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encoder-decoder Structure With the Feature Pyramid for Depth Estimation From a Single Image

IEEE ACCESS 2021年 9卷 22640-22650页

作者： Tang, Mengxia Chen, Songnan Dong, Ruifang Kan, Jiangming Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.

关键词： Estimation Feature extraction Three-dimensional displays Periodic structures Optimization Decoding Task analysis Depth prediction encoder-decoder feature pyramid single image

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encoder-decoder Neural Network Architecture for solving Job Shop Scheduling Problems using Reinforcement Learning

Encoder-Decoder Neural Network Architecture for solving Job ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Magalhaes, Ricardo Martins, Miguel Vieira, Susana Santos, Filipe Sousa, Joao Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9781728190488

This paper proposes an encoder-decoder neural network architecture with Attention Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the number of operations to schedule is problem dependent. Current state-of-the-art reinforcement learning methods arbitrarily simplify the input information to a fixed-size feature input vector. This way, they end up losing relevant problem information for a large enough number of operations. Furthermore, on the one hand, human schedulers tend to optimize production schedules by moving operations individually into more adequate positions in the schedule. On the other hand, the aforementioned state-of-the-art methods apply heuristics recurrently as their optimization procedure. These limitations come as the cost of the neural network architecture, which is limited to fixed-size inputs and outputs. The architecture proposed in this paper is a Recurrent Neural Network, which enables it to work with inputs and outputs of variable sizes. This decisive feature makes it possible for the agent to move a specific operation to a more adequate position in the schedule and receive explicit problem information, such as the processing times of all operations. In the end, this approach proved to be competitive with a state-of-the-art metaheuristic method, the KGFOA. This promising results come even with a limitation in the available computational resources, which only allowed the development of scarcely trained agent.

关键词： Job-Shop Scheduling Reinforcement Learning Deep Q-Learning encoder-decoder Attention Mechanism Pointer Networks

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Constrained Image Splicing Detection and Localization With Attention-Aware encoder-decoder and Atrous Convolution

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IEEE ACCESS 2020年 8卷 6729-6741页

作者： Liu, Yaqi Zhao, Xianfeng Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100093 Peoples R China

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task and plays an important role in verifying the generating process of a forged image. CISDL conducts dense matching between two investigated images and detects whether one image has forged regions pasted from the other. In this work, we introduce a novel attention-aware encoder-decoder deep matching network named as AttentionDM for CISDL. An encoder-decoder with atrous convolution is newly designed for hierarchical features dense matching and fine-grained masks generation. A novel attention-aware correlation computation module is built on normalization operations and informative features recalibration with channel attention blocks. Last but not least, VGG and ResNets are respectively formulated as feature extractors for comprehensive comparisons in CISDL. Extensive experiments demonstrate the superior performance of AttentionDM over the state-of-the-art methods.

关键词： encoder-decoder atrous convolution normalization channel attention

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Automated tongue segmentation using deep encoder-decoder model

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第24期82卷 37661页

作者： Kusakunniran, Worapan Borwarnginn, Punyanuch Imaromkul, Thanandon Aukkapinyo, Kittinun Thongkanchorn, Kittikhun Wattanadhirach, Disathon Mongkolluksamee, Sophon Thammasudjarit, Ratchainant Ritthipravat, Panrasee Tuakta, Pimchanok Benjapornlert, Paitoon Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand Srinakharinwirot Univ Fac Sci Dept Comp Sci Bangkok Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Epidemiol & Biostat Bangkok Thailand Mahidol Univ Fac Engn Dept Biomed Engn Nakhon Pathom Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Rehabil Med Bangkok Thailand

This paper proposes a solution of tongue segmentation in images. The solution relies on a convolutional neural network, using deep U-Net with deep layers of encoder-decoder modules. The model is trained with a starting resolution of 512 x 512 pixels. To enhance the segmentation performances of the trained model across recording environments, three main types of data augmentations are added in the training process, including additive gaussian noise, multiply and add to brightness, and change color temperature. They could also handle an inadequate number of data samples in the limited datasets. The proposed method is evaluated based on four measurement metrics of Dice coefficient, mean IoU, Jaccard distance, and accuracy. The model is successfully trained on publicly available datasets, and then transferred to be tested with the self-collected dataset in the real-world environment.

关键词： Tongue segmentation Deep U-Net encoder-decoder

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Pedestrian behavior prediction model with a convolutional LSTM encoder-decoder

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 560卷 125132-125132页

作者： Chen, Kai Song, Xiao Han, Daolin Sun, Jinghan Cui, Yong Ren, Xiaoxiang Beihang Univ BUAA Beijing Peoples R China Univ Illinois Urbana IL USA Nanan Primary Sch Xian Shanxi Peoples R China

Pedestrian behavior modeling is a challenging problem especially in crowded transportation scenarios. Some recent studies have addressed this problem using deep neural network, but the accuracy of trajectory prediction is still not high because the internal structure of the typical deep neural network with long short-term memory (LSTM) is a one-dimensional vector, which destroys the spatial information around a pedestrian. Therefore, these models cannot fully learn spatial sensing behavior of pedestrians. To solve this, we recommend using multi-channel tensors to represent the environmental information of pedestrians. Meanwhile, the spatiotemporal interactions among the pedestrians are represented by convolution operations of these tensors. Then, an end-to-end fully convolutional LSTM encoder-decoder is designed, trained and tested. Finally, our approach is compared with existing LSTM-based methods using five crowded video sequences with public datasets. The results show that our method reduces the displacement offset error and provides more realistic trajectory prediction in manifold cases. (c) 2020 Published by Elsevier B.V.

