W. P. Amorim, Felipe Silveira Brito Borges, Pache Marcio C. B., M. H. Carvalho, H. Pistori
{"title":"Optimum-Path Forest in the classification of defects in Bovine Leather","authors":"W. P. Amorim, Felipe Silveira Brito Borges, Pache Marcio C. B., M. H. Carvalho, H. Pistori","doi":"10.1109/WVC.2019.8876936","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876936","url":null,"abstract":"In this paper, the Optimum-Path Forest (OPF) classifier is applied in the classification of defects in cowhide, a problem of great evaluation complexity. The OPF classifier reduces a pattern classification problem to the problem of partitioning the vertices of a graph induced by its data set. The results revealed a competent performance compared to traditional classifiers, such as Support Vector Machines (SVM), Artificial Neural Networks-Perceptron Multilayer (MLP), Decision Trees (J48) and k-Nearest Neighbor (kNN).","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126988925","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}
Everton VIlhena Cardoso, Helmuth A. Risch, Lucas P. Laheras, Vinícius Luiz, P. S. Rodrigues, G. Wachs-Lopes
{"title":"Image Stitching Using Non-Extensive Statistics","authors":"Everton VIlhena Cardoso, Helmuth A. Risch, Lucas P. Laheras, Vinícius Luiz, P. S. Rodrigues, G. Wachs-Lopes","doi":"10.1109/WVC.2019.8876939","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876939","url":null,"abstract":"Nowadays there are different ways to make image stitching with help of Fiducial Point Descriptors (FPD), whose find the matches between images for an application, such as SIFT and calculates the homography with RANSAC. However, by finding the right match when we have images on differents points of view could be difficult. This paper introduces the application of q-SFT, a newest variation of SFT in a stitch algorithm, that can recognize large viewpoint changes called as LVC.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930013","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}
L. D. Lima, Eudamara Barbosa da Silva Acosta, A. B. Gonçalves, M. Pache, D. Sant’Ana, Celso Soares Costa, H. Pistori, A. Ferreira, C. Elisei
{"title":"Application of Superpixel to identify Maggots and their larval stages","authors":"L. D. Lima, Eudamara Barbosa da Silva Acosta, A. B. Gonçalves, M. Pache, D. Sant’Ana, Celso Soares Costa, H. Pistori, A. Ferreira, C. Elisei","doi":"10.1109/WVC.2019.8876927","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876927","url":null,"abstract":"Flies maggots are used to estimate the postmortem interval (PMI) through their developmental time in forensic entomology. Maggots identification is hard since they often have similar morphologies. Computer vision techniques and machine learning seem to be a good alternative to solve this problem. The aim is to create maggots microscopic images database and apply the dataset with an algorithm to automate the maggots identification. Although that approach could be used in forensic entomological identification and criminal expertise, this paper focuses on comparing the image classifications with IBK, J48, Random Forest and Random Tree classifiers. The Random Forest algorithm achieved the best performance, which was above 80% in most tests using the precision metric (P).","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712042","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}
{"title":"Coffee Leaf Rust Detection Using Genetic Algorithm","authors":"A. Marcos, Natan Luis Silva Rodovalho, A. Backes","doi":"10.1109/WVC.2019.8876934","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876934","url":null,"abstract":"In Brazil, most of the productive coffee plants is susceptible to rust, a severe disease caused by a pathogenic fungi which attacks the leaves of coffee plants, thus causing a drop in coffee production of up to 45%. To address this problem this paper proposes a genetic algorithm-based solution to identify rust in coffee leaves, thus contributing to a better combat of its fungus and less use of pesticides. We use the genetic algorithm to compute an optimal convolutional kernel mask that emphasizing color and texture features of the fungus infection in the leaf. Comparison with data provided by experts indicated that our approach represents and feasible solution for the problem of identifying rust.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825156","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}
Keiller Nogueira, C. César, P. H. T. Gama, Gabriel L. S. Machado, J. A. D. Santos
{"title":"A Tool for Bridge Detection in Major Infrastructure Works Using Satellite Images","authors":"Keiller Nogueira, C. César, P. H. T. Gama, Gabriel L. S. Machado, J. A. D. Santos","doi":"10.1109/WVC.2019.8876942","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876942","url":null,"abstract":"The identification of bridges in major infrastructure works is crucial to provide information about the status of these constructions and support possible decision-making processes. Typically, this identification is performed by human agents that must detect the bridges into large-scale datasets, analyzing image by image, a time-consuming task. In this paper, we propose a novel tool to perform bridge detection and identification in large-scale remote sensing datasets. This tool implements a deep learning-based algorithm, the Faster R-CNN (Regions with CNN features), a technique that is the current state-of-the-art for many object detection and identification applications. Since deep training usually requires a lot of data, we also created a bridge image dataset, composed of remote sensing images from around the globe. The proposed tool was encapsulated into an ArcGIS plugin in order to facilitate its use by non-programmer users.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822679","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}
