2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)最新文献_第2页

Classification of metabolic syndrome subjects and marathon runners with the k-means algorithm using heart rate variability features 基于心率变异性特征的k-means算法对代谢综合征受试者和马拉松运动员进行分类

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743352

Gilberto Perpiñan, E. Severeyn, M. Altuve, Sara Wong

{"title":"Classification of metabolic syndrome subjects and marathon runners with the k-means algorithm using heart rate variability features","authors":"Gilberto Perpiñan, E. Severeyn, M. Altuve, Sara Wong","doi":"10.1109/STSIVA.2016.7743352","DOIUrl":"https://doi.org/10.1109/STSIVA.2016.7743352","url":null,"abstract":"In this paper, we have applied the k-means clustering algorithm to classify three study groups (people with metabolic syndrome, marathon runners, and sedentary subjects) that underwent a 5-sample 2-hour oral glucose tolerance test (OGTT). For this purpose, time-domain, frequency-domain and non-linear parameters of the heart rate variability (HRV), extracted from ECG recordings acquired at five different instants of the OGTT, were used as unidimensional observations to the k-means algorithm. Specifically, standard deviation of RR intervals (SDNN), root-mean-square differences of successive RR intervals (RMSSD), frequency power in the low frequency (LF) and high-frequency (HF) bands, LF/HF ratio, Poincaré descriptors SD1 and SD2, fractal scaling exponents α1 and α2, and approximate entropy were used as observations. Experiments were carried out with k = 2 and k = 3 clusters and using the squared Euclidean and Cityblock distances. Results showed that the Cityblock distance outperformed the squared Euclidean distance for this kind of observations. In addition, the parameter SDNN at the end of the OGTT gave the best classification performance (69.2%). Parameters SDNN, RMSSD, SD1 and SD2 at fast and at 30 min of the test differentiated subjects with metabolic syndrome with classification a performance greater than 60%.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132104054","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

Weed detection in rice fields using aerial images and neural networks 利用航拍图像和神经网络进行稻田杂草检测

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743317

Oscar Barrero, D. Rojas, C. Gonzalez, Sammy A. Perdomo

引用次数: 42

Supervised learning models for control quality by using color descriptors: A study case 使用颜色描述符控制质量的监督学习模型:一个研究案例

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743368

Arley Bejarano Martinez, A. F. Calvo, Carlos Alberto Henao

{"title":"Supervised learning models for control quality by using color descriptors: A study case","authors":"Arley Bejarano Martinez, A. F. Calvo, Carlos Alberto Henao","doi":"10.1109/STSIVA.2016.7743368","DOIUrl":"https://doi.org/10.1109/STSIVA.2016.7743368","url":null,"abstract":"This paper presents a study case for color inspection in quality control applications using color descriptors histogram RGB-1D and histogram TSL and supervised machine learning methods such Support Vector Machine (SVM) and Artificial Neural Networks (ANN). For this, we build three annotated databases, and these are made using real application of quality control like color inspection in forages and polarized level from vehicle glasses. These bases are captured with a Samsung Galaxy S5 mini camera, which has a resolution of 800×480 pixels. Each class has fifty images under uncontrolled conditions of noise and lighting. The first databases consists of living colors required for Wood fodder with texture. For the third one, it takes glasses with different level of polarized. To calculate the learning methods performance, we use a cross-validation method, which fractionates the data (70% for training and 30% for validation). In the ANN test setup, we use a Backpropagation algorithm. For the SVM case, we take a multi-class setup with Gaussian Radial Kernel (RBF) that uses an adaptative radio with classification strategy “one-vs-all”. Finally, it is reported the accuracy average for each class and its standard deviation.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116073199","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

Motion monitor to measure progress of rehabilitation in people with conditions motor hand 运动监测器用于测量手部运动障碍患者的康复进展

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743322

B. Puello, Edwin Santamaria, F. Van, R. Díaz

引用次数: 2

Human features extraction by using anatomical and low level image descriptors from whole body images 利用解剖和低级图像描述符从全身图像中提取人体特征

