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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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R3L: CONNECTING DEEP REINFORCEMENT LEARNING TO RECURRENT NEURAL NETWORKS FOR image DENOISING VIA RESIDUAL RECOVERY

R3L: CONNECTING DEEP REINFORCEMENT LEARNING TO RECURRENT NEU...

引用

IEEE international conference on image processing (ICIP)

作者： Zhang, Rongkai Zhu, Jiang Zha, Zhiyuan Dauwels, Justin Wen, Bihan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Delft Univ Technol Dept Microelect Delft Netherlands

ISBN: (纸本)9781665441155

State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture search in image restoration, how it is connected to the classic deterministic training in solving inverse problems remains unclear. In this work, we propose a novel image denoising scheme via Residual Recovery using Reinforcement Learning, dubbed R3L. We show that R3L is equivalent to a deep recurrent neural network that is trained using a stochastic reward, in contrast to many popular denoisers using supervised learning with deterministic losses. To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance. With such a unified benchmarking system, we demonstrate that the proposed R3L has better generalizability and robustness in image denoising when the estimated noise level varies, comparing to its counterparts using deterministic training, as well as various state-of-the-art image denoising algorithms.

关键词： Recurrent Neural Network Deep Reinforcement Learning image Denoising Residual Recovery

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OPENING DEEP NEURAL NETWORKS WITH GENERATIVE MODELS

OPENING DEEP NEURAL NETWORKS WITH GENERATIVE MODELS

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IEEE international conference on image processing (ICIP)

作者： Vendramini, Marcos Oliveira, Hugo Machado, Alexei dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Univ Fed Minas Gerais Dept Anat & Imaging Belo Horizonte MG Brazil Pontificia Univ Catolica Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781665441155

image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pre-trained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.

关键词： Open Set Recognition image Classification Deep Visual Learning Out-of-Distribution Detection

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Research on image Dimensionality Reduction algorithms 4

Research on Image Dimensionality Reduction Algorithms

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4th international conference on Sensors, signal and image processing, SSIP 2021

作者： He, Zulun Zhang, Jingjun Shanghai Dehong International School China Ningbo Zhenhai Jiaochuan Academy China

ISBN: (纸本)9781450385725

This paper summarizes the principle of the Scale Invariant Feature (SIFT), Principal Component Analysis (PCA), and PCANet. Also, the paper uses IPython to realize the similarity comparison using SIFT and handwriting recognization using PCANet, then calculate the precision and recall of the result. The main thing of the SIFT is to find the key point descriptor through the Scale-space, Gaussian Pyramid, and Difference of Gaussian Pyramid (DoG), while the primary purpose of PCA is to find the eigenvalue and eigenvector through calculating the covariance. The combination of the PCA and neuro network, PCANet is divided into three stages. © 2021 ACM.

关键词： Principal component analysis

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Garbage Classification System with YOLOV5 Based on image Recognition 6

Garbage Classification System with YOLOV5 Based on Image Rec...

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6th international conference on signal and image processing, ICSIP 2021

作者： Yang, Guanhao Jin, Jintao Lei, Qujiang Wang, Yi Zhou, Jiangkun Sun, Zhe Li, Xiuhao Wang, Weijun Intelligent Robot and Equipment Center Guangzhou Institute of Advanced Technology Chinese Academy of Sciences Guangzhou511458 China University of Chinese Academy of Sciences Beijing100039 China

ISBN: (纸本)9780738133737

At present, the main technology of garbage identification and classification is the use of traditional machine vision algorithm or the use of sensors for screening and identification of garbage, in garbage sorting, the first accurate identification and classification of garbage is very necessary. By collecting various types of garbage pictures and building detection data sets, we adopt the garbage identification and detection algorithm based on YOLO-V5 and use data enhancement to improve the robustness of the model, to achieve fast and accurate identification of different types of garbage. Experimental results show that this method has high accuracy, short time consumption, and good robustness. © 2021 IEEE.

关键词： Computer vision

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Performance Analysis of Machine Learning-based Face Detection algorithms in Face image Transmission over AWGN and Fading Channels 16

Performance Analysis of Machine Learning-based Face Detectio...

