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Improving Semantic Segmentation via Label Propagation and Temporal Consistency

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Improving Semantic Segmentation via Label Propagation and Te...

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Feiyu Qin Lumeng Cao Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset.

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Deep Scalable Image Compression via Hierarchical Feature Decorrelation

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Deep Scalable Image Compression via Hierarchical Feature Dec...

Picture Coding Symposium, PCS

作者： Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

The following topics are dealt with: video coding; data compression; image coding; convolutional neural nets; decoding; learning (artificial intelligence); motion compensation; video codecs; image reconstruction; filt...

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InSAR DEM Reconstruction Based on Backprojection Algorithm in Two Converse Flights 6

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InSAR DEM Reconstruction Based on Backprojection Algorithm i...

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

作者： Hu, Xiaoning Xiang, Maosheng Wang, Bingnan Fu, Xikai University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing100190 China National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

A coarse-to-fine framework for learned color enhancement with non-local attention

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arXiv 2019年

作者： Shan, Chaowei Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic color enhancement is aimed to adaptively adjust photos to expected styles and tones. For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously. To address this problem, we propose a coarse-tofine framework with non-local attention for color enhancement in this paper. Within our framework, we propose to divide enhancement process into channel-wise enhancement and pixel-wise refinement performed by two cascaded Convolutional Neural Networks (CNNs). In channel-wise enhancement, our model predicts a global linear mapping for RGB channels of input images to perform global style adjustment. In pixel-wise refinement, we learn a refining mapping using residual learning for local adjustment. Further, we adopt a non-local attention block to capture the long-range dependencies from global information for subsequent fine-grained local refinement. We evaluate our proposed framework on the commonly using benchmark and conduct sufficient experiments to demonstrate each technical component within it. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

No-reference light field image quality assessment based on micro-lens image

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arXiv 2019年

作者： Luo, Ziyuan Zhou, Wei Shi, Likun 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 (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2-D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2-D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Image quality

RC-CNN: Representation-Consistent Convolutional Neural Networks for Achieving Transformation Invariance

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RC-CNN: Representation-Consistent Convolutional Neural Netwo...

IEEE International Conference on systems, Man and Cybernetics

作者： Jun Gu Anfeng He Xinmei Tian CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China

Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while some spatial transformations are applied on images. Herein, we propose representation-consistent neural networks to solve this problem. By introducing consistent losses between the representations in different layers of transformed images, the recognition performance of transformed images is significantly improved. This model not only learns to map from the transformed images to the pre-defined labels but each layer also learns to generate invariant representations when the input images are transformed. All the characteristics of transformation invariance are embedded in the model, which means that no extra parameters or computations are introduced in the well-trained model. Comparative experiments demonstrate the superiority of our model when learning invariance to rotation, translation, and scaling on large-scale image recognition and retrieval tasks.

关键词： Computational modeling Feature extraction Training Image recognition Data models Task analysis Kernel

Prediction of canopy mean traits in herbaceous plants by the UAV multispectral data: The quest for a better leaf-to-canopy upscaling method

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International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Yuanqi Shan Yunlong Yao Lei Wang Zhihui Wang Huaihu Yi Yi Fu Weineng Li Xuguang Zhang Wenji Wang Zhongwei Jing Wetland biodiversity conservation and research center Northeast Forestry University Harbin 150040 China No.26 Hexing Road Xiangfang District College of Wildlife and Protected Northeast Forestry University Harbin 150040 China No.26 Hexing Road Xiangfang District College of Landscape Architecture Northeast Forestry University Harbin 150040 China No.26 Hexing Road Xiangfang District Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 China

