High quality 3D reconstruction based on fusion of polarization imaging and binocular stereo vision

Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the polarization surface by correcting the azimuth angle errors on the basis of registered binocular depth, to solve the azimuthal ambiguity in the polarization imaging. Then we propose a joint 3D reconstruction model for depth fusion, including a data fitting term and a robust low-rank matrix factorization constraint. The former is to transfer textures from the polarization surface to the fused depth by assuming their relationship linear, whereas the latter is to utilize the low-frequency part of binocular depth to improve the accuracy of the fused depth considering the influences of missing-entries and outliers. To solve the optimization problem in the proposed model, we adopt an efficient solution based on the alternating direction method of multipliers. Extensive experiments have been conducted to demonstrate the efficiency of the proposed method in comparison with state-of-the-art methods and to exhibit its wide application prospects in 3D reconstruction.     

On the Yustova threshold plates

A response to criticism of threshold plates for the study of color vision developed at the Mendeleev All-Russia Research Institute of Metrology and published in 1994 is presented. The critics base their conclusions on colorimetric testing and the examination of the plates. In response to the article by M. V. Danilova and J. D. Mollon [4]. __________ Translated from Izmeritel’naya Tekhnika, No. 1, pp. 37–39, January, 2007.     

Development of a Sensor Fusion Strategy for Robotic Application Based on Geometric Optimization

Fusion of multi-sensor information is an important technology, which is growing exponentially due to its tremendous application potential in many areas. Effective fusion of data from sensors is very critical in increasing an intelligent system's capability to accomplish complex tasks. Appropriate fusion technologies are needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty has been modeled using classical Lagrangian optimization techniques. However, the uniqueness and effectiveness of the present technique lies on the fact that starting from the optimized value as initial estimate the accuracy of the sensory information has further been improved up to any pre defined bounded range, by developing two architectures – FFA (fission–fusion architecture) and FDD (fusion in differential domain). Sufficient evidences and analyses have been provided in the paper to show its effectiveness in various applications.     

新显色剂AAATAR在光度分析测定微量钯中的应用

介绍了４种对钯有高灵敏和选择性的安替比林偶氮胂酸类试剂。试剂在较高酸度介质中能与钯形成稳定的２：１红色络合物。表观摩尔吸光系数为３．５～５．１×１０４。用ＡＡＡＴＡＲ测定镍铜合金中的钯，结果满意。     

一种基于MPEG-2的立体视频编码中的视差匹配快速算法

高效，快速的视差匹配是立体视频处理中的一项关键技术。本文在分析立体图像序列的视差矢量与运动矢量之间的相关性的基础上，提出一种基于MPEG-2的立体视频编码中的视差匹配快速算法。实验结果表明，与全搜索法相比，在保证重建图像质量的前提下，快速算法能显著降低视差估计的计算复杂度。     

新型彩电面面观

新型彩电最能集中反映显示技术的突破性进展。本文介绍全球彩电市场概况、交互式彩电、数字式彩电、数字式有线彩电及近年来不断涌现的彩电新品，如眼控式彩电、声控式彩电、声控式彩电、多媒体彩电、三维立体彩电及无荧屏彩电等。     

应用光度分析法测定铁和渣中的微量镓

用光度分析法测定镓，为适应多种试样的测定，补做了个别条件试验。用该方法测定了铁、渣、浸钒渣等六种不同类型试样中镓的含量，分析其精度精度符合要求。     

基于梯度值的自适应窗口立体图像匹配算法

针对基于区域的立体图像匹配算法支持窗口难以选择,容易出现窗口过大或过小的问题,提出一种新的自适应窗口立体图像匹配算法。该算法利用Sobel梯度算子计算像素梯度值,并根据其梯度值动态地获取具有自适应的支持窗口,然后分别选择相似性测度函数SAD或NCC搜索最佳匹配点,获得视差图。此外,算法在窗口选择过程中进行优化,减少了计算量。实验结果表明,改进后的算法提高了匹配正确率且计算时间缩短了近5%。     

基于损失自注意力机制的立体匹配算法研究

针对现有的立体匹配算法在阴影、物体边缘和光照反射等区域匹配困难且存在大量错误结果的问题,设计了一种可拆卸的损失自注意力网络（loss self-attention net,LSAnet）查找图像中的匹配困难区域。LSAnet的网络各层相互稠密连接,应用了空洞卷积来扩大感受野,并以立体匹配算法生成的损失分布为标签,能够动态地进行有监督训练,最终生成匹配困难区域掩膜辅助立体匹配网络进行更好的优化;同时,改进了立体匹配网络中经典的特征匹配代价卷结构,降低了后续3D卷积的计算负荷,提高了匹配效率。实验结果表明,该算法相比于基准算法精度更高,并且可以提高算法对于匹配困难区域的鲁棒性。     

基于多尺度注意力网络的立体匹配方法

为解决网络深度与训练图像块大小耦合问题及进一步提高弱纹理区域及边缘处的匹配精度,提出了一种基于多尺度注意力网络的立体匹配方法.该方法将立体匹配过程分为2个阶段：第1阶段提出了一种成本网络用于计算匹配成本,该网络由基础网络层和缩放层组成.第2阶段提出了一种基于多尺度注意力的视差求精网络,该视差求精网络综合了多种视差线索,并加入多尺度注意力机制进一步提高立体匹配精度.该方法在KITTI 2012、KITTI 2015和SceneFlow数据集上的3像素坏点百分比分别为1.13%,1.87%和2.29%.实验结果表明,与国内外同类方法相比,采用多尺度注意力网络的立体匹配方法在匹配精度上获得了较大的提升,尤其是在弱纹理区域及物体边缘处表现较好.