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MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

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MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRA...

IEEE International Conference on Image processing

作者： Anzhou Hu Rong Zhang Dong Yin Wenlong Hu Department of Electronic Engineering and Information Science University of Science and Technology of China Key Laboratory of Geospatial Information Processing and Application System Technology Chinese Academy of Sciences

ISBN: (纸本)9781479923427

Multi-channel peculiarity is one of the most widely accepted human visual system (HVS) models for perceptual image quality assessment (IQA). Otherwise than extensive studies of channel decomposition and intra-channel distortion measure, relatively scant research effort has been devoted to develop efficient multichannel evaluation pooling strategies. In this paper, we review and address the limitations of the conventional pooling models based on HVS sensitivities-weighted average. Instead, we explore the utilization of machine learning for this pooling problem, since machine learning can establish an optimal and generalized mapping that models the highly complex relationship between the multi-channel distortion evaluations and the perceived image quality. Experiments based on available subjective IQA databases demonstrate the rationality, reliability and robustness of our proposed scheme.

关键词： Image quality assessment (IQA) Multi-channel model Evaluation pooling Machine learning

Real-time correlation tracking via joint model compression and transfer

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arXiv 2019年

作者： Wang, Ning Zhou, Wengang Song, Yibing Ma, Chao Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Tencent AI Lab Shenzhen China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art performance while consuming a large number of computing resources. This limits deep CF trackers to be deployed to many mobile platforms on which only a single-core CPU is available. In this paper, we propose to jointly compress and transfer off-the-shelf CNN models within a knowledge distillation framework. We formulate a CNN model pretrained from the image classification task as a teacher network, and distill this teacher network into a lightweight student network as the feature extractor to speed up CF trackers. In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network. Meanwhile, we design a tracking loss to adapt the objective of the student network from object recognition to visual tracking. The distillation process is performed offline on multiple layers and adaptively updates the student network using a background-aware online learning scheme. Extensive experiments on five challenging datasets demonstrate that the lightweight student network accelerates the speed of state-of-the-art deep CF trackers to real-time on a single-core CPU while maintaining almost the same tracking accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

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Electromagnetic Simulation Aided SAR Target Classification V...

IEEE International Conference on Radar

作者： Xiaoling Lv Xiaolan Qiu Wenming Yu Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (纸本)9781665468893

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy.

关键词： Training Adaptation models Optical fiber networks Feature extraction Optical imaging Radar polarimetry Data models

UNSUPERVISED STEREO MATCHING NETWORK FOR VHR REMOTE SENSING IMAGES BASED ON ERROR PREDICTION

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arXiv 2024年

作者： Jiang, Liting Xiang, Yuming Wang, Feng You, Hongjian Aerospace Information Research Institute Chinese Academy of Sciences 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

Stereo matching in remote sensing has recently garnered increased attention, primarily focusing on supervised learning. However, datasets with ground truth generated by expensive airbone Lidar exhibit limited quantity and diversity, constraining the effectiveness of supervised networks. In contrast, unsupervised learning methods can leverage the increasing availability of very-high-resolution (VHR) remote sensing images, offering considerable potential in the realm of stereo matching. Motivated by this intuition, we propose a novel unsupervised stereo matching network for VHR remote sensing images. A light-weight module to bridge confidence with predicted error is introduced to refine the core model. Robust unsupervised losses are formulated to enhance network convergence. The experimental results on US3D and WHU-Stereo datasets demonstrate that the proposed network achieves superior accuracy compared to other unsupervised networks and exhibits better generalization capabilities than supervised models. Our code will be available at https://***/Elenairene/CBEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Analysis of the Reconstruction Accuracy of Compact Polarized SAR Pseudo Quad-Pol Data for Use in Ocean Surface Observations

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SSRN

SSRN 2022年

作者： Wang, Xiaochen Hu, Yuxin Han, Bing Duan, Jitong Aerospace Information Research Institute Chinese Academy of Science Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Science Beijing100190 China Deqing Academy of Satellite Application Huzhou313200 China

This paper assesses the capability of Compact Polarized (CP) Synthetic Aperture Radar (SAR) to explore ocean surface backscattering by reconstructing the pseudo quad-pol data. Besides using Souyris’s or Nord’s algorithms, 8 scenes of Radarsat-2 quad-polarized images are used to develop an empirical model for estimating the initial value of N in Collins’ algorithm. The robustness the three published algorithms are discussed together with the reconstruction error of polarization scattering matrix [S]. The comparisons show that Collins’ algorithm is superior to the other two algorithms in terms of reconstruction errors for the hh, vv, especially, the hv channels. In addition, the influence of each algorithm on polarimetric parameters is investigated. Based on the retrieval of the polarimetric parameters, it is concluded that Souyris’s, Nord’s and Collins’ algorithms all have similar (high) accuracies for co-polarization backscattering coefficient σ0HH, σ0VV, co-polarization correlation coefficient ρ and first eigenvalue l1, while for cross-polarization backscattering coefficient σ0HV and scattering angle α, the accuracy of Collins’ algorithm is better than that of Souyris’s or Nord’s’. Although all three algorithms produced low accuracies for second eigenvalue l2, third eigenvalue l3, scattering entropy H and scattering anisotropy A, the results obtained using Collins’ algorithm were better than those produced by the other two algorithms. © 2022, The Authors. All rights reserved.

