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15th International Work-Conference on artificial neural networks (IWANN)

作者： Zdunek, Rafal Fonal, Krzysztof Sadowski, Tomasz Wroclaw Univ Sci & Technol Fac Elect Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783030205188;9783030205171

The topic of image completion has received increasing attention in recent years, motivated by many important applications in computer vision, data mining and image processing. In this study, we consider the problem of recovering missing values of pixels in highly incomplete images with a random or irregular structure. The analyzed gray-scale or colour images are transformed to multi-way arrays which are then recursively approximated by low-rank tensor decomposition models. In our approach, the multi-way array is represented by the tensor train model, and in each iterative step, the low-rank approximation is filtered with the Gaussian low-pass filter. As a result, the proposed algorithms considerably outperform the state-of-the art methods for matrix and tensor completion problems, especially when an incompleteness degree is very high, e.g. with 90% of missing pixels.

关键词： image completion Tensor train decomposition Low-rank approximations

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Digital Implementation of Bio-Inspired Spiking Neuronal networks

Digital Implementation of Bio-Inspired Spiking Neuronal Netw...

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作者： Gomar, Shaghayegh University of Windsor (Canada)

学位级别：Ph.D.

Spiking neural Network as the third generation of artificial neural networks offers a promising solution for future computing, prosthesis, robotic and image processing applications. This thesis introduces digital designs and implementations of building blocks of a Spiking neural networks (SNNs) including neurons, learning rule, and small networks of neurons in the form of a Central Pattern Generator (CPG) which can be used as a module in control part of a bio-inspired robot. The circuits have been developed using Verilog Hardware Description Language (VHDL) and simulated through Modelsim and compiled and synthesised by Altera Qurtus Prime software for FPGA devices. Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapseastrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX ii FPGA which saves up to 4.4% of FPGA resources in comparison to other designs. Learning rule is an essential part of every neural network including SNN. In an SNN, a special type of learning called Spike Timing Dependent Plasticity (STDP) is used to modify the connection strength between the spiking neurons. A pair-based STDP (PSTDP) works on pairs of spikes while a Triplet-based STDP (TSTDP) works on triplets of spikes to modify the synaptic weights. A low cost, accurate, and configurable digital architectures are proposed for PSTDP and TSTDP learning models. The proposed circuits have been compared with the state of the art methods like Lookup Table (LUT), and Pie

关键词： Electrical engineering Robotics

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Deep learning for photoacoustic imaging: a survey

arXiv

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arXiv 2020年

作者： Yang, Changchun Lan, Hengrong Gao, Feng Gao, Fei Hybrid Imaging System Laboratory Shanghai Engineering Research Center of Intelligent Vision and Imaging School of Information Science and Technology ShanghaiTech University Shanghai201210 China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China

— Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Analyzing Digital image by Deep Learning for Melanoma Diagnosis 15th

Analyzing Digital Image by Deep Learning for Melanoma Diagno...

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15th International Work-Conference on artificial neural networks (IWANN)

作者： Thurnhofer-Hemsi, Karl Dominguez, Enrique Univ Malaga Dept Comp Languages & Comp Sci Boulevar Louis Pasteur 35 E-29071 Malaga Spain

ISBN: (纸本)9783030205188;9783030205171

image classification is an important task in many medical applications, in order to achieve an adequate diagnostic of different lesions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important difficulties to overcome in order to obtain a good classification performance. In this work, a deep learning based method for accurate classification of wound regions is proposed. Raw images are fed into a Convolutional neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known effectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean accuracy with very few data and without any preprocessing.

关键词： image processing Deep learning Melanoma

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Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN 23rd

Detecting Micro-expression Intensity Changes from Videos Bas...

