2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)最新文献_第2页

[Title page i] [标题页i]

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/synasc.2018.00001

引用次数: 0

Ordinary Differential Equations & Computability 常微分方程与可计算性

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00011

Olivier Bournez

引用次数: 0

Unsupervised and Fully Autonomous 3D Medical Image Segmentation Based on Grow Cut 基于Grow Cut的无监督和全自动3D医学图像分割

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00068

Alexandru-Ion Marinescu, Z. Bálint, L. Dioşan, A. Andreica

{"title":"Unsupervised and Fully Autonomous 3D Medical Image Segmentation Based on Grow Cut","authors":"Alexandru-Ion Marinescu, Z. Bálint, L. Dioşan, A. Andreica","doi":"10.1109/SYNASC.2018.00068","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00068","url":null,"abstract":"Extending and optimizing cellular automata to handle 3D volume segmentation is a non-trivial task. First, it does not suffice to simply alter the cell neighborhood (be it von Neumann or Moore), and second, going from 2D to 3D means that the number of operations increases by an order of magnitude, thus GPU acceleration becomes a necessity, advantage inherent to cellular automata approaches. When discussing 3D medical imagistics, we mean that the entire stack of slices from a certain sequence within an acquisition is stored as a single entity. This, in turn, enables us to accurately segment whole volumes in a single run, which would otherwise need per-slice segmentation followed by a stitching post-process. This paper focuses mainly on a thorough benchmark analysis of the 3D Unsupervised Grow Cut technique. We discuss algorithm speed of convergence, stability and behavior with respect to global meta-parameters such as segmentation threshold, keeping track of output quality metrics as the algorithm unfolds. Our end goal is to segment the heart cavities from cardiac MRI and to yield an interactive 3D reconstruction which can be easily handled and analyzed by the radiologist.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261982","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

[Publisher's information] (发布者的信息)

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/synasc.2018.00080

引用次数: 0

An Improved Approach to Software Defect Prediction using a Hybrid Machine Learning Model 基于混合机器学习模型的软件缺陷预测改进方法

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00074

Diana-Lucia Miholca

引用次数: 5

Estimation of Prediction Intervals in Neural Network-Based Regression Models 基于神经网络的回归模型预测区间的估计

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00078

Kristian Miok

引用次数: 9

[Copyright notice] (版权)

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/synasc.2018.00003

引用次数: 0

Face Detection and Recognition Methods using Deep Learning in Autonomous Driving 基于深度学习的自动驾驶人脸检测与识别方法

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00060

Sebastian-Aurelian Ștefănigă, Mihail Gaianu

{"title":"Face Detection and Recognition Methods using Deep Learning in Autonomous Driving","authors":"Sebastian-Aurelian Ștefănigă, Mihail Gaianu","doi":"10.1109/SYNASC.2018.00060","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00060","url":null,"abstract":"One of the objectives of deep learning is to solve high complex tasks such as perception. In recent years, it has been demonstrated that deep learning can overcome traditional algorithms in image classification as well as object recognition and face recognition tasks. In this paper we are inspecting techniques of deep learning that deals with topical issues in the field of Computer Vision: real-time face detection and face recognition using embedded system and GPU processing on NVidia Tegra X2 (Jetson TX2). In the first part of our work we are proposing a novel experimental research to the problem of face detection and recognition in autonomous driving that use a new deep convolutional neural network model, named FADNet. The architecture model was used on a existing dataset containing more then 13.000 images of 2.000 different faces from different cultures, on which we gained an accuracy of 81.78%, along with an accuracy of 84.45% on a detection dataset containing new 8.600 images. In the final phase of the experimental research we did a real-time test on a dataset of self-acquired video frames from Jetson TX2 embedded system camera, achieving an accuracy of 67.45%, which is a promising result for real-time processing. Last but not least, accuracy and inference time are taken into account by comparing time performance between CPU and GPU implementations.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"125 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120841447","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

Order Relations Over Finitely Supported Structures 有限支撑结构上的序关系

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00030

A. Alexandru, Gabriel Ciobanu

引用次数: 0

Optimizing Cleanset Growth by Using Multi-Class Neural Networks 利用多类神经网络优化Cleanset增长

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI: 10.1109/SYNASC.2018.00071

Adrian Ioan Pîrîu, M. Leonte, Nicolae Postolachi, Dragos Gavrilut

引用次数: 2