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Distance Measurement system Based on Binocular Stereo Vision

IOP Conference Series. Earth and Environmental Science 2019年第5期252卷 052051-052051页

作者： Sun, Xiyan Ji, Yuanfa Jiang, Yingzhou Fu, Wentao Yan, Suqing Chen, Qidong Yu, Baoguo Gan, Xingli Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin 541004 China National & Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin 541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin 541004 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin 541004 China National & Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin 541004 China Key laboratory of radio wave environment and modeling technology Qingdao 266000 China State key laboratory of satellite navigation system and equipment technology Shijiazhuang 050000 China

Binocular stereo vision uses two cameras of the same specification instead of human eyes for measurement and judgment. The basic principle of binocular vision ranging is to simultaneously capture the image of the measured object by using two left and right cameras. The objectmeasuredisdifferent, andthe positions under the different camera screensaredifferent, this difference in position is called disparity. The three-dimensional information of the object is obtained according to the principle of disparity. The binocular stereo vision ranging system, which combines the advantages of high efficiency and high precision, is ideal for non-contact detection and quality control. This paper proposes a method combining MATLAB calibration and OpenCV matching to achieve binocular stereo vision. Using the MATLAB calibration tool to perform stereo calibration of the camera, combined with OpenCV for stereo rectification and stereo matching, the disparity value is obtained, and the distance from the target to the camera is calculated by disparity. Simulations show that this method can satisfy the distance measurement.

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Dynamic data packing towards the optimization of QoC and QoS in networked control systems

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Science China(Technological Sciences) 2016年第1期59卷 72-80页

作者： KANG Yu ZHAO YunBo Department of Automation and State Key Laboratory of Fire Science University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Department of Automation Zhejiang University of Technology

A class of networked control systems is investigated whose communication network is shared with other applications. The design objective for such a system setting is not only the optimization of the control performance but also the efficient utilization of the communication resources. We observe that at a large time scale the data packet delay in the communication network is roughly varying piecewise constant, which is typically true for data networks like the Internet. Based on this observation, a dynamic data packing scheme is proposed within the recently developed packet-based control framework for networked control systems. As expected this proposed approach achieves a fine balance between the control performance and the communication utilization: the similar control performance can be obtained at dramatically reduced cost of the communication resources. Simulations illustrate the effectiveness of the proposed approach.

关键词： networked control systems packet delay variation dynamic data packing quality of control quality of service

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Universal enhancement of salient object detection

Universal enhancement of salient object detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Runchun Ye Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781509060689

Recently, numerous salient object detection methods are proposed for different data types. And a reliable method, which can accurately extract complete salient objects, is beneficial to various vision tasks. However, existing methods may fail in highlighting the entire salient object uniformly. In this work, we propose a simple and universal framework aiming to improve the detection result of existing methods. To remove inaccurate salient regions, we apply location prior and adaptive de-noising to prior saliency maps extracted from existing methods in the pre-processing step. Then, an iteration optimization algorithm considering local smoothness and global similarity is introduced to refine the pre-processed saliency map. The experimental results show that the proposed framework can universally enhance the performance of state-of-the-art salient object detection methods for 2D, 3D and light field data.

关键词： Object detection Three-dimensional displays Optimization Two dimensional displays Color Image segmentation Computational modeling

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Improving triplet-wise training of convolutional neural network for vehicle re-identification

Improving triplet-wise training of convolutional neural netw...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiheng Zhang Dong Liu Zheng-Jun Zha CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781509060689

Vehicle re-identification (re-id) plays an important role in the automatic analysis of the drastically increasing urban surveillance videos. Similar to the other image retrieval problems, vehicle re-id suffers from the difficulties caused by various poses of vehicles, diversified illuminations, and complicated environments. Triplet-wise training of convolutional neural network (CNN) has been studied to address these challenges, where the CNN is adopted to automate the feature extraction from images, and the training adopts triplets of (query, positive example, negative example) to capture the relative similarity between them to learn representative features. The traditional triplet-wise training is weakly constrained and thus fails to achieve satisfactory results. We propose to improve the triplet-wise training at two aspects: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is designed. Our experimental results demonstrate the effectiveness of the proposed methods that achieve superior performance than the state-of-the-arts on two vehicle re-id datasets, which are derived from real-world urban surveillance videos.

