局部与全局结合的彩色图像序列光流场模型

针对彩色图像序列光流场计算问题并结合现有光流场计算模型,提出3种将局部彩色光流计算模型与全局彩色光流计算模型相结合的混合光流估计模型,从而有效利用局部光流模型计算精度高及全局光流模型可得到致密光流场的优点,并将3种模型进行比较,给出数值实验结果。     

全局光流场估计技术及展望

光流法是运动分析最为重要的技术之一,其能够从相邻帧图像中恢复出目标物体以及背景的运动信息,从而实现目标检测、运动跟踪以及特征识别等.文中从全局光流计算的基本原理出发,详细分析了非均匀光照校正、能量函数构建和优化方案设计等全局光流场估计技术的关键步骤;同时深入探讨了全局光流场估计技术存在的大位移计算、遮挡检测、弱纹理运动估计等难题.最后对全局光流场估计技术所面临的挑战进行了分析,并展望了其未来的发展方向.     

基于三参数模型的快速全局运动估计

提出了一种新的全局运动估计方法——基于三参数模型的快速全局运动估计.新的参数模型在保证准确性的同时,使用更少的参数来描述和估计全局运动,从而简化了计算复杂度.此外,对光流场计算做出了两方面改进：（1）提出了宏块预判的方法,计算光流场前对宏块的梯度信息进行预分析,通过减少参与计算的宏块数目提高光流场的计算速度;（2）提出了快速估计宏块运动向量的方法,在块匹配的过程中同时考虑图像的梯度和灰度信息,通过引入更多的约束提高运动向量的计算速度.实验证明了该方法的有效性.     

基于梯度光流场计算方法的一种改进

提出了一种新的改进光流场技术计算方法。它采用类似差分的方法从图像序列的多帧图像中检测出运动目标区域。通过代数运算减轻了Hom算法中全局约束对运动边界的影响。实验证明该方法是有效的。     

机器人视觉中彩色时变图像光流场计算的综述

本文对机器人视觉中彩色时变图像的光流场计算方法做了全面概述．彩色时变图像光 流场计算的基础是基本等式,列举了目前几种基于彩色图像光流场计算方法,并提出目前彩 色时变图像光流场计算中存在的几个问题,总结出今后彩色时变图像光流场计算的发展趋势 ,以期对彩色时变图像光流场的计算理论和方法提供较为具体的指导方案．     

一种面向移动机器人的光流场计算方法

光流场的计算是移动机器人领域一个很重要的研究课题,但是现有的光流场计算方法并未充分利用移动机器人的特点。文章假设移动机器人配备了双目视觉系统和码盘,在此基础上提出了一种基于立体匹配技术的光流场计算方法。通过对实际采集的图片的对比试验,表明该方法较之传统方法,具有速度更快,准确性更高的优点。     

一种基于特征信息种子点选取的多层次流线可视化

流线是流场可视化的一种重要的方法,而种子点的选取是生成流线一个关键问题.为此,提出一种新的流线种子点放置算法,将信息熵和临界点有机地结合起来,用信息熵找出流场变化剧烈的区域,然后利用临界点的拓扑结构信息对局部区域的种子点放置做精细控制,从而不仅能够覆盖流场中变化剧烈的区域,而且考虑了流场的拓扑结构特征,可以更加有效地可视化流场中的信息.为了使全局和局部区域的流场都高精度显示,设计了基于图像空间的交互操作,对于流场的全局展示,利用改进的算法放置种子点,在流场的宏观显示上能够尽可能多地绘制出流场信息,对于局部细节根据文中设计的交互形式,只针对用户放大的局部进行高清晰绘制,这样既可以对全局采用适度的流线高清晰显示流场整体情况而无视觉混乱,而放大到局部时又有足够多的流线刻画局部流场的细节特征.实验结果证明了算法的有效性.     

基于局部微分光流的运动对象分割

运动对象分割是研究从场景的图像序列或视频中提取出运动目标的理论和方法,是计算机视觉中一个重要的研究方向,在军事和工业等领域有着广阔的应用前景.提出一种基于局部微分光流的运动对象分割算法.首先采用局部微分光流算法计算出场景的运动光流场并完成其初始分割,然后利用canny算子探测出对象的边缘信息并将其作为对光流场得到的运动信息的补充,从而分割出更为准确的运动对象.实验结果显示该方法具有良好的分割性能.     

