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检索条件"主题词=Encoder-decoder network"

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DISCRIMINATIVE FEATURES RECONSTRUCTION network FOR SEMANTIC SEGMENTATION 44

DISCRIMINATIVE FEATURES RECONSTRUCTION NETWORK FOR SEMANTIC ...

引用

44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhou, Qiuhao Ma, Yanbo Lu, Haihua Chen, Xuesong Zhao, Yong Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China

ISBN: (纸本)9781479981311

Thanks to the development of convolutional neural networks (CNNs), researchers have proposed lots of effective semantic segmentation models. However, there are still two problems disturbing researchers, one of which is objects misidentification on the image level and another one is poor performance on details, especially the boundary of objects. 'lb tackle these two problems, we propose Discriminative Features Reconstruction network (DER) containing two modules: Second-order Pyramid Features Reconstruction Module (SPFR) and Second-order Boundary Attention Module (SBA). Specifically, SPFR fuses different scales features to gain pyramid receptive field. Besides, SPFR extracts second order statistics data to retrieve more discriminative features. Furthermore, we put forward SBA that is helpful to refine the segmentation results. On SBA, low-level features recover localization details under the high-level feature guidance. Our DFR achieves state-of-the-art performance on PASCAL VOC 2012 dataset with mloU accuracy 81.1% without pre-training COCO dataset and post-processing.

关键词： Semantic segmentation second-order statics attention mechanism image processing encoder-decoder network

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MSINET: Multi-scale Interconnection network for Medical Image Segmentation 40th

MSINET: Multi-scale Interconnection Network for Medical Imag...

引用

40th Computer Graphics International Conference (CGI)

作者： Xu, Zhengke Shan, Xinxin Wen, Ying East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9783031500787;9783031500770

In this work, an improved end-to-end U-Net structure, a hierarchical multi-scale interconnection network (HMINet), is proposed to make full use of the information contained in different feature maps in encoders and decoders to improve the accuracy of medical image segmentation. The network consists of two main components: a multi-scale fusion unit (MSF) and a multi-head feature enhancement unit (MFE). In the encoder part, the multi-scale fusion unit is used to fuse the information between the feature maps of different scales. By using convolution at different levels, a wider range of context information can be captured and fused into a more comprehensive representation of features. In the decoder part, multiple feature enhancement units can fully pay attention to the coordinates and channel information between feature maps, and then splice the encoded feature maps step by step to maximize the use of information from different feature maps. These feature maps are joined by a well-designed skip connection mechanism to retain more feature information and minimize information loss. The proposed method is tested on four public medical datasets and compared with other classical image segmentation models. The results show that HMINet can significantly improve the accuracy of medical image segmentation tasks and exceed the performance of other models in most cases.

关键词： Transformer-based method Multi-scale fusion Feature enhancement encoder-decoder network Medical image segmentation

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Automatic Reverse Engineering: Creating Computer-Aided Design (CAD) Models from Multi-view Images 45th

Automatic Reverse Engineering: Creating Computer-Aided Desig...

引用

45th Annual Conference of the German-Association-for-Pattern-Recognition (DAGM GCPR)

作者： Jobczyk, Henrik Homann, Hanno Hannover Univ Appl Sci Hannover NH Germany

ISBN: (纸本)9783031546044;9783031546051

Generation of computer-aided design (CAD) models from multi-view images may be useful in many practical applications. To date this problem is usually solved with an intermediate point-cloud reconstruction and involves manual work to create the final CAD models. In this contribution, we present a novel network for an automated reverse engineering task. Our network architecture combines three distinct stages: A convolutional neural network as the encoder stage, a multi-view pooling stage and a transformer-based CAD sequence generator. The model is trained and evaluated on a large number of simulated input images and extensive optimization of model architectures and hyper-parameters is performed. A proof-of-concept is demonstrated by successfully reconstructing a number of valid CAD models from simulated test image data. Various accuracy metrics are calculated and compared to a state-of-the-art point-based network. Finally, a real world test is conducted supplying the network with actual photographs of two three-dimensional test objects. It is shown that some of the capabilities of our network can be transferred to this domain, even though the training exclusively incorporates purely synthetic training data. However to date, the feasible model complexity is still limited to basic shapes.

关键词： computer-aided design (CAD) multi-view reconstruction encoder-decoder network

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encoder-decoder Architecture for Crop-Weed Classification Using Pixel-Wise Labelling

Encoder-Decoder Architecture for Crop-Weed Classification Us...

