一种新的体视对应方法

选择特征点作为匹配特征,采用视差估计方法,在满足一定匹配准则情况下,可以得到一约束最小化能量函数。模拟实验结果表明,使用模拟退火算法,可使能量函数达到全局最小,从而实现立体匹配。     

光栅式双目立体视觉传感器的立体匹配方法

光栅式双目立体视觉传感器的难点之一在于立体匹配问题,为此,提出了一种基于极线约束和空间点最小距离搜索的立体匹配方法.该方法将光栅式双目立体视觉传感器看作两个光栅结构光传感器,分别标定后可测定光条中心点关于某个结构光模型的三维坐标,若两点匹配,则其三维坐标间的距离理论上为零.引入极线约束,在左摄像机成像光条上找一个特征点,在右摄像机所成像中便可计算出一条极线与之对应,在极线与各光条中心的交点中寻找匹配点.该方法在三维空间进行匹配,计算量小,能够实现点与点的唯一匹配.仿真实验表明了该方法的有效性.     

Novel shrinking residual convolutional neural network for efficient accurate stereo matching

For stereo matching based on patch comparing using convolutional neural networks (CNNs), the matching cost estimation is highly dependent on the network structure, and the patch comparing is time consuming for traditional CNNs. Accordingly, we propose a stereo matching method based on a novel shrinking residual CNN, which consists of convolutional layers and skip-connection layers, and the size of the fully connected layers decreases progressively. Firstly, a layer-by-layer shrinking size model is adopted for the full-connection layers to greatly increase the running speed. Secondly, the convolutional layer and the residual structure are fused to improve patch comparing. Finally, the Loss function is re-designed to give higher weights to hard-classified examples compared with the standard cross entropy loss. Experimental results on KITTI2012 and KITTI2015 demonstrate that the proposed method can improve the operation speed while maintaining high accuracy.     

A new parallel hardware architecture for high-performance stereo matching calculation

In this paper, we propose a hardware (H/W) architecture to find disparities for stereo matching in real time. After analyzing the arithmetic characteristic of stereo matching, we propose a new calculating method that reuses the intermediate results to minimize the calculation load and memory access. From this, we propose a stereo matching calculation cell and a new H/W architecture. Finally, we propose a new stereo matching processor. The implemented H/W can operate at the clock frequency of 250 MHz at least in the FPGA (field programmable gate array) environment and produce about 120 disparity images per second for HD stereo images.     

Convolutional neural network based deep conditional random fields for stereo matching

Stereo matching has been studied for many years and is still a challenge problem. The Markov Random Fields (MRF) model and the Conditional Random Fields (CRF) model based methods have achieved good performance recently. Based on these pioneer works, a deep conditional random fields based stereo matching algorithm is proposed in this paper, which draws a connection between the Convolutional Neural Network (CNN) and CRF. The object knowledge is used as a soft constraint, which can effectively improve the depth estimation accuracy. Moreover, we proposed a CNN potential function that learns the potentials of CRF in a CNN framework. The inference of the CRF model is formulated as a Recurrent Neural Network (RNN). A variety of experiments have been conducted on KITTI and Middlebury benchmark. The results show that the proposed algorithm can produce state-of-the-art results and outperform other MRF-based or CRF-based methods.     

基于差异的视差估计方法研究

视差估计在立体图像编码、视频检索、立体图像分割等领域有重要的应用,是进行图像、视频分析的基础。首先,基于模糊隶属度提出了一种基于差异的视差估计方法;其次,引入模糊隶属度,把图像灰度转换为差异值;然后,分析了多块匹配的优越性;提出了基于多块匹配的视差估计方法;最后,在Middlebury网站上的测试结果证明了该方法的有效性。     

采用小波变换的立体匹配:一种基于相位的方法

本义提出了一种采用小波变换基于相位的立体匹配算法。该算法引入多分辨率分析思想,借助小波变换的Ｍａｌｌａｔ算法,采用金字塔式的多尺度匹配结构,从而显著地提高了匹配效率。同时,该算法利用平滑性约束条件实现了相位不稳定区域视差的自适应插值,保证了良好的匹配效果。立体象对的测试结果表明了该算法的有效性。     

