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2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)最新文献

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Ultrasound Anomaly Detection Based on Variational Autoencoders 基于变分自编码器的超声异常检测
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552041
Fran Milković, Branimir Filipovic, M. Subašić, T. Petković, S. Lončarić, M. Budimir
{"title":"Ultrasound Anomaly Detection Based on Variational Autoencoders","authors":"Fran Milković, Branimir Filipovic, M. Subašić, T. Petković, S. Lončarić, M. Budimir","doi":"10.1109/ISPA52656.2021.9552041","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552041","url":null,"abstract":"Analysis of ultrasonic testing (UT) data is a time-consuming assignment. In order to make it less demanding we propose an approach based on a variational autoencoder (VAE) to filter out the scans without anomalies/defects and in doing so, partially automate the procedure. The implemented approach uses an additional encoder network allowing to encode the reconstructed images. The differences in encodings of input and reconstructed images have shown to be good indicators of anomalous data. Anomaly detection results surpass the results of other VAE based anomaly criteria.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122932846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation 医学图像分割ML-CSC框架的多模态扩展
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552083
Jens Janssens, Srdan Lazendic, Shaoguang Huang, A. Pižurica
{"title":"Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation","authors":"Jens Janssens, Srdan Lazendic, Shaoguang Huang, A. Pižurica","doi":"10.1109/ISPA52656.2021.9552083","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552083","url":null,"abstract":"In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data model provides a model-based explanation of CNNs. This article aims to extend the ML-CSC framework towards multimodal data processing, which to our knowledge has not been addressed so far. In particular, we focus on interpretable medical image segmentation architecture design for multimodal data. We derive a novel sparse coding algorithm and propose three different CNN architectures with increasing performance, without introducing any additional learnable parameters. Based on the sparse coding theory, our multimodal extension enables the systematic design of interpretable CNN segmentation architectures. Experimental analysis demonstrates that the achieved segmentation results are consistent with the obtained theoretical expectations.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Epsilon Greedy Strategy for Hyper Parameters Tuning of A Neural Network Equalizer 神经网络均衡器超参数整定的Epsilon贪心策略
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552055
Quyet D. Nguyen, Noel Teku, T. Bose
{"title":"Epsilon Greedy Strategy for Hyper Parameters Tuning of A Neural Network Equalizer","authors":"Quyet D. Nguyen, Noel Teku, T. Bose","doi":"10.1109/ISPA52656.2021.9552055","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552055","url":null,"abstract":"In wireless communications, equalization can be used to remove channel impairments from transmissions. Neural networks (NNs) have proven to be an effective technique against conventional equalizers (i.e. decision-feedback, zero-forcing, etc.). High Frequency (HF) channels require high-performance equalizers to overcome Doppler shifts and large delay spreads. When using a NN equalizer, tuning its structure (i.e. activation function, optimizer, etc …) can be time-consuming. This work proposes using an annealing epsilon greedy algorithm, a reinforcement learning technique, to tune the attributes of a neural network equalizer. Reinforcement learning has been used to tune NNs in different applications, but to the best of our knowledge, it has not been done for NN equalization. The objective of this work is to analyze if using reinforcement learning can improve the performance of a NN equalizer.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126080493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification 基于c扫描缺陷分类的材料自动超声检测
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552056
Branimir Filipovic, Fran Milković, M. Subašić, S. Lončarić, T. Petković, M. Budimir
{"title":"Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification","authors":"Branimir Filipovic, Fran Milković, M. Subašić, S. Lončarić, T. Petković, M. Budimir","doi":"10.1109/ISPA52656.2021.9552056","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552056","url":null,"abstract":"The analysis of the data in non-destructive ultrasonic testing of materials is a very time-intensive task. To alleviate the aforementioned strain on the human expert inspectors, a plethora of assisted analysis methods based on deep learning have been developed recently. However, most of these methods are based on the automated detection of flaws in A-scans and B-scans and therefore we propose a method based on the detection of flaws in C-scans that can reduce the complexity of manual detection of flaws in B-scans. The proposed method classifies each row of the C-scan based on whether it contains any flaws or not. Afterward, the positively classified rows are forwarded for further automated (and manual) inspection. The results show that the developed method significantly reduces the number of B-scans that have to be further analyzed.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129866702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A Real-Time Implementation of a 3D Binaural System based on HRIRs Interpolation 基于HRIRs插值的三维双耳系统实时实现
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552125
V. Bruschi, Stefano Nobili, S. Cecchi
{"title":"A Real-Time Implementation of a 3D Binaural System based on HRIRs Interpolation","authors":"V. Bruschi, Stefano Nobili, S. Cecchi","doi":"10.1109/ISPA52656.2021.9552125","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552125","url":null,"abstract":"Binaural synthesis is a very important aspect in the field of immersive audio and it requires the knowledge of the head related impulse responses (HRIRs). This paper describes the real-time implementation of an impulse responses interpolation method that allows to obtain an accurate binaural reproduction reducing measurement sets. The method is based on the time decomposition and frequency division of the HRIRs and the application of a peak detection and matching procedure in combination with an alignment algorithm and a linear interpolation. A 3D set-up has been considered and the algorithm has been evaluated by means of objective and subjective tests, comparing it with the state of the art. The obtained results have demonstrated the excellent performance of the proposed system.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130752127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Synthetic 3D Ultrasonic Scan Generation Using Optical Flow and Generative Adversarial Networks 基于光流和生成对抗网络的合成三维超声扫描生成