Imaging spectroscopy has become a pivotal technique for estimating plant traits at the canopy scale. Accurate trait prediction is critical for biodiversity conservation, yet research on canopy traits in heterogeneous wetlands with complex species mixtures remains scarce. While the Community-Weighted Mean (CWM) method has been widely used for upscaling leaf traits to the canopy level, it often suffers from low model precision, and the suitability of alternative upscaling methods for predicting canopy mean traits using imaging spectroscopy remains uncertain. This study proposed a novel approach for calculating canopy mean traits using the geometric mean method and compared its performance to that of the CWM methods in combination with three modeling algorithms Partial Least Squares Regression (PLSR), Random Forest regression (RF), and Support Vector Machine regression (SVM). The accuracy was evaluated by exploring the predictive ability for nine canopy mean traits by using high spatial resolution UAV multispectral data. The analysis focuses on a wetland ecosystem characterized by high species diversity and hydrological variability, where precise plant trait estimation is essential for ecological process modeling. The results demonstrated that the geometric mean method yielded the highest validation accuracy for most canopy mean traits when paired with the SVM model (e.g., R 2 for N = 0.64, SLA = 0.38, and cellulose = 0.33). Notably, the geometric mean method, combined with UAV multispectral data, significantly enhanced the predictive performance for N, surpassing even that of hyperspectral data. This study underscores the potential of the geometric mean method for upscaling leaf traits to canopy traits. These findings contribute to advancing the prediction accuracy of plant functional traits through remote sensing techniques, while future studies may explore the integration of deep learning methods.

关键词： Geometric mean Multispectral Leaf-to-canopy upscaling method Canopy mean trait SVM

Learned fast HEVC intra coding

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arXiv 2019年

作者： Chen, Zhibo Shi, Jun Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

In High Efficiency Video Coding (HEVC), excellent rate-distortion (RD) performance is achieved in part by having a flexible quadtree coding unit (CU) partition and a large number of intra-prediction modes. Such an excellent RD performance is achieved at the expense of much higher computational complexity. In this paper, we propose a learned fast HEVC intra coding (LFHI) framework taking into account the comprehensive factors of fast intra coding to reach an improved configurable tradeoff between coding performance and computational complexity. First, we design a low-complex shallow asymmetric-kernel CNN (AK-CNN) to efficiently extract the local directional texture features of each block for both fast CU partition and fast intra-mode decision. Second, we introduce the concept of the minimum number of RDO candidates (MNRC) into fast mode decision, which utilizes AK-CNN to predict the minimum number of best candidates for RDO calculation to further reduce the computation of intra-mode selection. Third, an evolution optimized threshold decision (EOTD) scheme is designed to achieve configurable complexity-efficiency tradeoffs. Finally, we propose an interpolation-based prediction scheme that allows for our framework to be generalized to all quantization parameters (QPs) without the need for training the network on each QP. The experimental results demonstrate that the LFHI framework has a high degree of parallelism and achieves a much better complexity-efficiency tradeoff, achieving up to 75.2% intra-mode encoding complexity reduction with negligible rate-distortion performance degradation, superior to the existing fast intra-coding schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal distortion

Deep learning-based video coding: A review and a case study

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arXiv 2019年

作者： Liu, Dong Li, Yue Lin, Jianping 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

The past decade has witnessed great success of deep learning technology in many disciplines, especially in computer vision and image processing. However, deep learning-based video coding remains in its infancy. This paper reviews the representative works about using deep learning for image/video coding, which has been an actively developing research area since the year of 2015. We divide the related works into two categories: new coding schemes that are built primarily upon deep networks (deep schemes), and deep network-based coding tools (deep tools) that shall be used within traditional coding schemes or together with traditional coding tools. For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding scheme and transform coding scheme, respectively. For deep tools, there have been several proposed techniques using deep learning to perform intra-picture prediction, inter-picture prediction, cross-channel prediction, probability distribution prediction, transform, post- or in-loop filtering, down- and up-sampling, as well as encoding optimizations. According to the newest reports, deep schemes have achieved comparable or even higher compression efficiency than the state-of-the-art traditional schemes, such as High Efficiency Video Coding (HEVC) based scheme, for image coding;deep tools have demonstrated the compression capability beyond HEVC for video coding. However, deep schemes have not yet reached the current height of HEVC for video coding, and deep tools remain largely unexplored at many aspects including the tradeoff between compression efficiency and encoding/decoding complexity, the optimization for perceptual naturalness or semantic quality, the speciality and universality, the federated design of multiple deep tools, and so on. In the hope of advocating the research of deep learning-based video coding, we present a case study of our developed prototype video codec, namely Deep Learning Vi

关键词： Video signal processing