引用

International Conference on Artificial Intelligence and Signal Processing (AISP)

作者： Umamaheswari, S. Jain, Ashvini, V Anna Univ Dept Informat Technol Madras Tamil Nadu India

ISBN: (纸本)9781728144580;9781728144566

Agricultural growth is an important pathway in development of any country. Its productivity contributes in full filling the basic need of the human society. The productivity is therefore must be smoothen to provide quality and quantity. Reduction in usage of chemicals like pesticides and herbicides to provide quality and quantity. The major factor that affect the quantity is the presences of weed in the crop field. The nutrient present in the soil is therefore observed by both the weed and the crop. Manual removal of weed from crop is tedious, time consuming and costly. Spraying of herbicides over the field affects the quality of the crop. Emergence of technology in the agriculture field paves the path for selective spraying and robot removal of weed, which requires high accuracy classification of crop from weed. Therefore an encoder-decoder architecture based on VGG16 architecture is used for the pixel-wise segmentation. The architecture consists of convolutional layers with ReLU, Normalization layer and max-pooling layer.

关键词： Agriculture Weed detection Deep learning encoder-decoder network

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Maximal Information Complemented Refinement network for Gland Instance Segmentation

Maximal Information Complemented Refinement Network for Glan...

引用

IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Xu, Bowen Wang, Yankai Yang, Dawei Zhang, Wei Zhang, Ye Kong, Yajie Zhang, Wenqiang Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun Jilin Peoples R China

ISBN: (纸本)9781728162157

The analysis of glandular morphology is a crucial step to determine the presence and grade of cancer. The rise of computational pathology has led to the development of automated segmentation to overcome the time-consuming manual segmentation. Although the existing encoder-decoder networks haved made significant progress, the downsample operation causes fine-grain information loss. It deteriorates boundaries' localization especially in malignant cases. In this paper, we propose a maximal information complemented refinement network based on UNet. We extend the skip connection with two information complement, aggregate spatial detail information by reuse low-level features, and introduce semantic information by high-level feature guidance. Besides, a weighted cross-entropy loss and generalized dice loss is used to tackle the fuzzy boundary and class imbalance. We evaluated our model against a dozen recent deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset. Extensive experiments show that our proposal achieves the best overall performance, immensely improves the performance of malignant cases.

关键词： Histopathological image analysis gland instance segmentation encoder-decoder network

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Robust Adaptive Pulse Compression Under Encode-decoder network for Synthetic Aperture Radar 7

Robust Adaptive Pulse Compression Under Encode-Decoder Netwo...

引用

7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lu, Qianrong Wang, Xiji Wang, Zhicheng Ma, Xianchao Yang, Na Shanghai Radio Equipment Res Inst Shanghai Peoples R China

ISBN: (纸本)9781728173337

The matched filter is widely used in traditional synthetic aperture radar system with the criteria of maximum output signal-noise-ratio (SNR). However, matched filter has high-level side lobes (for example, 13.6dB without windowing) resulting in severe masking for adjacent weak targets. Recently, an adaptive pulse compression (APC) was developed based on minimum mean square error (MMSE) between true ground profiles and compressed outputs. Though has better compression results, it requires tremendous amount of time for processing, which hinders its applications. In this paper, a fast implementation named iterative adaptive pulse compression (IAPC), is designed whereby the respective tap coefficients of all filters are jointly updated with a recursive way and subsequently we incorporate it into the classic range Doppler algorithm via changing the processing sequence, which is shown to be obviously efficient with pulse duration increasing. Moreover, an encoder-decoder architecture is developed for improving the targets' energy to solve the SNR-dependence of both APC and IAPC. As a result, through various stressing experiments including simulation and real data validation, the proposed method is shown to be superior to the original APC and matched filter in the views of time efficiency and peak sidelobe ratio (PSLR) respectively.

关键词： Iterative adaptive pulse compression encoder-decoder network Synthetic aperture radar

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Dual-Stream Pyramid Registration network 22nd

Dual-Stream Pyramid Registration Network

引用

10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Hu, Xiaojun Kang, Miao Huang, Weilin Scott, Matthew R. Wiest, Roland Reyes, Mauricio Malong Technol Shenzhen Peoples R China Shenzhen Malong Artificial Intelligence Res Ctr Shenzhen Peoples R China Univ Inst Diagnost & Intervent Neuroradiol Univ Hosp Support Ctr Adv Neuroimaging Bern Switzerland Univ Bern Univ Hosp Insel Data Sci Ctr Inselspital Bern Switzerland

ISBN: (纸本)9783030322458;9783030322441

We propose a Dual-Stream Pyramid Registration network (referred as Dual-PRNet) for unsupervised 3D medical image registration. Unlike recent CNN-based registration approaches, such as Voxel-Morph, which explores a single-stream encoder-decoder network to compute a registration field from a pair of 3D volumes, we design a two-stream architecture able to compute multi-scale registration fields from convolutional feature pyramids. Our contributions are two-fold: (i) we design a two-stream 3D encoder-decoder network which computes two convolutional feature pyramids separately for a pair of input volumes, resulting in strong deep representations that are meaningful for deformation estimation;(ii) we propose a pyramid registration module able to predict multi-scale registration fields directly from the decoding feature pyramids. This allows it to refine the registration fields gradually in a coarse-to-fine manner via sequential warping, and enable the model with the capability for handling significant deformations between two volumes, such as large displacements in spatial domain or slice space. The proposed Dual-PRNet is evaluated on two standard benchmarks for brain MRI registration, where it outperforms the state-of-the-art approaches by a large margin, e.g., having improvements over recent VoxelMorph [2] with 0.683 -> 0.778 on the LPBA40, and 0.511 -> 0.631 on the Mind-boggle101, in term of average Dice score.

