2021 29th Signal Processing and Communications Applications Conference (SIU)最新文献_第6页

Comparison of the State-of-the-Art Image and Video Quality Assessment Metrics 最先进的图像和视频质量评估指标的比较

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477856

Emre Akkaya, Nükhet Özbek

引用次数: 0

Few-Shot Learning for Segmentation of Yeast Cell Microscopy Images 酵母细胞显微图像分割的少镜头学习

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477988

Muhammet Alkan, Berna Kiraz, Furkan Eren, Y. Uysalli, A. Kiraz

{"title":"Few-Shot Learning for Segmentation of Yeast Cell Microscopy Images","authors":"Muhammet Alkan, Berna Kiraz, Furkan Eren, Y. Uysalli, A. Kiraz","doi":"10.1109/SIU53274.2021.9477988","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477988","url":null,"abstract":"Cell segmentation from microscopic images can be performed using deep neural networks or image processing techniques. In addition to their inherent difficulties, these techniques come together with the requirement of feeding the neural network with a large number of image samples in order to obtain a good result. However, this is not sustainable in terms of collecting and labeling microscopic images and represents a costly and timeconsuming solution for every new microscopic image and cell type. Instead, fine-tuning can be employed by taking advantage of the adaptation ability of a model trained using meta-learning algorithms. In this way, while more general and better results can be obtained with fewer samples, the training process does not start from scratch for each new cell type or data set. In this article, microscopic images of yeast cells were recorded and analyzed using Reptile algorithm. After fine-tuning with a small number of samples, an average success rate of 81% IoU (Intersection over Union) was obtained on the test pictures in addition to the model accuracy reaching up to 87%.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131193853","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

LSTM Based Classification of Targets using FMCW Radar Signals 基于LSTM的FMCW雷达信号目标分类

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477927

Oytun Günes, Ö. Morgül

引用次数: 1

SAR Image Reconstruction from 1-Bit Quantized Data with Sign-Flip Errors 带有符号翻转错误的1位量化数据SAR图像重建

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477857

Mehmet Demir

引用次数: 0

Multi-Image Reconstruction in Multi-Contrast MRI 多对比MRI中的多图像重建

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477799

Muzaffer Özbey, T. Çukur

{"title":"Multi-Image Reconstruction in Multi-Contrast MRI","authors":"Muzaffer Özbey, T. Çukur","doi":"10.1109/SIU53274.2021.9477799","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477799","url":null,"abstract":"The acquisition of multiple contrast magnetic resonance images (MRI) has an important role in clinical diagnosis by increasing diagnostic knowledge. Long scanning durations, in which the patient must remain immobile, limits the acquisition of multiple contrast MRIs. Scanning times can be reduced by undersampled acquisitions and reconstruction of undersampled images. Common methods produce a fully sampled MR image of a single contrast from undersampled MR image of the same contrast. However, limited prior information of a single contrast MR image in the input data limits the reconstruction performance. Hence, reconstruction performance can be increased with the use of multiple contrast MRI as input data. In this study, a multi-contrast MRI reconstruction method is proposed which simultaneously produces fully sampled images from undersampled images of more than one contrast. This method is applied using generative adversarial network that produce highly realistic images by better recovering the high frequency details. The proposed method has been tested on a dataset containing multiple contrast brain MR images, and it has been demonstrated that it provides superior performance compared to the alternative single contrast reconstruction method as a result of numerical and visual evaluations.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126979423","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

Diet Application That Can Recognize Turkish Foods With Deep Learning 饮食应用程序，可以识别土耳其食品与深度学习

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477954

İlayda Sahin, Berna Taşel, M. A. Güvensan

{"title":"Diet Application That Can Recognize Turkish Foods With Deep Learning","authors":"İlayda Sahin, Berna Taşel, M. A. Güvensan","doi":"10.1109/SIU53274.2021.9477954","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477954","url":null,"abstract":"The problem of manually entering the meals one by one, which is one of the main problems in many traditional diet applications, was solved with the help of the proposed deep learning models integrated into the mobile application. Two different models resulting from the deep learning study were used in the application. The first model detects the food in the environment with real-time object detection and the second one recognizes the type of the food in the detected plate. A data set containing 102 different types of food belonging to Turkish cuisine and approximately 500 photographs of each type of food was collected and used in the training of the second model. The proposed TurkishFoodNet network with the number of three, five, seven, nine, eleven and thirteen layers were examined in the training of both models. Apart from this network, training operation were also held with Tensorflow Lite Image Classifier and MobilNetV2. According to the test results, Tensorflow Lite Image Classifier and TurkishFoodNet_L11 gives the highest accuracy for both detecting food and recognizing the type of food with an accuracy of 93% and 84%, respectively.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127775524","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

Gaussian Filter Based Digital Detail Enhancement in Thermal Images 基于高斯滤波的热图像数字细节增强

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477966

Hilal Bozkurt, A. Çelebi

{"title":"Gaussian Filter Based Digital Detail Enhancement in Thermal Images","authors":"Hilal Bozkurt, A. Çelebi","doi":"10.1109/SIU53274.2021.9477966","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477966","url":null,"abstract":"Thermal images have low contrast due to thermal radiation dispersion in the scene. It is generally difficult to separate objects from the background in these images. Thus, ,in IR image processing, it is a critical task to increase the details in the scene (Digital Detail Enhancement-DDE) and to reduce the noise, sharpening the edges and increasing the contrast in order to distinguish the targets in the scene properly. In this study, a suitable approach is proposed for improving thermal images by increasing the contrast and revealing details, low processing load and suitable for the hardware. In the proposed approach, thermal image is divided into low frequency and high frequency components using Gauss filter. Firstly, the adaptive double plateau histogram equalization method is applied to the low frequency component, followed by the Gamma Transform. In order to better reveal the details of the high frequency component, details were revealed by applying stepped gain. Then the images that hold the improved low frequencies and high frequencies are combined. In the final stage, noise removal is performed using Wiener Filter. When the experimental results are examined, it is shown that the proposed method performs well compared to other methods.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798618","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

Fair Data Collection in Wireless Networks 无线网络中的公平数据采集

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477894

Ömer Melih Gül

引用次数: 1

Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning 基于变分自编码器的深度贝叶斯主动学习特征降维

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477979

Pınar Ezgi Çöl, S. Ertekin

引用次数: 1

Comparative Analysis of Word Vectoring Methods for Malware Detection 用于恶意软件检测的词向量方法比较分析

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9478055

Ayşe Bilge Gündüz, A. Yavuz

引用次数: 0