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743308

Nicolás Múnera, C. Alvarez, Sebastian Sastoque, M. Iregui

{"title":"Human features extraction by using anatomical and low level image descriptors from whole body images","authors":"Nicolás Múnera, C. Alvarez, Sebastian Sastoque, M. Iregui","doi":"10.1109/STSIVA.2016.7743308","DOIUrl":"https://doi.org/10.1109/STSIVA.2016.7743308","url":null,"abstract":"Interaction experience in multimedia systems can be improved by adding personalization. Current applications for building and animating characters to represent real users are typically based on pose and motion detection. For so doing, computer vision algorithms do not exploit the anatomical characteristics of the human body for improving their classification accuracy. This work presents an strategy that considers age-group, body shape and height estimation by using anatomical low level descriptors. The proposed strategy allows to differentiate children from adults, and under-weighted and normal body shaped from over-weighted individuals, based on a set of features extracted from full body images and a classification process based on Support Vector Machine (SVM). These classification models were evaluated using a 10-fold cross validation, obtaining an area under the ROC curve of 89 % and 92 % respectively for age-group and body shape. On the other hand, the height of a person was computed by using a reference image in a leave-one-out evaluation and, in comparison with the real one, an square error (MSE) of 17cm was obtained.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315268","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

On the compensation of uneven illumination in retinal images for restoration by means of blind deconvolution 用盲反卷积法补偿视网膜图像中光照不均匀的恢复

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743327

A. Marrugo, Raul Vargas, S. Contreras, M. S. Millán

引用次数: 1

The impact of MOOCs on the performance of undergraduate students in digital signal processing mooc对大学生数字信号处理成绩的影响

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743356

S. Pertuz, J. Torres

{"title":"The impact of MOOCs on the performance of undergraduate students in digital signal processing","authors":"S. Pertuz, J. Torres","doi":"10.1109/STSIVA.2016.7743356","DOIUrl":"https://doi.org/10.1109/STSIVA.2016.7743356","url":null,"abstract":"Signal Processing is a core subject in the curriculum of Electronics Engineering at Universidad Industrial de Santander. Poor student performance and low completion rates are an important concern and professors devote great efforts to devise new teaching strategies. In recent years, massive open online courses (MOOC) have emerged as a disruptive form of online learning with attractive features for higher education. However, despite the attention that they have received by the community, the role that MOOCs will play in the scope of higher education is yet to be determined. In particular, previous research has not conclusively shown whether these courses have a positive impact on the performance of students. This paper proposes a methodology in order to assess the impact of MOOCs and their potential to improve the performance of undergraduate students attending a regular class on Digital Signal Processing (DSP). This work first addresses the challenges in the design of materials for MOOCs on DSP. Subsequently, a case-control study is carried out where the study group takes a regular class simultaneously with a MOOC on the subject. The control and study groups are compared in terms of student's performance, as measured by their grades in theoretical exams. Obtained results show a statistically significant improvement on the performance of students of the study group over the control group.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129912715","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

Localized X-ray compressive computer tomography reconstruction by designing measurement matrix 设计测量矩阵的局部x射线压缩计算机断层扫描重建

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743316

Lizeth Lopez, Óscar Espitia, H. Arguello

{"title":"Localized X-ray compressive computer tomography reconstruction by designing measurement matrix","authors":"Lizeth Lopez, Óscar Espitia, H. Arguello","doi":"10.1109/STSIVA.2016.7743316","DOIUrl":"https://doi.org/10.1109/STSIVA.2016.7743316","url":null,"abstract":"X-ray computed tomography (CT) is a noninvasive process for acquiring 3D images from the internal structure of an object. Traditionally, the number of samples needed to recover images from X-ray projections is due to the Nyquist criteria. Recently, a sampling protocol based on compressive sampling (CS) theory has been proposed for reducing the number of required samples. The compressive CT system measures coded projections by using coded apertures that can be adjusted to increase the quality of the retrieved information. In areas such as medicine, geology, and industry, there are applications where it is important the high resolution in only specific parts of the scene, and the additional information is ignored. The compressive CT system allows taking more compressive information of some part of the scene by designing the sensing matrix. This work formulates a localized reconstruction approach in compressive CT by downsampling the non-interest regions selectively and designing the coded apertures for ensuring a uniform sampling for the regions of interest. This process decreases the number of samples required to reconstruct all the data with a high resolution and to preserve a high quality only in the regions of interest. Simulation results, of real and synthetic data, show that the reconstruction algorithms based on CS theory allow the CT images reconstruction for selectively subsampled data and the regions of interest reconstructions have comparable quality to traditional results without selectivity.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281682","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

Face deformation system based on active shape models ASM 基于主动形状模型的人脸变形系统

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743330

F. C. H. Esparza, S. J. P. Tinoco, R. C. A. Pabon

引用次数: 1

Landsat and MODIS satellite image processing for solar irradiance estimation in the department of Narino-Colombia 哥伦比亚纳里诺省用于估算太阳辐照度的陆地卫星和MODIS卫星图像处理

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) Pub Date : 2016-08-01 DOI: 10.1109/STSIVA.2016.7743306

Omar Cabrera, Bayron Champutiz, Andres Calderon, A. Pantoja

引用次数: 3