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.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

This work intends to develop an online experimental system for screening of deoxynivalenol (DON) contamination in whole wheat meals by visible/near-infrared (Vis/NIR) spectroscopy and computer vision coupling technology. Spectral and image information of samples with various DON levels was collected at speed of 0.15 m s⁻¹ on a conveyor belt. The two-type data were then integrated and subjected to chemometric analysis. Discriminant analysis showed that samples could be classified by setting 1000 μg kg⁻¹ as the cut-off value. The best correct classified rate obtained in prediction was 93.55% based on fusion of spectral and image features, with reduced prediction uncertainty as compared to single feature. However, quantification of DON by quantitative analysis was not successful due to poor model performance. These results indicate that, although not accurate enough to provide conclusive result, this coupling technology could be adopted for rapid screening of DON contamination in cereals and feeds during processing.     

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real‐time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision‐based systems to avoid hazards as well as plan safe routes. However, vision‐based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision‐based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this study, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared with traditional fixed‐sensor configurations.     

基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.