Discontinuity preserving disparity estimation with occlusion handling

In this paper, we propose a stereo matching algorithm based on distance transform to generate high-quality disparity maps with occlusion handling. In general, pixel intensities around object edges are smeared due to mixed values located between the object and its background. This leads to problems when identifying discontinuous disparities. In order to handle these problems, we present an edge control function according to distance transform values. Meanwhile, occluded regions occur, i.e., some portions are visible only in one image. An energy function is designed to detect such regions considering warping, cross check, and luminance difference constraints. Consequently, we replace the disparity in the occluded region with the one chosen from its neighboring disparities in the non-occluded region based on color and spatial correlations. In particular, the occlusion hole is filled according to region types. Experimental results show that the proposed method outperforms conventional stereo matching algorithms with occlusion handling.     

A new head pose tracking method based on stereo visual SLAM

Real-time and reliable head pose tracking is the basis of human–computer interaction and face analysis applications. Aiming at the problems of accuracy and real time performance in current tracking method, a new head pose tracking method based on stereo visual SLAM is proposed in this paper. The sparse head map is constructed based on ORB feature points extraction and stereo matching, then the 3D-2D matching relations between 3D mappoints and 2D feature points are obtained by projection matching. Finally, the camera pose solved by the Bundle Adjustment is converted to head pose, which realizes the tracking of head pose. The experimental results show that this method can obtain high precise head pose. The mean errors of three Euler angles are all less than 1°. Therefore, the proposed head pose tracking method can track and estimate precise head pose in real time under smooth background.     

Edge-preserving guided filtering based cost aggregation for stereo matching

Stereo matching has been widely used in various computer applications and it is still a challenging problem. In stereo matching, the filter-based stereo matching methods have achieved outstanding performance. A local stereo matching method based on adaptive edge-preserving guided filter is presented in this paper, which can achieve proper cost-volume filtering and keep edges well. We introduce a gradient vector of the enhanced image generated by the proposed filter into the cost computation and the Census transform is adopted in the cost measurement. This cost computation method is robust against radiometric variations and textureless areas. The edge-preserving guided filter approach is proposed to aggregate the cost volume, which further proves the effectiveness of edge-preserving filter for stereo matching. The experiments conducted on Middlebury benchmark and KITTI benchmark demonstrate that the proposed algorithm produces better results compared with other edge-aware filter-based methods.     

User assisted disparity remapping for stereo images

This study concentrates on user assisted disparity remapping for stereo image footage, i.e. the disparity of an object of interest is altered while leaving the remaining scene unattended. This application is useful in the sense that it provides a method for emphasizing/de-emphasizing an object on the scene by adjusting its depth with respect to the camera. The proposed technique can also be used as a post-processing step for retargeting stereoscopic footage on different display sizes and resolutions. The proposed technique involves an MRF-based energy minimization step for interactive stereo image segmentation, for which user assistance on only one of the stereo pairs is required for determining the location of stereo object pair. A key contribution of the proposed study is elimination of dense disparity estimation step from the pipeline. This step is realized through a sparse feature matching technique between the stereo pairs. Moreover, by the help of the proposed technique, novel disparity adjusted views are synthesized using the produced stereo object segments and background information for the images. Qualitative and quantitative evaluation of the generated segments and the disparity adjusted images prove the functionality and superiority of the proposed technique.     

Stereo matching based on multi-direction polynomial model

This paper presents a segmentation based stereo matching algorithm. For the purposes of both preserving the shape of object surfaces and being robust to under segmentations, we introduce a new scene formulation where the reference image is divided into overlapping lines. The disparity value and the index of pixels on lines are modeled by polynomial functions. Polynomial functions are propagated among lines to obtain smooth surfaces via solving energy minimizing problems. Finally, the disparity of pixels is estimated from the disparity fields provided by lines. Because lines in multiple directions implicitly segment different objects in an under segmentation region, our method is robust for under segmented regions where it is usually difficult for conventional region based methods to produce satisfactory results. Experimental results demonstrate that the proposed method has an outstanding performance compared with the current state-of-the-art methods. The scene representation method in this work is also a powerful approach to surface based scene representations.     