图象光流场计算技术研究进展

时变图象光流场计算技术是计算机视觉中的重要研究内容，也是当今研究的热点问题。为了使人们对该技术有一个较全面的了解，因而对时变图象光流场计算技术的研究和进展做了较系统的论述，首先分别列举了灰度时变图象和彩色时变图象的光流场计算方法，并对这些方法进行了分类，然后总结了出目前图象光流场计算中存在的几个问题，最后对光流场计算技术的研究发展及其应用前景指出了一些可能的方向。     

一种新的全局优化BP网络*

将L-M算法与填充函数法相结合,提出一种训练前向网络的混合型全局优化GOBP(Global Optimization BP)算法。L-M算法的收敛速度快,利用它先得到一个局部极小点,然后利用填充函数算法跳出局部最小,得到一个更低的局部极小点,重复计算即可得到全局最优点。经实验验证,该算法收敛速度很快,避免了局部收敛,而且性能稳定。     

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.     

Massively parallel Lucas Kanade optical flow for real-time video processing applications

This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 × 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.     

Dense versus Sparse Approaches for Estimating the Fundamental Matrix

There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points, dense energy based methods mark the state of the art in optical flow computation. The goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way. As a first contribution we present a new application of dense optical flow for estimating the fundamental matrix. Comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches. Motivated by these promising results we propose, as a second contribution, a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional. In experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow. Our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches.     

Moving object detection and segmentation in urban environments from a moving platform

This paper proposes an effective approach to detect and segment moving objects from two time-consecutive stereo frames, which leverages the uncertainties in camera motion estimation and in disparity computation. First, the relative camera motion and its uncertainty are computed by tracking and matching sparse features in four images. Then, the motion likelihood at each pixel is estimated by taking into account the ego-motion uncertainty and disparity in computation procedure. Finally, the motion likelihood, color and depth cues are combined in the graph-cut framework for moving object segmentation. The efficiency of the proposed method is evaluated on the KITTI benchmarking datasets, and our experiments show that the proposed approach is robust against both global (camera motion) and local (optical flow) noise. Moreover, the approach is dense as it applies to all pixels in an image, and even partially occluded moving objects can be detected successfully. Without dedicated tracking strategy, our approach achieves high recall and comparable precision on the KITTI benchmarking sequences.     

Improving the robustness of variational optical flow through tensor voting

Differential optical flow methods allow the estimation of optical flow fields based on the first-order and even higher-order spatio-temporal derivatives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due to poor illumination conditions, the use of partial derivatives will amplify that noise and thus end up affecting the accuracy of the computed flow fields. The typical approach in order to reduce that noise consists of smoothing the required gradient images with Gaussian filters, for instance by applying structure tensors. However, that filtering is isotropic and tends to blur the discontinuities that may be present in the original images, thus likely leading to an undesired loss of accuracy in the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian filtering, and shows that the discontinuity preserving capabilities of the former yield more robust and accurate results. In particular, a state-of-the-art variational optical flow method has been adapted in order to utilize a tensor voting filtering approach. The proposed technique has been tested upon different datasets of both synthetic and real image sequences, and compared to both well known and state-of-the-art differential optical flow methods.     

On the Spatial Statistics of Optical Flow

We present an analysis of the spatial and temporal statistics of “natural” optical flow fields and a novel flow algorithm that exploits their spatial statistics. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from hand-held and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a Field-of-Experts model that captures the spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.     

A loop-consistency measure for dense correspondences in multi-view video

Many applications in computer vision and computer graphics require dense correspondences between images of multi-view video streams. Most state-of-the-art algorithms estimate correspondences by considering pairs of images. However, in multi-view videos, several images capture nearly the same scene. In this article we show that this redundancy can be exploited to estimate more robust and consistent correspondence fields. We use the multi-video data structure to establish a confidence measure based on the consistency of the correspondences in a loop of three images. This confidence measure can be applied after flow estimation is terminated to find the pixels for which the estimate is reliable. However, including the measure directly into the estimation process yields dense and highly accurate correspondence fields. Additionally, application of the loop consistency confidence measure allows us to include sparse feature matches directly into the dense optical flow estimation. With the confidence measure, spurious matches can be successfully suppressed during optical flow estimation while correct matches contribute to increase the accuracy of the flow.     

Temporal Multi-Scale Models for Flow and Acceleration

A model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. This model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. The integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. In addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. A demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera is given.     

稀疏光流快速计算的动态目标检测与跟踪

动态目标的检测与跟踪作为图像处理和计算机视觉学科的重要分支,广泛应用于军事和民用等各个领域。文中提出一种基于稀疏光流快速计算的目标检测和跟踪新方法,该方法通过计算能反映图像特征的特定像素点光流矢量来实现目标检测和跟踪,同时结合图像金字塔技术,可以检测和跟踪运动速度更快、运动尺度更大的目标。文中将该方法分别与稠密光流方法和基于颜色特征方法作对比,结果表明该方法有计算量小、能很好应对目标遮挡情况和能检测和跟踪运动速度较快的目标等诸多优点。实验在多种条件下对该方法进行了验证,跟踪准确率都能达到80%以上,且基本能符合实时性的要求,说明该方法具有可行性和实用价值。