Proceedings of the 2nd International Conference on Control and Computer Vision最新文献_第2页

Early Diagnosis of Alzheimer's Disease Using Deep Learning 利用深度学习早期诊断阿尔茨海默病

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341024

Huanhuan Ji, Zhenbing Liu, W. Yan, R. Klette

{"title":"Early Diagnosis of Alzheimer's Disease Using Deep Learning","authors":"Huanhuan Ji, Zhenbing Liu, W. Yan, R. Klette","doi":"10.1145/3341016.3341024","DOIUrl":"https://doi.org/10.1145/3341016.3341024","url":null,"abstract":"Alzheimer's disease (AD) leads to memory loss and impairment, which may cause further symptoms. It affects lives of patients seriously and is not curable, but early confirmation of AD may be helpful to start proper treatment so as to avoid further brain damage. Over the past decades, machine learning methods have been applied to the classification of AD with results based on manually prepared features and a classifier having a multiple-step architecture. Recently, with the development of deep learning, the end to- end process of neural networks has been employed for pattern classification. In this paper, we focus on early diagnosis of AD based on convolutional neural networks (ConvNets) by using magnetic resonance imaging (MRI). Image slices of gray matter and white matter from MRI have been used as the inputs for classification. Ensemble learning methods have been employed after the convolutional operations for improving the classification by combining outputs of deep learning classifiers [27]. Three base ConvNets were designed, implemented, and compared in this paper. Our method was evaluated based on a dataset from the Alzheimer's Disease Neuroimaging Initiative for the early diagnosis of this illness. In particular, the accuracy rates of our classifications have reached up to 97.65% for AD/mild cognitive impairment and 88.37% for mild cognitive impairment/normal control.","PeriodicalId":278141,"journal":{"name":"Proceedings of the 2nd International Conference on Control and Computer Vision","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129011951","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: 45

Deep Residual Network for Single Image Super-Resolution 单幅图像超分辨率的深度残差网络

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341030

Haimin Wang, K. Liao, B. Yan, Run Ye

引用次数: 5

Using Machine Vision to Command a 6-axis Robot Arm to Act on a Randomly Placed Zinc Die Cast Product 用机器视觉控制六轴机械臂对随机放置的锌压铸产品的动作

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341018

Luke Butters, Zezhong Xu, R. Klette

引用次数: 9

Design Therapy for Post-Stroke Patients with Robotics Tools and Principles of Mirror Neurons Using qEEG Parameter Analysis 基于qEEG参数分析的镜像神经元原理和机器人工具设计脑卒中后患者的治疗

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341027

R. Setiawan, Odilia Valentine, H. Zakaria

{"title":"Design Therapy for Post-Stroke Patients with Robotics Tools and Principles of Mirror Neurons Using qEEG Parameter Analysis","authors":"R. Setiawan, Odilia Valentine, H. Zakaria","doi":"10.1145/3341016.3341027","DOIUrl":"https://doi.org/10.1145/3341016.3341027","url":null,"abstract":"Stroke causes neurological disorders such as reduced muscle motor skills, as well as cognitive, visual, and coordination functions, significantly. The reduced level of independence and mobility of a person can affect their quality of life. So, with rehabilitation program, it is expected that motor skills and cognitive function of stroke patients can be restored. This study focused on designing an integrated therapeutic device by using stationary cycles with the principle of mirror neurons and equipped with qEEG signal analysis for parameters comparison before and after therapy. Channels EEG used are F3, F4, C3, Cz, C4, P3, Pz, and P4 according to the rules of localization 10-20 Ref. The device also designed as a robotic system controlled by the movement intentions detected from EEG signals before the actual movement occurs. The Event-Related Potential (ERP) phenomenon mu waves were used to recognize the movement intentions. The device has been tested on normal subjects. To train the movement intention algorithm, the subject were asked to cycle and stop at prescribed time by audio command. qEEG parameters were displayed through a monitor for realtime observation by physical therapist. This research is part of the design process of the stroke therapy system which will then be tested in several stroke patients. Therapy programs for post-stroke patients using this device are expected to increase mobility of the affected limb by stroke.","PeriodicalId":278141,"journal":{"name":"Proceedings of the 2nd International Conference on Control and Computer Vision","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435715","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

