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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yao, Li Gao, Ao Wan, Yan Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Recently, implicit function-based approaches have advanced 3D human reconstruction from a single-view image. However, previous methods suffer from issues such as noisy artifacts, loss of geometric details, and broken limbs under the scenarios of challenging poses. To address these problems, a novel end-to-end deep neural network named ReGIR is proposed, which is a multi-level architecture combining the parametric model with implicit function. The architecture consists of a coarse level and a fine level, and for each level, normal maps and the signed distance function (SDF) are introduced to encode query points. Furthermore, the network is trained in a coarse-to-fine manner to enable robust human body reconstruction with geometric details. Our extensive qualitative and quantitative experiments demonstrate that ReGIR achieves competitive reconstruction results.

关键词： Single-view human reconstruction Parametric model Implicit function SDF

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DRIQA-NR: no-reference image quality assessment based on disentangled representation

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signal image AND VIDEO processing 2023年第3期17卷 661-669页

作者： Ye, Zhidan Wu, Yilin Liao, Dong Yu, Tianqi Yang, Jianfeng Hu, Jianling Soochow Univ Suzhou Jiangsu Peoples R China Wuxi Univ Wuxi Jiangsu Peoples R China

Due to the characterization capabilities of deep features, image quality assessment (IQA) methods based on convolutional neural networks (CNNs) have been proposed. However, the existing CNN-based IQA does not make full use of deep features. So, we propose a novel no-reference image quality assessment based on disentangled representation (DRIQA-NR), which decomposes the deep features extracted from distorted images into content features and distortion information features. The content features are used to restore the input image. To eventually predict the quality of the image, features extracted from the restored image and the distorted image are merged with the distortion information feature. In addition, the distortion information features can also be used to improve the performance of full-reference image quality assessment. Experiments on LIVE, CSIQ and TID2013 suggest that the method proposed achieves favorable performance against other methods, with higher average Spearman's rank-order correlation coefficient, Pearson's linear correlation coefficient, Kendall rank-order correlation coefficient values and lower root mean square error.

关键词： No reference image quality assessment Disentangled representation Deep features

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IMPLICIT neural MULTIPLE DESCRIPTION FOR DNA-BASED DATA STORAGE 49

IMPLICIT NEURAL MULTIPLE DESCRIPTION FOR DNA-BASED DATA STOR...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Le, Trung Hieu Pic, Xavier Mateos, Jeremy Antonini, Marc Cote dAzur Univ I3S Lab Sophia Antipolis France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9798350344868;9798350344851

DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

关键词： DNA data storage Multiple Description Coding (MDC) Implicit neural Network (INR) Quaternary Shannon Fano Entropy Coder (SFC4)

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Alzheimer's Dementia Detection: An Optimized Approach using ITD of EEG signals 32

Alzheimer's Dementia Detection: An Optimized Approach using ...

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32nd European signal processing Conference (EUSIPCO)

作者： Sen, Sena Yagmur Akan, Aydin Cura, Ozlem Karabiber Izmir Univ Econ Dept Elect & Elect Engn Izmir Turkiye Izmir Katip Celebi Univ Dept Biomed Engn Izmir Turkiye

ISBN: (纸本)9789464593617;9798331519773

This paper presents a novel early-stage Alzheimer's dementia (AD) disease detection based on convolutional neural networks (CNNs). As it is widely used in detection and classification of AD disease, a time-frequency (TF) method has been proposed for AD detection. It has been described to address the problem of detecting early-stage AD by combining TF and CNN methods. The method is developed by utilizing the well-known structural similarity index measure (SSIM) to obtain discriminative features in each TF image. Experimental results demonstrate that the proposed method outperforms the early-stage AD detection using advanced signal decomposition algorithm that is intrinsic time-scale decomposition (ITD), and it achieves a notable improvement in terms of the detection success rates compared to AD detection from TF images of raw EEG signals.

关键词： Alzheimer's dementia (AD) Electroencephalography (EEG) Intrinsic Time-Scale Decomposition (ITD) Short-Time Fourier Transform (STFT) Convolutional neural Network (CNN)

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A Fault Diagnosis Method of Rotor System Based on Parallel Convolutional neural Network Architecture with Attention Mechanism

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JOURNAL OF signal processing SYSTEMS FOR signal image AND VIDEO TECHNOLOGY 2023年第8期95卷 965-977页

作者： Zhao, Zhiqian Jiao, Yinghou Zhang, Xiang Harbin Inst Technol Sch Mechatron Engn Harbin 150000 Heilongjiang Peoples R China Harbin Inst Technol Lab Vibrat & Noise Control Harbin 150000 Heilongjiang Peoples R China