关键词： Pedestrian behavior model Trajectory prediction Long short-term memory Convolution encoder-decoder

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Comprehensive Assessment of Coronary Calcification in Intravascular OCT Using a Spatial-Temporal encoder-decoder Network

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第4期41卷 857-868页

作者： Li, Chao Jia, Haibo Tian, Jinwei He, Chong Lu, Fang Li, Kaiwen Gong, Yubin Hu, Sining Yu, Bo Wang, Zhao Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China Harbin Med Univ Dept Cardiol Affiliated Hosp 2 Harbin 150001 Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu 611731 Peoples R China

Coronary calcification is a strong indicator of coronary artery disease and a key determinant of the outcome of percutaneous coronary intervention. We propose a fully automated method to segment and quantify coronary calcification in intravascular OCT (IVOCT) images based on convolutional neural networks (CNN). All possible calcified plaques were segmented from IVOCT pullbacks using a spatial-temporal encoder-decoder network by exploiting the 3D continuity information of the plaques, which were then screened and classified by a DenseNet network to reduce false positives. A novel data augmentation method based on the IVOCT image acquisition pattern was also proposed to improve the performance and robustness of the segmentation. Clinically relevant metrics including calcification area, depth, angle, thickness, volume, and stent-deployment calcification score, were automatically computed. 13844 IVOCT images with 2627 calcification slices from 45 clinical OCT pullbacks were collected and used to train and test the model. The proposed method performed significantly better than existing state-of-the-art 2D and 3D CNN methods. The data augmentation method improved the Dice similarity coefficient for calcification segmentation from 0.615 +/- 0.332 to 0.756 +/- 0.222, reaching human-level inter-observer agreement. Our proposed region-based classifier improved image-level calcification classification precision and F1-score from 0.725 +/- 0.071 and 0.791 +/- 0.041 to 0.964 +/- 0.002 and 0.883 +/- 0.008, respectively. Bland-Altman analysis showed close agreement between manual and automatic calcification measurements. Our proposed method is valuable for automated assessment of coronary calcification lesions and in-procedure planning of stent deployment.

关键词： Three-dimensional displays Image segmentation Spatial resolution Imaging Medical services Lesions Planning Calcification convolutional neural networks encoder-decoder optical coherence tomography

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Longs short-term based semi-supervised memory encoder-decoder for early prediction of failures in self-lubricating bearings

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Friction 2023年第1期11卷 109-124页

作者： Vigneashwara PANDIYAN Mehdi AKEDDAR Josef PROST Georg VORLAUFER Markus VARGA Kilian WASMER Laboratory for Advanced Materials Processing(LAMP) Swiss Federal Laboratories for Materials Science and Technology(Empa)Thun CH-3602Switzerland AC2T research GmbH Wiener Neustadt 2700Austria

The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service *** a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial ***,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and ***,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance *** article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they *** sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer *** force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum *** this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very *** the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predic

关键词： predictive maintenance in-situ sensing long short-term memory(LSTM) encoder-decoder wear monitoring tribology

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encoder-decoder with Multi-scale Information Fusion for Semantic Image Segmentation 11

Encoder-Decoder with Multi-scale Information Fusion for Sema...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Ma, Xinxin Liu, Kai Ding, Chongyang Yan, Lin Duan, Meiyu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781510635241

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

关键词： Semantic image segmentation multi-scale context module skip-layer encoder-decoder

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encoder-decoder Based CNN Structure for Microscopic Image Identification 27th

Encoder-Decoder Based CNN Structure for Microscopic Image Id...

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27th International Conference on Neural Information Processing

作者： Polap, Dawid Wozniak, Marcin Korytkowski, Marcin Scherer, Rafal Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland Czestochowa Tech Univ Dept Intelligent Comp Syst Czestochowa Poland

ISBN: (纸本)9783030638290;9783030638306

The significant development of classifiers has made object detection and classification by using neural networks more effective and more straightforward. Unfortunately, there are images where these operations are still difficult due to the overlap of objects or very blurred contours. An example is images obtained from various microscopes, where bacteria or other biological structures can merge, or even have different shapes. To this end, we propose a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in such images. In our research, we have included elements such as very weak contours of shapes that may result from the merging of biological objects. The presented method was compared with others, such as a faster recurrent convolutional neural network to indicate the advantages of the proposed solution.

关键词： Convolutional neural networks encoder-decoder Bacteria shape detection

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encoder-decoder WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING BASED SPOKEN LANGUAGE UNDERSTANDING

ENCODER-DECODER WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhu, Su Yu, Kai Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Key Lab Shanghai Educ Commiss Intelligent Interac SpeechLabDept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781509041176

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness

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