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Guided image filtering (GIF) based cost aggregation or disparity refinement stereo matching algorithms are studied extensively owing to the edge-aware preserved smoothing property. However, GIF suffers from halo artifacts in sharp edges and shows high computational costs on high-resolution images. The performance of GIF in stereo matching would be limited by the above two defects. To solve these problems, a novel fast gradient domain guided image filtering (F-GDGIF) is proposed. To be specific, halo artifacts are effectively alleviated by incorporating an efficient multi-scale edge-aware weighting into GIF. With this multi-scale weighting, edges can be preserved much better. In addition, high computational costs are cut down by sub-sampling strategy, which decreases the computational complexity from O(N) to O(N/s²) (s: sub-sampling ratio) To verify the effectiveness of the algorithm, F-GDGIF is applied to cost aggregation and disparity refinement in stereo matching algorithms respectively. Experiments on the Middlebury evaluation benchmark demonstrate that F-GDGIF based stereo matching method can generate more accuracy disparity maps with low computational cost compared to other GIF based methods.     

最小失配的宽带天线匹配网络设计

本文从控制网络输入端口反射系数出发,提出了一种最小失配的宽带天线匹配网络的设计方法。利用精英保留非劣排序遗传算法分别设计了最小失配的天线无耗匹配网络和有耗匹配网络,实现网络输入端口反射系数和传输功率增益的多目标优化,并对匹配网络的传输函数对负载变化的灵敏度进行了分析。仿真实验表明,所设计的最小失配的匹配网络具有随负载变化灵敏度小的优点,证明了该方法的有效性。     

基于视差补偿预测的立体视频图像压缩编码

本文介绍了立体视频编码方法,并对其关键技术-视差补偿预测技术进行深入研究.本文所提出的基于视差分割的视差补偿预测算法是建立在可变尺寸块匹配算法的基础上,充分利用视差信息实现对目标图像帧的有效分割,并采用相适应的视差向量编码方案.与传统算法相比,在相同预测精度下,明显降低了视差信息编码开销.     

用于自由曲面视觉测量的立体精匹配方法

根据立体视觉原理,针对自由曲面视觉测量的实际情况,文中将多种方法融合为一体用于立体匹配,提出一种亚像素级的立体精匹配方法。首先利用投影光栅,在曲面上形成变形条纹,采用小波变换检测出像素级的离散边缘点;在此基础上,提出搜索式无监督聚类拟合匹配算法,将边缘点按实际边缘情况分为不同组别,并用三次B样条将离散的边缘点拟合成连续曲线;根据对极约束特性,实现亚像素级的立体精匹配,解决了交向摆放姿态的双目立体视觉系统的匹配问题。     

用于信号、图像和景象匹配的特征区选择方法

匹配中最常见的问题之一是根据给定的准则选择最佳匹配子区，而选择的关键是具有唯一性和缺乏模糊性。唯一性可以通过原始图和子图相关函数峰值的幅值和个数决定。模糊性可以用相关长度或主相关峰宽度的50％来衡量。相关长度越短，检测正确匹配位置的可能性就越大。提出了一种根据最小相关长度来选择最佳匹配子区的方法。此分析可扩展到二维，三维或更高维，而且可通过快速傅里叶变换来解决离散问题。     

Dense-CNN: Dense convolutional neural network for stereo matching using multiscale feature connection

In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural network-based stereo matching method with multiscale feature connection, named Dense-CNN. First, we construct a novel densely connected network with multiscale convolutional layers to extract rich image features, in which the merged multiscale features with context information are utilized to estimate the cost volume for stereo matching. Second, we plan a novel loss-function strategy to learn the network parameters more reasonably, which can develop the performance of the proposed Dense-CNN model on disparity computation. Finally, we run our Dense-CNN model on the Middlebury and KITTI databases to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieved superior performance on computational accuracy and robustness of disparity estimation, especially achieving the significant benefit of feature preservation in ill-posed regions.     

一种新的双眼视觉的立体匹配方法及其应用

立体视觉中最大的困难是对应点的寻找,即需要匹配二幅或更多幅立体图像对之间的特征。本文提出一种新的双眼视觉的立体匹配方法,对积木(BLOCK)一类物体的立体图象匹配十分有效。将这种方法应用于三维物体的识别与定位,实验结果表明,与已有方法相比,本匹配方法简单、有效、匹配快速。文章最后给出用这种方法进行物体识别与精确定位的实验结果。