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

Parameterized Extraction of Tiles in Multilevel Gigapixel Images 几亿像素图像中的参数化贴图提取

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552104

Rune Wetteland, K. Engan, Trygve Eftesol

{"title":"Parameterized Extraction of Tiles in Multilevel Gigapixel Images","authors":"Rune Wetteland, K. Engan, Trygve Eftesol","doi":"10.1109/ISPA52656.2021.9552104","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552104","url":null,"abstract":"In many image domains using multilevel gigapixel images, each image level may reveal different information. E.g., a histological image will reveal specific diagnostic information at different resolutions. By incorporating all levels in deep learning models, the accuracy can be improved. It is necessary to extract tiles from the image since it is intractable to process an entire gigapixel image at full resolution at once. Therefore, a sound method for finding and extracting tiles from multiple levels is essential both during training and prediction. In this paper, we have presented a method to parameterize and automate the task of extracting tiles from different scales with a region of interest (ROI) defined at one of the scales. The proposed method makes it easy to extract different datasets from the same group of gigapixel images with different choices of parameters, and it is reproducible and easy to describe by reporting the parameters. The method is suitable for many image domains and is demonstrated here with different parameter settings using histological images from urinary bladder cancer. An efficient implementation of the method is openly provided via Github.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"68 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":"126085261","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: 4

Scaled-Magnitude Multi-Channel Correlation Filters for Multimodal Biometric Recognition 多模态生物特征识别的尺度大小多通道相关滤波器

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552117

C. NarendraK., Sanjeev Gurugopinath, R. Kumaraswamy

{"title":"Scaled-Magnitude Multi-Channel Correlation Filters for Multimodal Biometric Recognition","authors":"C. NarendraK., Sanjeev Gurugopinath, R. Kumaraswamy","doi":"10.1109/ISPA52656.2021.9552117","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552117","url":null,"abstract":"We propose a novel variant of the multi-channel correlation filters (MCCF), namely the scaled-magnitude MCCF (SM-MCCF). The SM-MCCF is characterized by a scaling factor on the magnitude response, which has phase-only spectrum and conventional magnitude and phase spectra as the corner cases. We show that the SM-MCCF design technique, when applied to a multimodal biometric authentication system based on face and handwritten signature recognition, outperforms the conventional MCCF and SVM classifiers under low SNR conditions. Furthermore, the utility of the SM-MCCF is also explored for multimodal fusion with image features for face and handwritten signatures with i-vectors for speech data. Our experimental results indicate that SM-MCCF provides a reasonable improvement in performance, in terms of the EER and recognition rate, as opposed to the MCCF in both moderately and severely degraded scenarios. Moreover, we also demonstrate that the feature level fusion is advantageous than score fusion as the level of abstraction in feature representation is lesser when compared to score level representations.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"54 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":"129920831","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

[Copyright notice] (版权)

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ispa52656.2021.9552049

引用次数: 0

Linearization Approach for Multi-Scale Digital Polynomial Curve Segmentation 多尺度数字多项式曲线分割的线性化方法

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552153

Gaëlle Skapin, R. Zrour, Eric Andres

引用次数: 0

CoviSegNet - Covid-19 Disease Area Segmentation using Machine Learning Analyses for Lung Imaging CoviSegNet -使用机器学习分析进行肺部成像的Covid-19疾病区域分割

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552078

Bhuvan Mittal, IungHwan Oh

{"title":"CoviSegNet - Covid-19 Disease Area Segmentation using Machine Learning Analyses for Lung Imaging","authors":"Bhuvan Mittal, IungHwan Oh","doi":"10.1109/ISPA52656.2021.9552078","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552078","url":null,"abstract":"The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus - 2 (SARS-CoV-2). Over 175 million cases and 3.8 million deaths were reported worldwide as of June 2021. Covid-19 disease induces lung changes observed in lung Computerized Tomography (CT) predominantly as ground-glass opacification (GGO) with occasional consolidation in the peripheries. It was revealed in some literature that 88% of Covid-19 positive patients' CT scans showed GGO and 32% showed consolidation. Moreover, it was reported that the percentage of the lung showing GGO, and consolidation is tied to disease severity. Thus, segmentation of ground-glass opacities and consolidations in CT images will help to quantify disease severity and assist physicians in disease triage, management, and prognosis. In this paper, we propose CoviSegNet, an enhanced U-Net model to segment these ground-glass opacities and consolidations. The performance of CoviSegNet was evaluated on three public CT datasets. The experimental results show that the proposed CoviSegNet is highly promising.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"27 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":"125697995","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

An Accurate and Efficient Zero-Crossing Line Classifier for Multiscale Parameter Estimation of Gaussian Signals Subject to Noise 一种精确高效的高斯信号多尺度参数估计的过零线分类器