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23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Thuseethan, Selvarajah Rajasegarar, Sutharshan Yearwood, John Deakin Univ Geelong Vic 3220 Australia

ISBN: (纸本)9783030161422;9783030161415

Facial micro-expressions, which usually last only for a fraction of a second, are challenging to detect by the human eye or machine. They are useful for understanding the genuine emotional state of a human face, and have various applications in education, medical, surveillance and legal sectors. Existing works on micro-expressions are focused on binary classification of the micro-expressions. However, detecting the micro-expression intensity changes over the spanning time, i.e., the micro-expression profiling, is not addressed in the literature. In this paper, we present a novel deep Convolutional neural Network (CNN) based hybrid framework for micro-expression intensity change detection together with an image pre-processing technique. The two components of our hybrid framework, namely a micro-expression stage classifier, and an intensity estimator, are designed using a 3D and 2D shallow deep CNNs respectively. Moreover, we propose a fusion mechanism to improve the micro-expression intensity classification accuracy. Evaluation using the recent benchmark micro-expression datasets;CASME, CASME ii and SAMM, demonstrates that our hybrid framework can accurately classify the various intensity levels of each micro-expression. Further, comparison with the state-of-the-art methods reveals the superiority of our hybrid approach in classifying the micro-expressions accurately.

关键词： Micro-expression intensity Convolutional neural networks Hybrid framework Fusion mechanism

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Deep Learning Utilization in Beamforming Enhancement for Medical Ultrasound

Deep Learning Utilization in Beamforming Enhancement for Med...

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IEEE Annual International Computer Software and applications Conference (COMPSAC)

作者： Mariam Fouad Yousef Metwally Georg Schmitz Michael Huebner Mohamed A. Abd El Ghany Ruhr University Bochym (RUB) Germany and German University in Cairo (GUC) Egypt German University in Cairo (GUC) Egypt Ruhr University Bochum (RUB) Germany BTU Cottbus - Senftenberg Germany German University in Cairo (GUC) Egypt and TU Darmstadt (TUD) Germany

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

Ultrasound imaging offers a low cost, noninvasive and portable system, which allowed it to be an invaluable tool for medical imaging. However, the quality of the reconstructed images depends significantly on the beamforming technique utilized. Although advanced data-adaptive methods of reconstruction such as Minimum Variance (MV) beamforming can recover image quality much higher than conventional techniques, their implementation also entails a heavy computational burden. This dichotomy hinders the ultrasound imaging use as a standalone device in some applications such as early breast cancer detection. Deep neural networks (DNNs) have shown a huge potential when applied to many artificial intelligence (AI) research fields. In this work, the use of Deep learning in improving the quality of the beamforming technique Delay and Sum (DAS) normally used for ultrasound (US) images reconstruction is explored. Three different architectures are implemented: Convolutional AutoEncoder (CAE), Fully Connected network (FC) and U-Net-like architecture. They were trained on datasets simulated using field ii. The dataset consists of input-output pairs where the input is Noisy DAS beamformed scan lines and the output is MV beamformed non-noisy scan lines. The networks show a great ability in predicting the beamformed signals along with significantly reducing noise in the reconstructed images. Additionally, the proposed networks improve other image characteristics such as scatterer size and position along with reducing tail characteristic normally found in DAS beamformed ultrasound images. US images constructed by the networks achieved better quality metrics that surpass conventional DAS beamformed images. The CAE, U-net-like architecture, and FC enhanced the signal to noise ratio (SNR) compared to DAS by 218%, 165% and 136% respectively. Additionally, the networks showed higher Contrast to Noise Ratio (CNR) and Contrast Ratio (CR) metrics than DAS beamformed signals. Finally, the propo

关键词： Array signal processing Ultrasonic imaging image reconstruction Delays Imaging Computer architecture Covariance matrices

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Compression-Based Clustering of Video Human Activity Using an ASCii Encoding 27th

Compression-Based Clustering of Video Human Activity Using a...

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27th International Conference on artificial neural networks (ICANN)

作者： Sarasa, Guillermo Montero, Aaron Granados, Ana Rodriguez, Francisco B. Univ Autonoma Madrid Escuela Politecn Super Grp Neurocomputac Biol Madrid Spain Univ Complutense Madrid CES Felipe 2 Madrid Spain

ISBN: (纸本)9783030014216

Human Activity Recognition (HAR) from videos is an important area of computer vision research with several applications. There are a wide number of methods to classify video human activities, not without certain disadvantages such as computational cost, dataset specificity or low resistance to noise, among others. In this paper, we propose the use of the Normalized Compression Distance (NCD), as a complementary approach to identify video-based HAR. We have developed a novel ASCii video data format, as a suitable format to apply the NCD in video. For our experiments, we have used the Activities of Daily Living Dataset, to discriminate several human activities performed by different subjects. The experimental results presented in this paper show that the NCD can be used as an alternative to classical analysis of video HAR.

