大位移变分光流计算的快速算法

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。     

曲面扩散的变分水平集方法

基于变分水平集方法提出了一种通用的曲面扩散变分模型,其数据项为演化曲面与原曲面的水平集函数Heaviside函数差的平方,规则项为基于整体曲率的通用函数,通过图像扩散模型中的总变差与该模型中的总曲率类比设计该规则项,以实现曲面扩散的任务。为了避免水平集函数的重新初始化,在本文的能量泛函中增加了水平集函数为符号距离函数的惩罚项。所得到的演化方程为4阶偏微分方程,对其对流项采用经典迎风差分格式离散,对其中的扩散项采用中心差分格式。最后通过数值算例验证了模型用于曲面光滑、边缘保持与边缘增强的可行性。     

运动细节估计的光流场方法

针对现有的光流方法在处理大位移和估计运动图像细节方面存在的问题,提出一种结合图像细节特征的变分光流场模型.首先通过增加特征点的对应,采用自适应的保持边缘的正则项以及引入occlusion检测函数对经典光流模型进行了改进;其次,采用基于变分框架下的高斯金字塔方法以及加权中值滤波的方法对所提出的模型进行求解.大量的实验结果...     

图像光流联合驱动的变分光流计算新方法

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。     

基于深度匹配的由稀疏到稠密大位移运动光流估计

针对非刚性大位移运动场景的光流计算准确性与鲁棒性问题, 提出一种基于深度匹配的由稀疏到稠密大位移运动光流估计方法. 首先利用深度匹配模型计算图像序列相邻帧的初始稀疏运动场; 其次采用网格化邻域支持优化模型筛选具有较高置信度的图像网格和匹配像素点, 获得鲁棒的稀疏运动场; 然后对稀疏运动场进行边缘保护稠密插值, 并设计全局能量泛函优化求解稠密光流场. 最后分别利用MPI-Sintel和KITTI数据库提供的测试图像集对本文方法和Classic + NL, DeepFlow, EpicFlow以及FlowNetS等变分模型、匹配策略和深度学习光流计算方法进行综合对比与分析, 实验结果表明本文方法相对于其他方法具有更高的光流计算精度, 尤其在非刚性大位移和运动遮挡区域具有更好的鲁棒性与可靠性.     

基于Potts模型的图像分割快速算法

Potts模型是一种通用的多相图像分割的变分模型,其极值问题需要迭代求解一系列偏微分方程。针对其求解过程计算效率较低的问题,提出一种基于对偶方法的快速算法。采用离散二值标记函数作为特征函数,利用Lagrange乘子法把对特征函数的约束加入能量泛函,然后引入对偶变量改写模型中的长度项,利用KKT的条件得到特征函数的二值解以及对偶变量的简单迭代格式。通过数值实验将该方法与梯度降方法、对偶方法和Split Bregman方法进行比较。实验结果表明,该算法的计算效率和分割准确性都高于其他三种方法。     

基于GPU图像去噪总变分对偶模型的并行计算

研究基于总变分(TV)的图像去噪问题,针对中央处理器(CPU)计算速度较慢的问题,提出了在图像处理器(GPU)上并行计算的方法。考虑总变分最小问题的对偶模型,建立原始变量与对偶变量的关系,采用梯度投影算法求解对偶变量。数值实验分别在GPU与CPU上进行。实验结果表明,总变分去噪模型对偶算法在GPU设备上执行的效率高于在CPU上执行的效率,并且随着图像尺寸的增大,GPU并行计算的优势更加突出。     

基于多尺度V1-MT前馈模型的光流计算方法

针对在大位移、弱纹理等情况下光流信息计算误差较大的问题,提出一种基于改进V1-MT前馈模型的光流计算方法。以视频序列作为输入,分别计算初级视皮层和中颞叶(MT)细胞的响应,分解MT细胞的响应得到光流信息。采用多尺度和由粗到精的方法,解决大位移情况下的光流计算问题,采用基于双边滤波的插值方法,融合邻域运动信息,以估计弱纹理区的光流信息。仿真结果表明,与其他基于生物模型的计算方法相比,该方法能更为精确地计算光流信息。     

基于运动优化语义分割的变分光流计算方法

针对光照变化和大位移运动等复杂场景下图像序列变分光流计算的边缘模糊与过度分割问题,文中提出基于运动优化语义分割的变分光流计算方法.首先,根据图像局部区域的去均值归一化匹配模型,构建变分光流计算能量泛函.然后,利用去均值归一化互相关光流估计结果,获取图像运动边界信息,优化语义分割,设计运动约束语义分割的变分光流计算模型.最后,融合图像不同标签区域光流,获得光流计算结果.在Middlebury、UCF101数据库上的实验表明,文中方法的光流估计精度与鲁棒性较高,尤其对光照变化、弱纹理和大位移运动等复杂场景的边缘保护效果较优.     