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16th IEEE international Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Kim, Junghwan Wei, Lan Univ Toledo EECS Dept 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9781665449083

Although machine learning and artificial intelligence have been widely applied, noise and interference are still major disturbances to degrade the quality of image transmission and processing efficiency in multimedia data transmission. Based on the face detection system utilizing the machine learning algorithms and artificial intelligence, this paper examines, analyzes and compares the performances of the AdaBoost machine learning algorithm and Convolutional Neural Network (CNN)-based algorithm in processing face information, which is disturbed by channel noise and fading effect encountered in the transmission of face image. The face detection system used is based on HAAR feature extraction. The extracted HAAR features are subjected to classification training and learning of the cascade classifier. Then the face detection is performed on the picture information outside the database. Results of computer simulation show that for the image data affected by the fading and AWGN, the face recognition system still marked the positions of the eyes and mouth with high accuracy. However for fading and higher AWGN, using a machine learning algorithm with a convolutional neural network is better than the ADABOOST algorithm. It can be concluded that the machine learning algorithm can effectively reduce the adverse effect of multimedia data transmission without increasing the SNR and use of higher level of modulation scheme.

关键词： Machine learning Artificial intelligence-based multimedia coding and processing advanced signal processing Face detection system BPSK/QPSK Fading Channel

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A NOVEL MEDICAL image FUSION FRAMEWORK INTEGRATING MULTI-SCALE ENCODER-DECODER WITH DISCRETE WAVELET DECOMPOSITION 49

A NOVEL MEDICAL IMAGE FUSION FRAMEWORK INTEGRATING MULTI-SCA...

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49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liu, Renhe Liu, Yu Wang, Han Hu, Kai Du, Shan Tianjin Univ Sch Microelect Tianjin Peoples R China Univ British Columbia Dept Comp Sci Math Phys & Stat Okanagan Campus Vancouver BC Canada

ISBN: (纸本)9798350344868;9798350344851

In recent years, many fusion algorithms based on multi-scale transform or neural networks have been proposed to improve medical image fusion (MIF) performance. However, there is still enormous potential to explore the combination of different fusion theories. In this paper, we propose a novel MIF framework to integrate powerful feature representation abilities of the deep learning model and accurate frequency decomposition characteristics of discrete wavelet transform (DWT). Firstly, a multi-scale encoder-decoder network is well-trained to extract feature information in different scales and achieve efficient image reconstruction. In particular, DWT is introduced into each scale to decompose the extracted features into high- and low-frequency sub-bands for information preservation during down-sampling. An elaborate feature fusion process is designed to achieve multi-scale fusion while merging different frequency sub-bands. Experiment results on benchmark datasets demonstrate that the proposed fusion framework outperforms current state-of-the-art methods with comparable time complexity in both objective and subjective evaluation.

关键词： medical image fusion discrete wavelet transform multi-scale encoder-decoder high- and low-frequency sub-bands feature fusion

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2021 IEEE 6th international conference on Intelligent Computing and signal processing, ICSP 2021

2021 IEEE 6th International Conference on Intelligent Comput...

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6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

ISBN: (纸本)9780738143705

The proceedings contain 310 papers. The topics discussed include: single color image dehazing based on variational model;application research of submersible diaphragm pump based on nonmetal coiled tubing;a study on safety issues of human-machine transitions in autonomous vehicles;intelligent diagnosis method for intermittent fault of digital circuit based on dynamic power current;selection of communication frequency points for shortwave radio network based on VOACAP;research and practice of industrial sorting technology based on machine vision;a research on age of information minimization scheme of wireless sensor network assisted by UAV;improved affine projection algorithms with momentum term;NORA based group paging overload control method for massive MTC accesses under 5G networks;a data model and algebraic operation for multidimensional analysis of oil and gas exploration data;and research on image steganography information detection based on support vector machine.

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Gait Phase Recognition Based on Air-pressure Mechanomyogram and Sensor Fusion 6

Gait Phase Recognition Based on Air-pressure Mechanomyogram ...