关键词： Training Feature extraction Streaming media Videos Sampling methods Licenses Cameras

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Title: Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

arXiv

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arXiv 2018年

作者： Wang, Yuan Yuan, Qiangqiang Li, Tongwen Shen, Huanfeng Zheng, Li Zhang, Liangpei School of Geodesy and Geomatics Wuhan University Wuhan Hubei430079 China School of Resource and Environmental Sciences Wuhan University Wuhan Hubei430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei430079 China The Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan Hubei430079 China Key Laboratory of Geospace Environment and Geodesy Ministry of Education Wuhan University Wuhan Hubei430079 China Collaborative Innovation Center for Geospatial Technology WuhanHubei430079 China

In this study, we evaluated the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products and compared them with the Collection 6 (C6) products over regions in China with multifarious underlying surfaces during 2001–2016. The AOD retrievals were validated against 20 AERONET (V3) sites, and the results show that the correlation coefficient (R) for dark target (DT) retrievals in C6.1 is 0.946, while the fraction within the expected error (EE) can be considered relatively low at only 54.03%. Deep blue (DB) retrievals in C6.1 have a slightly lower R value (0.931), but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the low elevation vegetation (LEV) areas. For the high elevation vegetation (HEV) areas, DB performs synthetically better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the different land cover types. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DB tends to be high in areas where the main land cover type is bare soil and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals i

关键词： Supercomputers

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Investigating the spectral characteristic and humification degree of dissolved organic matter in saline-alkali soil using spectroscopic techniques

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Frontiers of Earth Science 2017年第1期11卷 76-84页

作者： Qiang LI Xujing GUO Lu CHEN Yunzhen LI Donghai YUAN Benlin DAI Sisi WANG Department of land Management School of Urban Economics and Public Administration Capital University of Economics and Business Beijing 100070 China College of Resources and Environment Chengdu University of Information Technology Chengdu 610225 China Key Laboratory of Development and Application of Rural Renewable Energy Ministry of Agriculture Biogas institute of Ministry of Agriculture Chengdu 610041 China Sichuan. Academy of Environmental Science Chengdu 610041 China Key Laboratory of Urban Stormwater System and Water Environment Ministry of Education Beijing University of Civil Engineering and Architecture Beijing 100044 China Jiangsu Key Laboratory for Biomass-based Energy and Enzyme Technology Huaiyin Normal University Huaian 223300 China

Soil samples were collected from the areas surrounding Wuliangsuhai Lake in China. Dissolved organic matter （DOM） was extracted from the samples and characterized by fluorescence and UV-Vis spectra. Spectral properties and humification degree of DOM were studied. The results indicated that both humic- and protein- like fluorophores were present in the DOM spectra, and the former was the dominant component. The analysis of humification （HIX） and r （A, C） indices revealed that the maximum humification degree in three agricultural soils （AAF, ASC, and ASW） was presented in the second soil layer （20-40 cm）. However, the humification degree of the two Halophytes soils （SSE and GKF） decreased with increasing depth. One index, I344/270, showed that humification degree increased gradually with an increasing proportion of humic-like acid. There was a significant positive correlation between humification degree （HIX） and aromaticity （SUVA254）, indicating that a higher aromaticity corresponded to a higher humification degree. Land use was an important factor responsible for the major difference of cation exchange capacity （CEC） in different soils, which led to a higher CEC value in the second soil layer for the three agricultural soils. CEC values and humification degree had the same trend for all five soils. The correlation analysis showed that there was a significant positive correlation between HIX and CEC,and a negative correlation between the r （A, C） index and CEC, indicating that humification degree increases gradually with increasing CEC values.