In practical engineering applications, the working load of the rotor system is changing constantly, and the noise pollution of its working environment is serious, which leads to the performance degradation of traditional fault diagnosis methods. To solve the above problems, we present a novel rotor system fault diagnosis model based on parallel convolutional neural network architecture with attention mechanism (AMPCNN). The model uses convolution kernels of different sizes in parallel channels to process raw data, and based on late feature fusion, a more comprehensive feature map is obtained. Furthermore, the information sharing between the two channels is realized through the attention mechanism so that the effective features of one channel can be reflected in another channel. The performance of the model under variable working conditions is verified by the Machinery Fault Database (MAFAULDA), and the average accuracy is 99.58%. By dividing Gaussian white noise from -9 dB to 2 dB into 11 intervals and adding it to the public data of Wuhan University, the noise resistance performance is verified, and the proposed method can obtain 100% diagnosis accuracy even in the high noise condition. The above experiments show that in terms of load adaptability and noise immunity, the method has higher accuracy than traditional deep learning classification methods.

关键词： Rotor system Fault diagnosis Feature fusion Convolutional neural network Attention mechanism

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Riemannian Generalized Gaussian Distributions on the Space of SPD Matrices for image Classification

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IEEE ACCESS 2024年 12卷 26096-26109页

作者： Abbad, Zakariae El Maliani, Ahmed Drissi El Hassouni, Mohammed Abbassi, Mohamed Tahar Kadaoui Bombrun, Lionel Berthoumieu, Yannick Mohammed V Univ Rabat ENSIAS Rabat 10000 Morocco Mohammed V Univ Rabat Fac Sci LRIT Rabat IT Ctr Rabat 10000 Morocco Mohammed V Univ Rabat FLSH Rabat 10000 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz Lab Math Sci & Applicat Fes 30000 Morocco Univ Bordeaux CNRS Bordeaux INP IMSUMR 5218 F-33400 Talence France Bordeaux Sci Agro F-33175 Gradignan France

The space of symmetric positive definite (SPD) matrices, denoted as $P_{m}$ , plays a crucial role in various domains, including computer vision, medical imaging, and signal processing. Its significance lies in its capacity to represent the underlying structure in nonlinear data using its Riemannian geometry. Nevertheless, a notable gap exists in the absence of statistical distributions capable of characterizing the statistical properties of data within this space. This paper proposes a new Riemannian Generalized Gaussian distribution (RGGD) on that space. The major contributions of this paper are, first of all, providing the exact expression of the probability density function (PDF) of the RGGD model, as well as an exact expression of the normalizing factor. Furthermore, an estimation of parameters is given using the maximum likelihood of this distribution. The second contribution involves exploiting the second-order statistics of feature maps derived from the first layers of deep convolutional neural networks (DCNNs) through the RGGD stochastic model in an image classification framework. Experiments were carried out on four well-known datasets, and the results demonstrate the efficiency and competitiveness of the proposed model.

关键词： Symmetric positive definite matrices generalized Gaussian distribution texture Riemannian geometry Rao's distance Riemannian metric

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MMUNet: Morphological feature enhancement network for colon cancer segmentation in pathological images

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BIOMEDICAL signal processing AND CONTROL 2024年 91卷

作者： Yuan, Haojun Chen, Lingna He, Xiaofeng Univ South China Comp Sch Hengyang 421001 Peoples R China

Colon image analysis is an important step in diagnosing colon cancer, and achieving automated and accurate segmentation remains a challenging problem because of the diversity of cell shapes and boundaries in pathological sections. In this paper, we propose a U-shaped colon cancer segmentation network, which combines depth-separable convolution and morphological methods to reduce the number of model parameters and effectively improve segmentation accuracy. We improve the global and local feature capabilities by taking advantage of serial convolution and external focus as the underlying architecture for the model. We designed the skip connection to fuse the features from the encoder in a morphological way to enhance the morphological features. We introduced an edge enhancement module by extracting contour information using morphological methods to enhance edge features. We evaluated the proposed method on three colon cancer datasets, and the experimental results showed that our method with a small number of parameters has a Dice coefficient of 92.76% +/- 5.86% on the Glas dataset, 86.11% +/- 7.11% on the CoCaHis dataset, and 91.61% +/- 11.25% on the Colon dataset. The code will be openly available at https://***/Yuanhaojun513/MMUNet.