关键词： Data mining Normalized Compression Distance Clustering Dendrogram image processing Human Activity Recognition Silhouette Coefficient Similarity

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A Data-Driven No-Reference image Quality Assessment via Deep Convolutional neural networks 19th

A Data-Driven No-Reference Image Quality Assessment via Deep...

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19th Pacific-Rim Conference on Multimedia (PCM)

作者： Fan, Yezhao Zhu, Yuchen Zhai, Guangtao Wang, Jia Liu, Jing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9783030007676;9783030007669

Nowadays, image quality assessment (IQA) is important for many applications, such as transmission, compression or reproduction. And no-reference image quality assessment (NRIQA) has received extensive attention because there is no need to use original images. Especially in this artificial intelligence and big data age, it is impossible to get the reference images every time. However, many conventional NR-IQA algorithms extracted features by hand and designed only for one specific distortion type. In this paper, we will propose a NR-IQA method to predict image quality accurately with great generalization ability. We first use gaussian kernel regression to do the data augmentation owing to the limited dataset. Then, a NR-IQA model based on convolutional neural network (CNN) is trained, named Deep No-Reference image Quality Assessment (DNRIQA), including five convolutional layers and three pooling layers for feature extraction, and three fully connected layers for regression. Finally, the experiments show that this approach achieves the state-of-the-art performance on LIVE dataset, and further cross dataset experiments prove that the model has excellent generalization ability. Experimental results of this paper is very competitive compared with other algorithms.

关键词： image quality assessment No-Reference image Quality Assessment Gaussian kernel regression Deep NoReference image Quality Assessment

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Deep Multi-structural Shape Analysis: Application to Neuroanatomy 1

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21st International Conference on Medical image Computing and Computer Assisted Intervention (MICCAI)

作者： Gutierrez-Becker, Benjamin Wachinger, Christian LMU Munchen KJP Artificial Intelligence Med Imaging AI Med Munich Germany

ISBN: (数字)9783030009311

ISBN: (纸本)9783030009311;9783030009304

We propose a deep neural network for supervised learning on neuroanatomical shapes. The network directly operates on raw point clouds without the need for mesh processing or the identification of point correspondences, as spatial transformer networks map the data to a canonical space. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. The proposed network consists of multiple branches, so that features for multiple structures are learned simultaneously. We demonstrate the performance of our method on two applications: (i) the prediction of Alzheimer's disease and mild cognitive impairment and (ii) the regression of the brain age. Finally, we visualize the important parts of the anatomy for the prediction by adapting the occlusion method to point clouds.

关键词： Deep neural networks

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A review of deep learning with special emphasis on architectures, applications and recent trends

arXiv

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arXiv 2019年

作者： Sengupta, Saptarshi Basak, Sanchita Saikia, Pallabi Paul, Sayak Tsalavoutis, Vasilios Atiah, Frederick Ditliac Ravi, Vadlamani Peters ii, Richard Alan EECS Department Vanderbilt University United States CSE Department IIT Guwahati India DataCamp Inc India School of Mechanical Engineering Industrial Engineering Laboratory National Technical University of Athens Greece Computer Science Department University of Pretoria Institute for Development and Research in Banking Technology India

Deep learning has solved a problem that as little as five years ago was thought by many to be intractable-the automatic recognition of patterns in data;and it can do so with an accuracy that often surpasses that of human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of deep learning increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular Deep Learning model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer artificial neural networks that comprise deep learning. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology: Anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to use deep learning in their work for what it does best: Statistical pattern recognition with unparalleled learning capacity with the ability to sc

关键词： Deep neural networks

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