Proceedings of the Sixteenth Mexican International Conference on Computer Science最新文献

An Agent Based Framework for Multi-Sink Wireless Sensor Networks 一种基于Agent的多sink无线传感器网络框架

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235.3149240

Miriam A. Carlos-Mancilla, E. López-Mellado, Mario Siller

引用次数: 1

Designing Visualization Tools to Support Older Adults Care Process 设计可视化工具以支持老年人护理过程

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235.3149237

Amanda Tapia, Jessica Beltrán-Márquez, Valeria Soto-Mendoza, Karina Caro

引用次数: 1

Proceedings of the Sixteenth Mexican International Conference on Computer Science 第十六届墨西哥计算机科学国际会议论文集

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235

Karina Caro, Karina Figueroa, Marcela D. Rodríguez

引用次数: 0

An Optimization Based Approach for Maximizing the Information Content of Keypoints Detected on a Digital Image 一种基于优化的数字图像关键点信息量最大化方法

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235.3149236

Fernando Fausto, E. V. C. Jiménez, M. A. P. Cisneros

{"title":"An Optimization Based Approach for Maximizing the Information Content of Keypoints Detected on a Digital Image","authors":"Fernando Fausto, E. V. C. Jiménez, M. A. P. Cisneros","doi":"10.1145/3149235.3149236","DOIUrl":"https://doi.org/10.1145/3149235.3149236","url":null,"abstract":"The task of finding point correspondences between two images of the same scene is one of the most challenging problems for a wide variety of computer vision applications. With regard to this, matching precision difficulties are related to the information content provided by the feature descriptors that are extracted from a set of interest points previously detected within a digital image. To overcome such difficulties, methods and techniques which aim to increase the information content (distinctiveness) of such feature descriptors must be developed. In this paper, an optimization-based approach for maximizing the information content provided by a set of feature vectors computed by the feature description method known as Spider Local Image Feature (SLIF) is proposed. In order to test the feasibility of the proposed approach, several state-of-the-art swarm optimization algorithms, such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Social Spider Optimization (SSO) were implemented. The proposed experimental setup results show that the problem of maximizing the distinctiveness of a set of feature descriptors could be effectively modeled as an optimization problem, and as such, be solved by implementing popular optimization techniques.","PeriodicalId":413074,"journal":{"name":"Proceedings of the Sixteenth Mexican International Conference on Computer Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415279","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

Towards Document Distribution in Private Cloud Storage Systems 面向私有云存储系统的文档分发

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235.3149239

I. Lopez-Arevalo, J. L. Gonzalez, Leyla Alvarez-Medina, Ana B. Ríos-Alvarado

引用次数: 0

A Machine Learning Pipeline to Automatically Identify and Classify Roadway Surface Disruptions 自动识别和分类道路表面破坏的机器学习管道

Proceedings of the Sixteenth Mexican International Conference on Computer Science Pub Date : 2016-11-14 DOI: 10.1145/3149235.3149238

Mario Ezra Aragón, M. Carlos, Luis Carlos González-Gurrola, H. Escalante

{"title":"A Machine Learning Pipeline to Automatically Identify and Classify Roadway Surface Disruptions","authors":"Mario Ezra Aragón, M. Carlos, Luis Carlos González-Gurrola, H. Escalante","doi":"10.1145/3149235.3149238","DOIUrl":"https://doi.org/10.1145/3149235.3149238","url":null,"abstract":"Smartphone-based applications for Intelligent Transportation Systems (ITS) have become a real possibility because of the sensing and computing capabilities of these devices. In this work we employ smartphones' accelerometers to sense the quality of roads, detecting the perturbations encountered by the vehicle. The ultimate goal of this line of work is to correctly identify, classify and georeference all obstacles so alleviating measures can be taken. Having a continuous series of accelerometer readings, the first problem is to identify when a perturbation was sensed (segmentation). To approach this problem, we propose using a Support Vector Machine (SVM), obtaining an accuracy of about 82%, outperforming other ad-hoc techniques such as Simple Mobile Average (SMA) and four other competitors. After segmentation, the next problem is to classify the event in one out of four different categories. To this end, we apply a Bag of Words representation and a Random Forest (RF), obtaining an accuracy of about 75%. These results were obtained by exhaustively training and testing this classifier over a newly created dataset that comprises signals for 30 different roads. Altogether, the use of a SVM followed by a RF seems to be a viable option to create a pipeline to automatically recognize and identify Roadway Surface Disruptions.","PeriodicalId":413074,"journal":{"name":"Proceedings of the Sixteenth Mexican International Conference on Computer Science","volume":" 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159386","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