关键词： dissolved organic matter fluorescence spectroscopy humification degree cation exchange capacity

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Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

arXiv

引用

arXiv 2018年

作者： Li, Zhiwei Shen, Huanfeng Cheng, Qing Liu, Yuhao You, Shucheng He, Zongyi School of Resource and Environmental Sciences Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan China School of Urban Design Wuhan University Wuhan China Department of Remote Sensing China Land Surveying and Planning Institute Beijing China

Cloud detection is an important preprocessing step for the precise application of optical satellite imagery. In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors. In the network architecture of MSCFF, the symmetric encoder-decoder module, which provides both local and global context by densifying feature maps with trainable convolutional filter banks, is utilized to extract multi-scale and high-level spatial features. The feature maps of multiple scales are then up-sampled and concatenated, and a novel multi-scale feature fusion module is designed to fuse the features of different scales for the output. The two output feature maps of the network are cloud and cloud shadow maps, which are in turn fed to binary classifiers outside the model to obtain the final cloud and cloud shadow mask. The MSCFF method was validated on hundreds of globally distributed optical satellite images, with spatial resolutions ranging from 0.5 to 50 m, including Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and collected high-resolution images exported from Google Earth. The experimental results show that MSCFF achieves a higher accuracy than the traditional rule-based cloud detection methods and the state-of-the-art deep learning models, especially in bright surface covered areas. The effectiveness of MSCFF means that it has great promise for the practical application of cloud detection for multiple types of medium and high-resolution remote sensing images. Our established global high-resolution cloud detection validation dataset has been made available online (http://***/en/mscff/). Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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A convolutional neural network approach for half-pel interpolation in video coding

arXiv

引用

arXiv 2017年

作者： Yan, Ning Liu, Dong Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Motion compensation is a fundamental technology in video coding to remove the temporal redundancy between video frames. To further improve the coding efficiency, sub-pel motion compensation has been utilized, which requires interpolation of fractional samples. The video coding standards usually adopt fixed interpolation filters that are derived from the signal processing theory. However, as video signal is not stationary, the fixed interpolation filters may turn out less efficient. Inspired by the great success of convolutional neural network (CNN) in computer vision, we propose to design a CNN-based interpolation filter (CNNIF) for video coding. Different from previous studies, one difficulty for training CNNIF is the lack of ground-truth since the fractional samples are actually not available. Our solution for this problem is to derive the "ground-truth" of fractional samples by smoothing high-resolution images, which is verified to be effective by the conducted experiments. Compared to the fixed half-pel interpolation filter for luma in High Efficiency Video Coding (HEVC), our proposed CNNIF achieves up to 3.2% and on average 0.9% BD-rate reduction under low-delay P configuration. Copyright © 2017, The Authors. All rights reserved.

关键词： Video signal processing

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Neural network-based arithmetic coding of intra prediction modes in HEVC

arXiv

引用

arXiv 2017年

作者： Song, Rui Liu, Dong Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Hierarchical co-segmentation of 3D point clouds for indoor scene

Hierarchical co-segmentation of 3D point clouds for indoor s...

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International Conference on systems, Signals and Image Processing, IWSSIP

作者： Yanting Lin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei City China

ISBN: (纸本)9781509063451

Segmentation of point clouds has been studied under a variety of scenarios. However, the segmentation of scanned point clouds for a clustered indoor scene remains significantly challenging due to noisy and incomplete data, as well as scene complexity. Based on the observation that objects in an indoor scene vary largely in scale but are typically supported by planes, we propose a co-segmentation approach. This technique utilizes the mutual agency between the point clouds captured at different times after the objects' poses change due to human actions. Hence, we hierarchically segment scenes from different times into patches and generate tree structures to store their relations. By iteratively clustering patches and co-analyzing them based on the relations between patches, we modify the tree structures and generate our results. To test the robustness of our method, we evaluate it on imperfectly scanned point clouds from a childroom, a bedroom, and two offices scenes.

关键词： Three-dimensional displays Image color analysis Geometry Mathematical model Principal component analysis Silicon

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