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6th IEEE international conference on advanced Robotics and Mechatronics (ICARM)

作者： Wang, Enkai Huang, Jian Li, Yuge Cui, Yuqi Li, Xiaolong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9780738133645

Gait phase recognition can improve the control of lower limb exoskeleton robot and promote human-machine collaboration. One of the current mainstream methods is to use electromyogram (EMG) to recognize gait phase, but the EMG signal has shortcomings such as weak signal, difficult to wear, easy to be affected by noise and sweat. Therefore, we designed a novel air-pressure mechanomyograph (PMMG) sensor, and further made a wearable PMMG-based sensing system composed of PMMG-based thighrings and inertial measurement units (IMUs). In order to improve the performance of gait phase recognition, we used five popular machine learning algorithms to fuse the data from PMMG-based thighrings and IMUs. We recruited three experimental subjects and constructed two datasets for different walking conditions: constant speed walking and variable speed walking. Experimental results show that the proposed PMMG sensor is effective, and the gait phase recognition accuracy reached 96.25% by using only the PMMG-based thighrings. In addition, we found that the performance of multi-modal sensor fusion is better than that of single-modal sensor fusion through three comparative experiments. Among the five machine learning algorithms, the SVM fusion model got the highest average accuracy of 98.82%.

关键词： Legged locomotion Support vector machines Machine learning algorithms Mechatronics Measurement units Multimodal sensors Sensor fusion

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SepUnet: DEPTHWISE SEPARABLE CONVOLUTION INTEGRATED U-NET FOR MRI RECONSTRUCTION

SepUnet: DEPTHWISE SEPARABLE CONVOLUTION INTEGRATED U-NET FO...

引用

IEEE international conference on image processing (ICIP)

作者： Zabihi, Soheil Rahimian, Elahe Asif, Amir Mohammadi, Arash Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada

ISBN: (纸本)9781665441155

Accelerating Magnetic Resonance Imaging (MRI) acquisition process is a critical and challenging medical imaging problem as basic reconstructions obtained from the undersampled k-space often exhibit blur or aliasing effects. Despite its significance and recent advancements in the field of deep neural networks (DNNs), development of deep learning-based MRI reconstruction algorithms is not yet flourished due to unavailability of public and large datasets. The recently introduced large-scale fastMRI dataset is posed to change this state of affairs, however, existing DNN solutions developed based on fastMRI require learning a large number of parameters rendering their practical application limited due to the strict low-latency requirements of real-time MRI acquisition. In this paper, we aim to address this drawback and target reducing the computational cost associated with single-coil reconstruction task. More specifically, the paper proposes a novel deep model referred to as the SepUnet architecture achieving significant reduction in the required number of parameters while maintaining high accuracy. Performance of the proposed SepUnet architecture is evaluated based on the official test dataset from fastMRI illustrating accuracy improvement in comparison to its published counterparts while requiring significantly reduced number of trainable parameters (i.e., the SepUnet architecture is much faster and lighter than its counterparts).

关键词： Magnetic Resonance Imaging (MRI) image Reconstruction Depthwise Separable Convolution Atrous Spatial Pyramidal Pooling (ASPP)

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ATTENTION BASED NETWORK FOR NO-REFERENCE UGC VIDEO QUALITY ASSESSMENT

ATTENTION BASED NETWORK FOR NO-REFERENCE UGC VIDEO QUALITY A...

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IEEE international conference on image processing (ICIP)

作者： Yi, Fuwang Chen, Mianyi Sun, Wei Min, Xiongkuo Tian, Yuan Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tencent Social Commun Lab Shenzhen Peoples R China

ISBN: (纸本)9781665441155

The quality assessment of user-generated content (UGC) videos is a challenging problem due to the absence of reference videos and their complex distortions. Traditional no-reference video quality assessment (NR-VQA) algorithms mainly target specific synthetic distortions. Less attention has been paid to authentic distortions in UGC videos, which are not distributed evenly in both the spatial and temporal domains. In this paper, we propose an end-to-end neural network model for UGC video quality assessment based on the attention mechanism. The key step in our approach is to embed the attention modules in the feature extraction network, which effectively extracts local distortion information. In addition, to exploit the temporal perception mechanism of the human visual system (HVS), the gated recurrent unit (GRU) and temporal pooling layer are integrated into the proposed model. We validate the proposed model on three public in-the-wild VQA databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm. Experimental results demonstrate that the proposed method outperforms state-of-the-art NR-VQA models. The implementation of our method is released at https://***/qingshangithub/AB-VQA.

关键词： No-reference video quality assessment user-generated content videos attention mechanism

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