Henrique Leal Tavares, João Baptista Cardia Neto, J. Papa, Danilo Colombo, A. Marana
{"title":"Tracking and Re-identification of People Using Soft-Biometrics","authors":"Henrique Leal Tavares, João Baptista Cardia Neto, J. Papa, Danilo Colombo, A. Marana","doi":"10.1109/WVC.2019.8876921","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876921","url":null,"abstract":"The goal of this work is proposing a method of biometric identification using soft-biometrics, that aims the extraction of physical characteristics and estimation of the pose as unique traits of each individual, to name and trace that specific person trough the scene. In this work we partially used the public database CASIA Gait Database-A, which has several frames of people, already classified, walking in different directions and angulations, along with a set of silhouettes that were extracted from these scenes and the background used at recordings. Besides, we used a private database of the project sponsor, Petrobras, containing videos of security cameras used to demonstrate the daily routine of workers at an oil platform. The biggest challenges of performing biometrics in this dataset are the quality of the provided images and the heavy clothing used by the workers on the platform, that often hinders the processing quality of the algorithm, explaining why we chose to work with soft-biometric. The algorithm used in this method is PifPaf, made to estimate the human pose and extract features and capable of performing the detection in environments with noises, low illumination or low resolution. With its help, we mean to extract parts of the workers bodies in the private database and from the actors in the scenes from the CASIA Gait Database-A. For our methodology we used the Euclidean and city block distance calculations, obtaining 70% hits with a combination between the PifPaf algorithm and Euclidean distance.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"70 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126575896","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}
Murillo F. Bouzon, Guilherme Albertini, Gustavo Viana, G. Medeiros, P. S. Rodrigues
{"title":"A Bio-Inspired Strategy for 3D Surface Reconstruction of Unstructured Scenes Applied to Medical Images","authors":"Murillo F. Bouzon, Guilherme Albertini, Gustavo Viana, G. Medeiros, P. S. Rodrigues","doi":"10.1109/WVC.2019.8876954","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876954","url":null,"abstract":"The use of 3D reconstruction, along with immersive technologies, is a technique used in several areas of research and development. Currently, the most common strategy for performing this type of reconstruction is using a stereoscopic camera model. The problem worsens when the challenge involves unstructured scenes, which are scenes that have an ill-defined cognitive architecture. The present work proposes a methodology for 3D reconstruction of unstructured surfaces using monocular cameras. Thus, modern AI techniques, Computer Vision and Computer Graphics techniques have been applied to solve this problem. The experiments performed in this work can be concluded that the proposed method can reconstruct structured scenes with a hit rate between 63% and 68%, depending on the number of thresholds used in the segmentation, thus being superior to the classical method, where the extraction of points is done over the original image without any pre-processing.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124485371","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}
Erik Miguel de Elias, P. M. Tasinaffo, Roberto Hirata Junior
{"title":"Alignment, Scale and Skew Correction for Optical Mark Recognition Documents Based","authors":"Erik Miguel de Elias, P. M. Tasinaffo, Roberto Hirata Junior","doi":"10.1109/WVC.2019.8876933","DOIUrl":"https://doi.org/10.1109/WVC.2019.8876933","url":null,"abstract":"Acquiring an OMR (Optical Mark Recognition) reading equipment can be impracticable to some companies or individuals due to its costs. Computational software solutions can be more attractive, but they require specific page format or page layout, such as specific marks to be used when recognizing any OMR document. In this paper, we propose a way to treat skew, translation, scale and alignment using a base document as reference due to the intrinsic characteristics of the problem. Key points are found by a pattern matching algorithm and used for the document image transformation. The method does not require specific layout, needing less formatting, allowing non-experts to create the form using ordinary software and scanners. Two experiments were executed: one with 40 images distorted randomly from a document clipping, and the second one with 1034 images of real student tests. Both experiments reached high overall accuracy.","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130729845","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}
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