关键词： Medical image registration encoder-decoder network Deformable registration Brain MRI

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Vectorized Beam Search for CTC-Attention-based Speech Recognition 20

Vectorized Beam Search for CTC-Attention-based Speech Recogn...

引用

Interspeech Conference

作者： Seki, Hiroshi Hori, Takaaki Watanabe, Shinji Moritz, Niko Le Roux, Jonathan Toyohashi Univ Technol Toyohashi Aichi Japan Mitsubishi Elect Res Labs MERL Cambridge MA USA Johns Hopkins Univ Baltimore MD 21218 USA

This paper investigates efficient beam search techniques for end-to-end automatic speech recognition (ASR) with attention-based encoder-decoder architecture. We accelerate the decoding process by vectorizing multiple hypotheses during the beam search, where we replace the score accumulation steps for each hypothesis with vector-matrix operations for the vectorized hypotheses. This modification allows us to take advantage of the parallel computing capabilities of multi-core CPUs and GPUs, resulting in significant speedups and also enabling us to process multiple utterances in a batch simultaneously. Moreover, we extend the decoding method to incorporate a recurrent neural network language model (RNNLM) and connectionist temporal classification (CTC) scores, which typically improve ASR accuracy but have not been investigated for the use of such parallelized decoding algorithms. Experiments with LibriSpeech and Corpus of Spontaneous Japanese datasets have demonstrated that the vectorized beam search achieves 1.8x speedup on a CPU and 33x speedup on a GPU compared with the original CPU implementation. When using joint CTC/attention decoding with an RNNLM, we also achieved 11x speedup on a GPU while maintaining the benefits of CTC and RNNLM. With these benefits, we achieved almost real-time processing with a small latency of 0.1 x real-time without streaming search process.

关键词： speech recognition beam search parallel computing encoder-decoder network GPU

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MOTION-BASED WAVE INFERENCE WITH NEURAL networkS: TRANSFER LEARNING FROM NUMERICAL SIMULATION TO EXPERIMENTAL DATA 42

MOTION-BASED WAVE INFERENCE WITH NEURAL NETWORKS: TRANSFER L...

引用

ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Bisinotto, Gustavo A. De Mello, Pedro C. Cozman, Fabio G. Tannuri, Eduardo A. Univ Sao Paulo Numer Offshore Tank TPN Sao Paulo SP Brazil Univ Sao Paulo Sao Paulo SP Brazil

ISBN: (纸本)9780791886830

The directional wave spectrum, which describes the distribution of wave energy along frequencies and directions, can be estimated from the measured motions of a vessel subjected to a particular sea condition by resorting to the wave-buoy analogy. Several methods have been proposed to address the inverse estimation problem;recently, machine learning techniques have been assessed as further alternatives. However, it may be difficult to gather large datasets of in-service motion responses and the associated sea states to train effective data-driven models. In this work, an encoder-decoder neural network is trained with the synthetic responses of a station-keeping platform supply vessel (PSV) to estimate the directional wave spectrum. This estimation model is directly applied to perform wave inference from motion data of wave basin tests with a small-scale model of the same vessel. Furthermore, fine-tuning is also used to incorporate experimental data into the neural network model. Results show a satisfactory match between estimated and measured values, both with respect to the energy distribution and the integral spectrum parameters, indicating that the proposed approach can be employed to obtain data-driven wave inference models when there is little or no availability of measured motion records and the corresponding sea conditions.

关键词： Directional wave spectrum encoder-decoder network transfer learning wave-buoy analogy platform supply vessel model tests

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AREA-AWARE REFLECTION DETECTION AND REMOVAL FOR SINGLE IMAGE 16

AREA-AWARE REFLECTION DETECTION AND REMOVAL FOR SINGLE IMAGE

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16th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Fu, Rong Kuang, Ping Zhou, Yang Yan, Hua-Rui Zheng, Ting-Ying Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728142425

Images taken through transparent glass are usually covered with unwanted reflective layers, which can affect the effects of many computer vision tasks, so eliminating these reflections has a wide range of applications in image processing studies. In fact, the areas in the reflective layer that have a great impact on the image are often those with high reflectivity. Therefore, in this paper, we propose a deep learning model that can remove the reflection from a single image. The model can automatically detect the reflection area and obtains a reflective-free background image. After extensive comparative experiments and validation, the model shows excellent performance.

关键词： Area-aware reflection detection Reflection removal encoder-decoder network Single image Regional property

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