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552069
L. Posilović, D. Medak, M. Subašić, T. Petković, M. Budimir, S. Lončarić
{"title":"Synthetic 3D Ultrasonic Scan Generation Using Optical Flow and Generative Adversarial Networks","authors":"L. Posilović, D. Medak, M. Subašić, T. Petković, M. Budimir, S. Lončarić","doi":"10.1109/ISPA52656.2021.9552069","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552069","url":null,"abstract":"Non-destructive ultrasonic analysis of materials is a method for assessing the integrity of the inspected components. It is commonly used in monitoring critical parts of the power plants, in aeronautics, oil and gas, and the automotive industry. Since most ultrasonic inspections rely on expert's previous experience they must constantly practice on new, unseen data. Acquiring enough data for training human experts on non-destructive ultrasonic scan analysis can be an expensive and time-consuming task. The only possibility to get new data for practicing is to implant synthetic defects in real metal blocks. Artificial defects are made by temperature strain, electrical discharge, and physical damage. All of those methods are very complicated and expensive to perform. Also metal blocks have to be taken from the components of the power plants to have the same structure and be realistic. In this work, some attempts have been made to generate 3D ultrasonic scans using computer vision and deep learning methods.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123951416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Real Time Subband Implementation of an Active Noise Control System for Snoring Reduction 一种用于减少打鼾的主动噪声控制系统的实时子带实现
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552038
Stefano Nobili, V. Bruschi, F. Bettarelli, S. Cecchi
{"title":"A Real Time Subband Implementation of an Active Noise Control System for Snoring Reduction","authors":"Stefano Nobili, V. Bruschi, F. Bettarelli, S. Cecchi","doi":"10.1109/ISPA52656.2021.9552038","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552038","url":null,"abstract":"In this paper, a real time implementation of an active noise control (ANC) system for snoring cancellation is presented. This method is based on an online secondary path estimation procedure and includes a subband adaptive filtering (SAF) structure for the primary path estimation. The introduction of the SAF approach allows to improve the performances on the estimation of both primary and secondary path in terms of convergence rate. The algorithm has been compared with the state of the art through several experiments that have proved the effectiveness of the proposed system in real applications.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"829 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116422071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
1-Attempt Subfield-Based Parallel Thinning 尝试基于子域的并行细化
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552163
K. Palágyi, Gábor Németh
{"title":"1-Attempt Subfield-Based Parallel Thinning","authors":"K. Palágyi, Gábor Németh","doi":"10.1109/ISPA52656.2021.9552163","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552163","url":null,"abstract":"A widely used skeletonization technique is thinning, which is an iterative layer-by-layer erosion in a topology preserving way. In the conventional implementation of thinning algorithms, the deletability of all border pixels in the actual picture is to be investigated. That is why we introduced the concept of 1-attempt thinning. In the case of a 1-attempt algorithm, if a border pixel is not deletable in an iteration step, it cannot be deleted in the remaining thinning phases. This paper presents a computationally efficient implementation scheme for 1-attempt 2-subfield parallel thinning; proves that a 2-subfield parallel thinning algorithm (acting on the conventional 2D square grid) is 1-attempt; shows that 1-attempt property is useful and algorithms fulfilling this property can be implemented with a remarkable speed up.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121128107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery SaRNet:基于卫星图像的深度学习辅助搜索和救援数据集
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-07-26 DOI: 10.1109/ISPA52656.2021.9552103
Michael Thoreau, Frazer Wilson
{"title":"SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery","authors":"Michael Thoreau, Frazer Wilson","doi":"10.1109/ISPA52656.2021.9552103","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552103","url":null,"abstract":"Access to high resolution satellite imagery has dramatically increased in recent years as several new constellations have entered service. High revisit frequencies as well as improved resolution has widened the use cases of satellite imagery to areas such as humanitarian relief and even Search and Rescue (SaR). We propose a novel remote sensing object detection dataset for deep learning assisted SaR. This dataset contains only small objects that have been identified as potential targets as part of a live SaR response. We evaluate the application of popular object detection models to this dataset as a baseline to inform further research. We also propose a novel object detection metric, specifically designed to be used in a deep learning assisted SaR setting.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention 肺炎扩展:CNN在通过CXR图像检测肺炎时补偿了人类的错误
2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2020-08-11 DOI: 10.1109/ISPA52656.2021.9552171
Sanskriti Singh
{"title":"PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention","authors":"Sanskriti Singh","doi":"10.1109/ISPA52656.2021.9552171","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552171","url":null,"abstract":"Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic. Pneumonia, a deadly disease, is diagnosed through CXRs and machine learning can accelerate this process. To this end, we present PneumoXttention, an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. The algorithm's architecture consists of an ensemble of two 13-layer convolutional neural networks trained on a dataset provided by the Radiological Society of North America, RSNA, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia annotated by professional radiologists in North America. We validate PneumoXttention with impressive F1 scores on the test set, and against human radiologists on images drawn from RSNA and NIH, and also analyze PneumoXttention's usefulness in practice.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127464725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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