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arXiv 2018年

作者： Chen, Zhibo He, Tianyu Jin, Xin Wu, Feng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc. Experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding. Copyright © 2018, The Authors. All rights reserved.

关键词： Image compression

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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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Efficient structure-preserving superpixel segmentation based on minimum spanning tree

Efficient structure-preserving superpixel segmentation based...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yu Bai Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781467372596

We propose a novel superpixel algorithm based on Minimum Spanning Tree (MST), to generate superpixels efficiently while strictly adhere to object boundaries. The MST, which built by gradually removing strong edges of the image graph extracted from the image, is more sensitive to image local structures. Therefore, an efficient hierarchical clustering strategy is basically employed in our algorithm to segment the input image into superpixels based on the tree distance. To gradually merge the image pixels and remove texture noises, a multi-layer scheme with different resolutions of superpixels is proposed. In each layer, the graph is constructed from the lower layer and segmented into superpixels in a linear complexity with the node number in the graph. Because the node number in each layer is exponentially reduced, the computational time of our method mainly concentrates on the first few layers, which is linear with the number of image pixels. The experimental results conducted on the Berkeley Segmentation Dataset demonstrate that our method outperforms state-of-the-art methods both in terms of structure preservation and computational efficiency.

关键词： Image segmentation Image color analysis Image edge detection Clustering algorithms Partitioning algorithms Joining processes Colored noise

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VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision

arXiv

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arXiv 2023年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and – as usual – before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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No-reference light field image quality assessment based on spatial-angular measurement

arXiv

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arXiv 2019年

作者： Shi, Likun Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Deterioration

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Experiment research on scattering characters of passive calibrator for high resolution SAR calibration

Experiment research on scattering characters of passive cali...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Ming, Feng Hong, Jun Zhang, Rui Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences Beijing China Graduate University of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424427314

In this paper, the scattering characters of passive calibrator are experimentally researched for SAR calibration applications. The works are focused on the SAR image of corner reflectors larger than the resolution cells. What is more, the RCS of the nonorthogonal and unevenness corner reflector are also studied. Finally, for full PolSAR calibration, we also discover some interesting phenomena for the first time, which are very important for the calibration activity. ©2009 IEEE.

关键词： Calibration

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THE REMOTE SENSING IMAGE geoMETRICAL MODEL of BP NEURAL NETWORK

THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETW...

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2020 International Conference on geomatics in the Big Data Era, ICGBD 2020

作者： Yue, C.Y. Sun, T. Xie, J.F. Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing China

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution. © 2020 C. Y. Yue et al.

关键词： Backpropagation

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Integrating Regularization and PnP Priors for SAR Image Reconstruction Using Multi-agent Consensus Equilibrium

Integrating Regularization and PnP Priors for SAR Image Reco...

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2024 International Workshop on the Theory of Computational Sensing and its applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024

作者： Fan, Yizhe Liu, Mingqian Shao, Mingxiao Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering China Aeronautical Remote Sensing Center Chinese Academy of Sciences China

ISBN: (纸本)9798350365504

Multi-agent consensus equilibrium mechanism is a generalization of popular used PnP-ADMM method and composite regularization in computational sensing. We propose a novel SAR image sparse reconstruction method based on MACE mechanism. High-quality SAR images necessitate low ambiguity, high SNR, and low coherent speckle. We therefore integrate regularization priors and PnP priors for azimuth ambiguity suppression, sparsity inducing and despeckling. The proposed method's performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data. © 2024 IEEE.

关键词： Image reconstruction

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Vibration Error Compensation Imaging Algorithm for Arc Array SAR Besed on Parameter Estimation 6

Vibration Error Compensation Imaging Algorithm for Arc Array...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Dong, Yifan Wu, Congfei Xu, Wei Huang, Pingping Tan, Weixian Hong, Wen College of Information Engineering Inner Mongolia University of Technology China Inner Mongolia Key Laboratory of Radar Technology and Application Hohhot China Institute of Electronics Chinese Academy of Sciences China Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9781728129129

Arc array synthetic aperture radar (SAR) is a novel array imaging system for wide-area observation, with wide observation range and high resolution. Arc array synthetic aperture radar uses W-band as carrier signal, and W-band wavelength is short. The small high-frequency vibration of the helicopter-borne platform will cause significant changes in the phase of the echo signal, which will seriously deteriorate the imaging performance of arc array synthetic aperture radar. Based on the high order approximate imaging algorithm of arc array radar, this paper proposes a vibration phase error compensation imaging algorithm based on Short-time Fourier transform (STFT) parameter estimation. The algorithm uses the high-order approximation imaging algorithm of curved array radar to perform high-order approximation of the slant range model, compensates the range cell migration (RCM) and range azimuth coupling in the two-dimensional frequency domain, and finally realizes the vibration error compensation in the range Doppler domain. The simulation results of the lattice target verify the effectiveness of the imaging method. © 2019 IEEE.

关键词： Synthetic aperture radar

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SAR Tomographic Inversion of Urban Areas via Morphology Regularization 15

SAR Tomographic Inversion of Urban Areas via Morphology Regu...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Li, Jie Li, Zhiyuan Fan, Yizhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

The current synthetic aperture radar (SAR) images with ultra high resolution provide the detailed structures of the urban areas, which are often utilized to retrieve 3D spatial information of the detailed structures based on tomographic synthetic aperture radar (TomoSAR). Compressive sensing (CS), as a favorable sparse reconstruction technique has been widely used in TomoSAR, however, the sparsity constraints by CS algorithms often lose detailed structures of the targets due to the pixel-wise processing. In this paper, we apply the morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization can shrink the concatenations caused by outliers in the iterative reconstruction, enhancing the detailed structural properties of reconstructed targets. And the alternating direction method of multipliers (ADMM) is applied, where the Bregman iteration is adopted for solving the sub-problem with the morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and preserve the detailed structures of tomographic reconstruction, leading to improved correctness and completeness. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Morphology

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