Video Object Segmentation with 3D Convolution Network 基于三维卷积网络的视频对象分割

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341031

Huiyun Tang, Pin Tao, Rui Ma, Yuanchun Shi

{"title":"Video Object Segmentation with 3D Convolution Network","authors":"Huiyun Tang, Pin Tao, Rui Ma, Yuanchun Shi","doi":"10.1145/3341016.3341031","DOIUrl":"https://doi.org/10.1145/3341016.3341031","url":null,"abstract":"We explore a novel method to realize semi-supervised video object segmentation with special spatiotemporal feature extracting structure. Considering 3-dimension convolution network can convolute a volume of image sequence, it is a distinct way to get both spatial and temporal information. Our network is composed of three parts, the visual module, the motion module and the decoder module. The visual module learns object appearance feature from object in the first frame for network to detect specific object in following image sequences. The motion module aims to get spatiotemporal information of image sequences with 3-dimension convolution network, which learns diversities of object temporal appearance and location. The purpose of decoder module is to get foreground object mask from output of visual module and motion module with concatenation and upsampling structure. We evaluate our model on DAVIS segmentation dataset[15]. Our model doesn't need online training compared with most detection-based methods because of visual module. As a result, it takes 0.14 second per frame to get mask which is 71 times faster than the state-of-art method-OSVOS[2]. Our model also shows better performance than most methods proposed in recent years and its meanIOU accuracy is comparable with state-of-art methods.","PeriodicalId":278141,"journal":{"name":"Proceedings of the 2nd International Conference on Control and Computer Vision","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561012","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

Recognizing Faces in Shades of Gray 识别灰色阴影中的面孔

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341029

Alaa E. Abdel-Hakim, M. El-Melegy, Shreen K. Refaay

引用次数: 0

Multi-focus Color Image Fusion using Laplacian Filter and Discrete Fourier Transformation with Qualitative Error Image Metrics 基于拉普拉斯滤波和离散傅立叶变换的多焦点彩色图像融合与定性误差图像度量

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341019

S. Khan, Muzammil Khan, Qiong Ran

{"title":"Multi-focus Color Image Fusion using Laplacian Filter and Discrete Fourier Transformation with Qualitative Error Image Metrics","authors":"S. Khan, Muzammil Khan, Qiong Ran","doi":"10.1145/3341016.3341019","DOIUrl":"https://doi.org/10.1145/3341016.3341019","url":null,"abstract":"The Multi-focus image is limited Depth-of-Field (DOF) of an imaging system causes blur images when the sample is wider than the DOF of the optical system. Color multi-focus image fusion allows merging two multi-focus images and produces a composite image integrating complementary information to better understand the entire scene. This paper introduced a new two-level approach for color image fusion. In the first level, the multi-focus image is enhanced by Laplacian Filter (LF) technique with Discrete Fourier Transform (DFT). In the second level, the enhanced images are further processed by Stationary Wavelet Transform (SWT) and construct a more informative fused image. The experimental results on the color multi-focused images showed the capabilities and improved results of the proposed approach as compared with the traditional SWT method. The output image is evaluated using both a qualitative and the quantitative approach. The standard deviation, mean and entropy quantitative metrics are used to assess the performance of the proposed method. The article also introduced a new type of qualitative performance metric named as \"Qualitative Error Image (QEI)\" to evaluate the proposed method and assess future evaluation.","PeriodicalId":278141,"journal":{"name":"Proceedings of the 2nd International Conference on Control and Computer Vision","volume":"333 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335450","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

Proposal of Environment Adaptive Software 环境自适应软件的构想

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341035

Y. Yamato, Hirofumi Noguchi, M. Kataoka, Takuma Isoda

引用次数: 0

Chinese Rubbing Image Binarization based on Deep Learning for Image Denoising 基于深度学习的中文摩擦图像二值化去噪

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341023

Zhi-Kai Huang, Zhen-Ning Wang, Jun-Mei Xi, Ling-Ying Hou

引用次数: 3

Railway Signal Interlocking Logic Simulation System 铁路信号联锁逻辑仿真系统

Proceedings of the 2nd International Conference on Control and Computer Vision Pub Date : 2019-06-15 DOI: 10.1145/3341016.3341017

Lidong Zhang

引用次数: 0