关键词： Synthetic aperture radar

Study on Pixel Level Segmentation and Area Quantification of Highway Slope Cracks 3rd

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Study on Pixel Level Segmentation and Area Quantification of...

3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021

作者： Zhang, YunLing Guo, Pei Liu, Pengyu Li, Yaoyao Chen, Shanji Research and Development Center of Transport Industry of Spatial Information Application and Disaster Prevention and Mitigation Technology Beijing100097 China China Highway Engineering Consultants Corporation Beijing100097 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China School of Physics and Electronic Information Qinghai Nationalities University Xining810007 China

ISBN: (纸本)9783030901950

Highway slope disasters show obvious stage characteristics before the occurrence, and cracks are the early symptoms of most highway slope disasters. Computer vision is widely used in crack detection because of its advantages of high efficiency and low cost. In view of the shortage of traditional crack actual area calculation methods and poor effect, this paper proposes a slope crack pixel level segmentation method based on deep convolutional neural network, so as to generate accurate segmentation of crack morphology. Then, according to the binary segmentation mask, the checkerboard mapping method is proposed to calculate the actual crack area. Finally, the effectiveness and superiority of the proposed checkerboard mapping method are verified and evaluated with a self-made data set of highway crack image. The experimental results show that this method can effectively detect the actual crack area, and the relative error is small. The calculated results can be used as a reference for slope disaster warning. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep neural networks

Predictive auto-encoding network for blind stereoscopic image quality assessment

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arXiv 2019年

作者： Xu, Jiahua Zhou, Wei Chen, Zhibo Ling, Suiyi Le Callet, Patrick CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China quipe Image Perception et Interaction Laboratoire des Sciences du Numrique de Nantes Universit de Nantes Nantes44300 France

Stereoscopic image quality assessment (SIQA) has encountered non-trivial challenges due to the fast proliferation of 3D contents. In the past years, deep learning oriented SIQA methods have emerged and achieved spectacular performance compared to conventional algorithms which are only relied on extracting hand-crafted features. However, most existing deep SIQA evaluators are not specifically built for stereoscopic content and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, to better simulate the binocular rivalry phenomenon, we propose a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality assessment (NR-SIQA). The proposed encoder-decoder architecture is inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions. Besides, we design the Siamese framework to mimic the binocular rivalry in the 3D HVS based on the likelihood and prior maps generated from the predictive coding process. Extensive experimental results on three publicly available stereoscopic image quality databases demonstrate that the devised approach outperforms state-of-the-art algorithms for predicting the perceptual quality of both symmetrically and asymmetrically distorted stereoscopic images with various distortion types. Copyright © 2019, The Authors. All rights reserved.

关键词： Network coding

Exponential synchronization of partial-state coupled linear systems via contraction analysis

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Exponential synchronization of partial-state coupled linear ...

Annual Conference of Industrial Electronics Society

作者： Qichao Ma Ku Du Yu Kang Wei Xing Zheng Jiahu Qin Department of Automation University of Science and Technology of China Hefei China Department of Automation Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China Engineering and Mathematics Western Sydney University Sydney NSW Australia

In this paper, the contraction theory is used to analyze the synchronization for a collection of partial-state linearly coupled linear systems. First, the synchronization problem of the linear systems is transformed by defining proper error variables such that a stability problem of error systems is to be investigated. Then, the contraction analysis is performed with respect to the error system dynamics. It turns out that the error system dynamics is contracting, which in turn proves that the original systems reach synchronization exponentially fast. In addition, a brief comparison between Lyapunov method and contraction analysis is also provided. Finally, two examples are presented in order to illustrate the effectiveness of the theoretical result.

关键词： Synchronization Linear systems Trajectory Eigenvalues and eigenfunctions Symmetric matrices Lyapunov methods

Rethinking the key Factors for the Generalization of Remote Sensing Stereo Matching Networks

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arXiv 2024年

作者： Jiang, Liting Wang, Feng Zhang, Wenyi Li, Peifeng You, Hongjian Xiang, Yuming Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Stereo matching, a critical step of 3D reconstruction, has fully shifted towards deep learning due to its strong feature representation of remote sensing images. However, ground truth for stereo matching task relies on expensive airborne LiDAR data, thus making it difficult to obtain enough samples for supervised learning. To improve the generalization ability of stereo matching networks on cross-domain data from different sensors and scenarios, in this paper, we dedicate to study key training factors from three perspectives. (1) For the selection of training dataset, it is important to select data with similar regional target distribution as the test set instead of utilizing data from the same sensor. (2) For model structure, cascaded structure that flexibly adapts to different sizes of features is preferred. (3) For training manner, unsupervised methods generalize better than supervised methods, and we design an unsupervised early-stop strategy to help retain the best model with pre-trained weights as the basis. Extensive experiments are conducted to support the previous findings, on the basis of which we present an unsupervised stereo matching network with good generalization performance. We release the source code and the datasets at https://***/Elenairene/RKF_RSSM to reproduce the results and encourage future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning