化是非线性的。因此,在HS算法中最小均方差迭代的最终收敛点会产生偏移,从而导致光流计算结果的不准确。为此,详细分析了灰度估计不准确造成的偏差,提出了一种改进HS算法。实验部分给出了改进算法和其他经典光流计算方法的计算结果比较。实验结果表明,改进HS算法可以得到较好的计算结果,并能明显减少光流计算的迭代次数。 相似文献
4.
多相图像分割的Split-Bregman方法及对偶方法 总被引:1,自引:0,他引:1
变分水平集方法为多相图像分割提供了统一框架,但其能量泛函的局部极值问题和较低的计算效率制约着该类方法的应用,文中针对此问题提出一种改进模型和方法.首先将两相图像分割的全局凸优化模型推广到多相图像分割,建立了多相图像分割的交替凸优化变分模型,以改善传统模型的局部极值问题;然后提出了相应的快速Split-Bregman方法和对偶方法来提高计算效率,其中Split-Bregman方法通过引入辅助变量将凸松弛后的变分问题转化为简单的Poisson方程和精确的软阈值公式,对偶方法则通过引入对偶变量将该问题转化为对偶变量的半隐式迭代计算和主变量的精确计算公式.文中的改进模型适用于任意多相图像分割,且对二维和三维图像分割具有相同形式,可用于三维图像的多对象自动形状恢复.最后通过多个数值算例验证了文中方法的计算效率优于传统的方法. 相似文献
5.
基于对偶变量变分原理和两端动量独立变量的保辛方法 总被引:3,自引:2,他引:3
将广义位移和动量同时用拉格朗日多项式近似,并选择积分区间两端动量为独立变量,然后基于对偶变量变分原理导出了哈密顿系统的离散正则变换和对应的数值积分保辛算法.当位移和动量的拉格朗日多项式近似阶数满足一定条件时,可以自然导出保辛算法的不动点格式.通过数值算例讨论了位移和动量采用不同阶次插值所需最少Gauss积分点个数,并讨论了位移插值阶数、动量插值阶数以及Gauss积分点个数对保辛算法精度的影响,说明了上述不动点格式恰好是一种最优格式. 相似文献
6.
7.
结合深度学习模型实现光流端到端的计算是当前计算机视觉领域的一个研究热点.文中对基于深度学习的光流估计方法进行总结和梳理.首先,介绍了光流的起源与定义;其次,总结了现有的数据集合和评价指标;最重要的是,着重从3个方面回顾了深度光流估计方法,包括有监督的深度光流估计方法、无监督的深度光流估计方法以及对现有光流估计方法的性能... 相似文献
8.
提出一种基于图像光流联合驱动的变分光流计算方法。数据项采用灰度守恒和梯度守恒相结合、局部约束与全局约束结合的思想,并引入正则化因子提高计算精度。平滑项采用图像与光流联合驱动的各向异性平滑策略,将数据项与平滑项紧密地联系起来,并通过设计扩散张量的两个本征值来控制光流扩散速度。最后采用多分辨率分层细化策略解决大位移问题。实验结果证明,该计算模型在背景复杂、光照变化、运动边界等情况的光流计算具有很好的效果。 相似文献
9.
基于梯度的光流计算方法中梯度计算对性能的影响 总被引:6,自引:1,他引:6
基于梯度的方法是光流计算中很重要的一类方法,而梯度的计算对整个算法的性能有着重要的影响。文章考察了几种常用梯度算子对光流计算的影响,并给出了理论分析。实验结果证明理论分析是正确的。 相似文献
10.
高质量的稠密光流算法计算复杂度很高,因此计算速度成为制约其在实际系统中应用的重要原因。针对这一问题,利用现场可编程门阵列(FPGA)的细粒度并行特性,实现了一种高质量的稠密光流算法CBG(Combined- Brightness-Gradient)的硬件加速器。实验结果表明,在FPGA工作频率200 MHz,计算全部像素对应的光流信息的情况下,该系统处理分辨率为316×252的图像序列的帧频可达40 frame/s。 相似文献
11.
Nonquadratic variational regularization is a well-known and powerful approach for the discontinuity-preserving computation of optic flow. In the present paper, we consider an extension of flow-driven spatial smoothness terms to spatio-temporal regularizers. Our method leads to a rotationally invariant and time symmetric convex optimization problem. It has a unique minimum that can be found in a stable way by standard algorithms such as gradient descent. Since the convexity guarantees global convergence, the result does not depend on the flow initialization. Two iterative algorithms are presented that are not difficult to implement. Qualitative and quantitative results for synthetic and real-world scenes show that our spatio-temporal approach (i) improves optic flow fields significantly, (ii) smoothes out background noise efficiently, and (iii) preserves true motion boundaries. The computational costs are only 50% higher than for a pure spatial approach applied to all subsequent image pairs of the sequence. 相似文献
12.