关键词： Pathological images Convolution neural network Colon cancer segmentation Morphological methods

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A new health indicator extracted by unsupervised learning using autoencoder in tandem with t-sne and multi-kernel CNN to enhance the early detection and classification of bearings multi-faults

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第7期45卷 1-14页

作者： Zair, Mohamed Rahmoune, Chemseddine Imane, Moussaoui Amine, Mahami Benazzouz, Djamel Univ Mhamed Bougara Boumerdes Solid Mech & Syst Lab LMSS Boumerdes 35000 Algeria

The conventional process diagnostic scheme comprises data acquisition, feature extraction, and fault classification. However, traditional feature extraction uses signal processing technologies that are deeply reliant on both subjectivity and foreknowledge. However, these techniques have shown some limitations that are affecting efficiency and effectiveness, especially for varying fault and noisy environments. To address these issues, this paper proposes a practical and effective unsupervised deep learning methodology based on autoencoder (AE) in tandem with t-distributed stochastic neighbor embedding (t-sne) and multi-kernel convolutional neural network. In this approach, the raw data are first extracted in the time domain, normalizing and segmenting the vibration signal into small portions by sliding window to keep more information data. Subsequently, during autoencoder (AE) training, the dropout smoothing and batch normalization are used to avoid overfitting and to extract the deep features of the retread form from the normalized data set in the time domain. Then, the nonlinear mapping obtained in the high-dimensional data is reduced using the t-sne algorithm by deleting redundant and insignificant parameters that can confuse the classification. Finally, the measurement with low-dimensional feature vectors is selected as inputs of the deep structure of multi-kernel CNN for automatic fault detection and classification. Comparative studies were implemented with several techniques including CNN, KNN and SVM to diagnose and differentiate different types of defects in bearings. The results showed that the proposed approach is effective for bearings in terms of predictive accuracy with higher accuracy 99.46% compared to conventional methods.

关键词： Rolling bearings Autoencoder Visualization data Faults diagnosis t-Distributed stochastic neighbor embedding (t-sne) Fault classification Multi-kernel conventional neural network (CNN)

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Modeling the Detection and Classification of Tomato Leaf Diseases Using a Robust Deep Learning Framework

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TRAITEMENT DU signal 2024年第4期41卷 1667-1678页

作者： Gupta, Manish Yadav, Dharmveer Khan, Safdar Sardar Kumawat, Ashish Kumar Chourasia, Ankita Rane, Pinky Ujlayan, Anshul GLA Univ Dept Elect & Commun Engn Mathura 281406 India St Xaviers Coll Jaipur Dept Comp Sci Jaipur 302029 India Engn Med Caps Univ Dept Comp Sci Indore 453331 India Graphnexti Chief Data Scientist & Mentor Dept Greater Noida 201310 India

Tomatoes areAa noteworthy horticultural crop that has considerable importance in a diverse range of culinary traditions. At now, the primary concern for food security is in the realm of plant diseases. Researchers are actively working towards developing a streamlined approach to detect and diagnose illnesses in their early stages, with the ultimate goal of enhancing the agricultural industry. Currently, computer scientists and engineers are actively engaged in the fast development of a diverse range of tools and methodologies, with a special focus on the field of artificial intelligence. The advancement of cutting-edge machine learning applications for artificial intelligence relies on the establishment of original methods and frameworks. In contrast to the single-layer topologies of more traditional neural network learning methods, "deep learning" makes use of networks with many processing layers. In this research, a DL model is developed to detect & diagnose plant diseases by analyzing healthy & unhealthy plant image samples using deep learning techniques. The dataset contains 43,823 images of plants, including healthy plants and unhealthy plants. For this model implementation, follow some methodology processes like data preprocessing, image segmentation, data balancing, data splitting classification and detection, and assess the model's efficacy. The study uses a Fine-Tuned EfficientNetB7 approach with an impressive Mean Average Accuracy of 99.31%. AThe proposed technique demonstrates efficacy in early detection and has the potential for further improvement in terms of performance, hence facilitating the development of a real-world automated system for detecting plant diseases in agricultural settings.

关键词： plant diseases image processing machine learning deep learning intelligence

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image enhancement with intensity transformation on embedding space

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 101-115页

作者： Hanul Kim Yeji Jeon Yeong Jun Koh Department of Applied Artificial Intelligence Seoul National University of Science and TechnologySeoulSouth Korea Department of Computer Science and Engineering Chungnam National UniversityDaejeonSouth Korea

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained ***,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality *** order to address this limitation,a simple yet effective approach for image enhancement is *** proposed algorithm based on the channel-wise intensity transformation is ***,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to *** this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding ***,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced *** experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space ***,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch ***,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

关键词： computer vision deep learning image enhancement image processing

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