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552119

Robert L. Leeker, Nicolai Spicher, M. Kukuk

{"title":"An Accurate and Efficient Zero-Crossing Line Classifier for Multiscale Parameter Estimation of Gaussian Signals Subject to Noise","authors":"Robert L. Leeker, Nicolai Spicher, M. Kukuk","doi":"10.1109/ISPA52656.2021.9552119","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552119","url":null,"abstract":"The multiscale parameter estimation framework is a method for estimating the true parameters of signals subject to noise. The method is based on detecting lines of zero-crossings within the Continuous Wavelet Transform and substituting their locations in time into analytical equations directly expressing the unknown signal parameters. Evidently, this approach depends on selecting the correct lines, corresponding to the signal of interest and not to other phenomena related to noise. This task can be posed as the binary classification problem of determining for each zero-crossing line found whether or not it should be used for parameter estimation. It has been shown that even for very high noise levels, a correct classification leads to very accurate estimates, while a wrong classification results in highly inaccurate estimates. Therefore, with this particular approach the classification of zero-crossing lines poses the limiting factor to the accuracy of the estimated parameters. In this work, we propose a novel, efficient and more robust classifier called “stencil operator” which accurately detects the best combination of zero-crossing lines of Gaussian input signals. We evaluate the performance of this new classifier using synthetic Gaussian signals subject to white (Gaussian) noise with signal-to-noise ratios ranging from 50 dB to - 20 dB. By studying the error between estimated and ground truth parameters, we show that the new classifier outperforms the current method for all noise levels considered and for a noise level of e.g. -12 dB improves the median error from 132% to 28%. The proposed classifier pushes the boundary for analyzing heavily disturbed signals using multiscale parameter estimation to a new level.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"7 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":"127909836","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

The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning 袋内采样对端到端多实例学习的影响

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552170

N. Koriakina, Natasa Sladoje, Joakim Lindblad

{"title":"The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning","authors":"N. Koriakina, Natasa Sladoje, Joakim Lindblad","doi":"10.1109/ISPA52656.2021.9552170","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552170","url":null,"abstract":"End-to-end multiple instance learning (MIL) is an important concept with a wide range of applications. It is gaining increased popularity in the (bio)medical imaging community since it may provide a possibility to, while relying only on weak labels assigned to large regions, obtain more fine-grained information. However, processing very large bags in end-to-end MIL is problematic due to computer memory constraints. We propose within-bag sampling as one way of applying end-to-end MIL methods on very large data. We explore how different levels of sampling affect the performance of a well-known high-performing end-to-end attention-based MIL method, to understand the conditions when sampling can be utilized. We compose two new datasets tailored for the purpose of the study, and propose a strategy for sampling during MIL inference to arrive at reliable bag labels as well as instance level attention weights. We perform experiments without and with different levels of sampling, on the two publicly available datasets, and for a range of learning settings. We observe that in most situations the proposed bag-level sampling can be applied to end-to-end MIL without performance loss, supporting its confident usage to enable end-to-end MIL also in scenarios with very large bags. We share the code as open source at https://github.com/MIDA-group/SampledABMIL","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"18 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":"132006057","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

Speech Intelligibility Enhancement using an Optimal Formant Shifting Approach 使用最优峰移方法增强语音清晰度

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552080

K. Nathwani, Faizal M. F. Hafiz, A. Swain, R. Biswas

{"title":"Speech Intelligibility Enhancement using an Optimal Formant Shifting Approach","authors":"K. Nathwani, Faizal M. F. Hafiz, A. Swain, R. Biswas","doi":"10.1109/ISPA52656.2021.9552080","DOIUrl":"https://doi.org/10.1109/ISPA52656.2021.9552080","url":null,"abstract":"The present study proposes a novel delta function-based optimal shift in formants for enhancing the near-end speech intelligibility. The delta function being used here is trapezoidal in shape. The shaping parameters of this delta function are determined using comprehensive learning particle swarm optimization (CLPSO) which maximizes the short time objective intelligibility (STOI) of speech sequences. The proposed method does not require the knowledge of noise statistics in designing the delta function. Further, the proposed method does not require post-processing in terms of the computation of smoothing of the shifted formants. The performance of the proposed method is illustrated using speech signals from the Hearing In Noise Test (HINT) French database by including the engine noise from a car running at 130 km/h. The results of the investigation, at various SNRs, convincingly demonstrate that the optimal delta function (function with the optimized parameters) could significantly improve the speech intelligibility at very low SNRs while preserving the quality and naturalness of the sound.","PeriodicalId":131088,"journal":{"name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","volume":"353-358 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":"116329156","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

Embedding Naka-Rushton's equation in the geometric setting of Möbius transformations 将Naka-Rushton方程嵌入Möbius变换的几何设置中

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552109

Nicoletta Prencipe, E. Provenzi

引用次数: 0

A Combinatorial Coordinate System for the Vertices in the Octagonal $C_{4}C_{8}(S)$ Grid 八角形$C_{4}C_{8}(S)$网格中顶点的组合坐标系

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) Pub Date : 2021-09-13 DOI: 10.1109/ISPA52656.2021.9552147

Lidija Comic

引用次数: 1