一种改进的变分光流计算方法

提出一种改进的变分光流算法的能量泛函,该能量泛函的数据项由灰度不变假设和Hessian矩阵不变假设组成,并与Lucas局部光流一致方法相结合。平滑项的设计采用先各项同性平滑再各项异性平滑的策略,其中引入图像一致增强思想。实验结果证明,运用该方法进行光流计算的效果比以往变分方法有所改进。     

Weighted and extended total variation for image restoration and decomposition

In various information processing tasks obtaining regularized versions of a noisy or corrupted image data is often a prerequisite for successful use of classical image analysis algorithms. Image restoration and decomposition methods need to be robust if they are to be useful in practice. In particular, this property has to be verified in engineering and scientific applications. By robustness, we mean that the performance of an algorithm should not be affected significantly by small deviations from the assumed model. In image processing, total variation (TV) is a powerful tool to increase robustness. In this paper, we define several concepts that are useful in robust restoration and robust decomposition. We propose two extended total variation models, weighted total variation (WTV) and extended total variation (ETV). We state generic approaches. The idea is to replace the TV penalty term with more general terms. The motivation is to increase the robustness of ROF (Rudin, Osher, Fatemi) model and to prevent the staircasing effect due to this method. Moreover, rewriting the non-convex sublinear regularizing terms as WTV, we provide a new approach to perform minimization via the well-known Chambolle's algorithm. The implementation is then more straightforward than the half-quadratic algorithm. The behavior of image decomposition methods is also a challenging problem, which is closely related to anisotropic diffusion. ETV leads to an anisotropic decomposition close to edges improving the robustness. It allows to respect desired geometric properties during the restoration, and to control more precisely the regularization process. We also discuss why compression algorithms can be an objective method to evaluate the image decomposition quality.     

一种空间自适应的Retinex变分校正模型

针对传统Retinex变分模型采用相同的权重容易引起虚假痕迹的缺陷,通过引入差分特征值作为边缘指示算子,构造了一种具有空间自适应调节能力的Retinex变分校正模型,该模型能够利用影像空间域信息来控制变分校正模型在不同像素点的约束强度,在边缘区域施加较小的正则化约束保持影像的边缘特征;而在平坦区域施加较大的正则化约束。同时根据反射分量的物理性质,在变分校正模型中对其施加均值逼近灰度中值约束防止局部曝光过度。本文用分裂Bregman迭代法实现对该变分校正模型的最优化求解,利用模拟影像和真实影像进行实验,并与传统方法进行比较,结果表明,本文方法能够消除影像灰度不均匀现象,同时大幅提高计算效率。     

Semi-supervised learning with regularized Laplacian

We study a semi-supervised learning method based on the similarity graph and regularized Laplacian. We give convenient optimization formulation of the regularized Laplacian method and establish its various properties. In particular, we show that the kernel of the method can be interpreted in terms of discrete and continuous-time random walks and possesses several important properties of proximity measures. Both optimization and linear algebra methods can be used for efficient computation of the classification functions. We demonstrate on numerical examples that the regularized Laplacian method is robust with respect to the choice of the regularization parameter and outperforms the Laplacian-based heat kernel methods.     

Optic flow estimation by a Hopfield neural network using geometrical constraints

Sparse optic flow maps are general enough to obtain useful information about camera motion. Usually, correspondences among features over an image sequence are estimated by radiometric similarity. When the camera moves under known conditions, global geometrical constraints can be introduced in order to obtain a more robust estimation of the optic flow. In this paper, a method is proposed for the computation of a robust sparse optic flow (OF) which integrates the geometrical constraints induced by camera motion to verify the correspondences obtained by radiometric-similarity-based techniques. A raw OF map is estimated by matching features by correlation. The verification of the resulting correspondences is formulated as an optimization problem that is implemented on a Hopfield neural network (HNN). Additional constraints imposed in the energy function permit us to achieve a subpixel accuracy in the image locations of matched features. Convergence of the HNN is reached in a small enough number of iterations to make the proposed method suitable for real-time processing. It is shown that the proposed method is also suitable for identifying independently moving objects in front of a moving vehicle. Received: 26 December 1995 / Accepted: 20 February 1997     

Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint

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.     

Optic flow based on multi-scale anchor point movement and discontinuity-preserving regularization

We introduce a new method to determine the flow field of an image sequence using multi-scale anchor points. These anchor points manifest themselves in the scale-space representation of an image. The novelty of our method lies largely in the fact that the relation between the scale-space anchor points and the flow field is formulated in terms of soft constraints in a variational method. This leads to an algorithm for the computation of the flow field that differs fundamentally from previously proposed ones based on hard constraints. We show a significant performance increase when our method is applied to the Yosemite image sequence, a standard and well-established benchmark sequence in optic flow research. Also, it is shown that this performance is not sensitive to slight changes in the two parameters used and that, with the same parameter values, our method yields very good results in the Rubber Whale image sequence as well.     

Time-to-Collision estimation from motion based on primate visual processing

A population coded algorithm, built on established models of motion processing in the primate visual system, computes the time-to-collision of a mobile robot to real-world environmental objects from video imagery. A set of four transformations starts with motion energy, a spatiotemporal frequency based computation of motion features. The following processing stages extract image velocity features similar to, but distinct from, optic flow; "translation" features, which account for velocity errors including those resulting from the aperture problem; and finally, estimate the time-to-collision. Biologically motivated population coding distinguishes this approach from previous methods based on optic flow. A comparison of the population coded approach with the popular optic flow algorithm of Lucas and Kanade against three types of approaching objects shows that the proposed method produces more robust time-to-collision information from a real world input stimulus in the presence of the aperture problem and other noise sources. The improved performance comes with increased computational cost, which would ideally be mitigated by special purpose hardware architectures.     

Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods

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.