五点约束最小二乘法估计光流速度场 总被引:3,自引:0,他引:3
提出一种二维光流场快速计算算法.首先求取当前像素点光流约束线与其8邻域像素点所对应8条光流约束线的交点;其次从8个交点中选取几何位置处于中间的4点,即速度处于中间值,且相互接近的4点,并以其对应的像素点与当前像素点一起构成5置信点;利用5置信点的光流约束方程构造一超定的方程组;最后利用最小二乘法求取当前像素点的光流速度. 相似文献
13.
A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods 总被引:1,自引:0,他引:1
Andrés Bruhn Joachim Weickert Timo Kohlberger Christoph Schnörr 《International Journal of Computer Vision》2006,70(3):257-277
Variational methods are among the most accurate techniques for estimating the optic flow. They yield dense flow fields and
can be designed such that they preserve discontinuities, estimate large displacements correctly and perform well under noise
and varying illumination. However, such adaptations render the minimisation of the underlying energy functional very expensive
in terms of computational costs: Typically one or more large linear or nonlinear equation systems have to be solved in order
to obtain the desired solution. Consequently, variational methods are considered to be too slow for real-time performance.
In our paper we address this problem in two ways: (i) We present a numerical framework based on bidirectional multigrid methods
for accelerating a broad class of variational optic flow methods with different constancy and smoothness assumptions. Thereby,
our work focuses particularly on regularisation strategies that preserve discontinuities. (ii) We show by the examples of
five classical and two recent variational techniques that real-time performance is possible in all cases—even for very complex
optic flow models that offer high accuracy. Experiments show that frame rates up to 63 dense flow fields per second for image
sequences of size 160 × 120 can be achieved on a standard PC. Compared to classical iterative methods this constitutes a speedup
of two to four orders of magnitude. 相似文献
14.
15.
Nils Papenberg Andrés Bruhn Thomas Brox Stephan Didas Joachim Weickert 《International Journal of Computer Vision》2006,67(2):141-158
In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy
assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the
Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is
also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an
efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that
there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since
our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation
issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance
of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments
also confirm excellent robustness under noise and insensitivity to parameter variations. 相似文献
16.
Reliable and Efficient Computation of Optical Flow 总被引:3,自引:3,他引:3
In this paper, we present two very efficient and accurate algorithms for computing optical flow. The first is a modified gradient-based regularization method, and the other is an SSD-based regularization method. For the gradient-based method, to amend the errors in the discrete image flow equation caused by numerical differentiation as well as temporal and spatial aliasing in the brightness function, we propose to selectively combine the image flow constraint and a contour-based flow constraint into the data constraint by using a reliability measure. Each data constraint is appropriately normalized to obtain an approximate minimum distance (of the data point to the linear flow equation) constraint instead of the conventional linear flow constraint. These modifications lead to robust and accurate optical flow estimation. We propose an incomplete Cholesky preconditioned conjugate gradient algorithm to solve the resulting large and sparse linear system efficiently. Our SSD-based regularization method uses a normalized SSD measure (based on a similar reasoning as in the gradient-based scheme) as the data constraint in a regularization framework. The nonlinear conjugate gradient algorithm in conjunction with an incomplete Cholesky preconditioning is developed to solve the resulting nonlinear minimization problem. Experimental results on synthetic and real image sequences for these two algorithms are given to demonstrate their performance in comparison with competing methods reported in literature. 相似文献
17.
Andrés Bruhn Joachim Weickert Christoph Schnörr 《International Journal of Computer Vision》2005,61(3):211-231
Differential methods belong to the most widely used techniques for optic flow computation in image sequences. They can be classified into local methods such as the Lucas–Kanade technique or Bigün's structure tensor method, and into global methods such as the Horn/Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. The goal of this paper is to contribute to a better understanding and the design of novel differential methods in four ways; (i) We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for optic flow computation. (ii) This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches: It yields dense flow fields that are robust against noise. (iii) Spatiotemporal and nonlinear extensions as well as multiresolution frameworks are presented for this hybrid method. (iv) We propose a simple confidence measure for optic flow methods that minimise energy functionals. It allows to sparsify a dense flow field gradually, depending on the reliability required for the resulting flow. Comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure. 相似文献
18.
The optic flow field is defined such that along integral lines of the field the image intensity remains constant. For each
time instance in an image sequence poles are created in the optic flow field at the position of spatial image singularities.
We describe the generic flow singularities and the generic transitions of these over time. For classic analytic flow fields
the classification of the generic topology is based on points of vanishing flow which can be further subdivided into repellers,
attractors, whirls, and combinations hereof. We point out the resemblance, but also the important differences between the
structure of the classical analytic flow field, and the structure of the optic flow field expressed through its normal flow.
We conclude by giving a operational scheme for the detection of these singularities and events; and apply the scheme to two
different examples within attention mechanism and the degree of turbulence in a flow field respectively. 相似文献