Machine Graphics and Vision最新文献_第3页

Isocontouring with sharp corner features 等高轮廓与尖锐的角落特征

Machine Graphics and Vision Pub Date : 2019-12-01 DOI: 10.22630/mgv.2018.27.1.2

S. Gong, Timothy S Newman

引用次数: 0

Constraint-based algorithm to estimate the line of a milling edge 基于约束的铣削刃线估计算法

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.6

Marcin Bator, K. Śmietańska

引用次数: 1

Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network 基于卷积神经网络的钻头磨损分类迁移学习改进的数据增强技术

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.1

J. Kurek, J. Aleksiejuk-Gawron, Izabella Antoniuk, J. Górski, Albina Jegorowa, M. Kruk, A. Orłowski, J. Pach, B. Świderski, Grzegorz Wieczorek

{"title":"Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network","authors":"J. Kurek, J. Aleksiejuk-Gawron, Izabella Antoniuk, J. Górski, Albina Jegorowa, M. Kruk, A. Orłowski, J. Pach, B. Świderski, Grzegorz Wieczorek","doi":"10.22630/mgv.2019.28.1.1","DOIUrl":"https://doi.org/10.22630/mgv.2019.28.1.1","url":null,"abstract":"This paper presents an improved method for recognizing the drill state on the basis of hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red -- for drill that is worn out and should be replaced, yellow -- for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green -- denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.","PeriodicalId":39750,"journal":{"name":"Machine Graphics and Vision","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87514851","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: 8

Context-based segmentation of the longissimus muscle in beef with a deep neural network 基于上下文的牛肉最长肌的深度神经网络分割

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.5

Karol Talacha, Izabella Antoniuk, L. Chmielewski, M. Kruk, J. Kurek, A. Orłowski, J. Pach, A. Półtorak, B. Świderski, Grzegorz Wieczorek

引用次数: 0

Textural features based on run length encoding in the classification of furniture surfaces with the orange skin defect 基于行程长度编码的纹理特征在桔黄色表皮缺陷家具表面分类中的应用

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.4

J. Pach, Izabella Antoniuk, L. Chmielewski, J. Górski, M. Kruk, J. Kurek, A. Orłowski, K. Śmietańska, B. Świderski, Grzegorz Wieczorek

引用次数: 0

BCT Boost Segmentation with U-net in TensorFlow TensorFlow中基于U-net的BCT Boost分割

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.3

Grzegorz Wieczorek, Izabella Antoniuk, M. Kruk, J. Kurek, A. Orłowski, J. Pach, B. Świderski

{"title":"BCT Boost Segmentation with U-net in TensorFlow","authors":"Grzegorz Wieczorek, Izabella Antoniuk, M. Kruk, J. Kurek, A. Orłowski, J. Pach, B. Świderski","doi":"10.22630/mgv.2019.28.1.3","DOIUrl":"https://doi.org/10.22630/mgv.2019.28.1.3","url":null,"abstract":"In this paper we present a new segmentation method meant for boost area that remains after removing the tumour using BCT (breast conserving therapy). The selected area is a region on which radiation treatment will later be made. Consequently, an inaccurate designation of this region can result in a treatment missing its target or focusing on healthy breast tissue that otherwise could be spared. Needless to say that exact indication of boost area is an extremely important aspect of the entire medical procedure, where a better definition can lead to optimizing of the coverage of the target volume and, in result, can save normal breast tissue. Precise definition of this area has a potential to both improve the local control of the disease and to ensure better cosmetic outcome for the patient. In our approach we use U-net along with Keras and TensorFlow systems to tailor a precise solution for the indication of the boost area. During the training process we utilize a set of CT images, where each of them came with a contour assigned by an expert. We wanted to achieve a segmentation result as close to given contour as possible. With a rather small initial data set we used data augmentation techniques to increase the number of training examples, while the final outcomes were evaluated according to their similarity to the ones produced by experts, by calculating the mean square error and the structural similarity index (SSIM).","PeriodicalId":39750,"journal":{"name":"Machine Graphics and Vision","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81770113","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

Image annotating tools for agricultural purpose - A requirements study 农业用图像注释工具。需求研究

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.7

Marcin Bator, Maciej Pankiewicz

引用次数: 0

Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network 基于迁移学习的分类器集成改进的卷积神经网络钻头磨损分类

Machine Graphics and Vision Pub Date : 2019-01-01 DOI: 10.22630/mgv.2019.28.1.2

J. Kurek, J. Aleksiejuk-Gawron, Izabella Antoniuk, J. Górski, Albina Jegorowa, M. Kruk, A. Orłowski, J. Pach, B. Świderski, Grzegorz Wieczorek

{"title":"Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network","authors":"J. Kurek, J. Aleksiejuk-Gawron, Izabella Antoniuk, J. Górski, Albina Jegorowa, M. Kruk, A. Orłowski, J. Pach, B. Świderski, Grzegorz Wieczorek","doi":"10.22630/mgv.2019.28.1.2","DOIUrl":"https://doi.org/10.22630/mgv.2019.28.1.2","url":null,"abstract":"In this paper we introduce the enhanced drill wear recognition method, based on classifiers ensemble, obtained using transfer learning and data augmentation methods. Red, green and yellow classes are used to describe the current drill state. The first one corresponds to the case when drill should be immediately replaced. The second one denotes a tool that is still in a good condition. The final class refers to the case when a drill is suspected of being worn out, and a human expert evaluation would be required. The proposed algorithm uses three different, pretrained network models and adjusts them to the drill wear classification problem. To ensure satisfactory results, each of the methods used was required to achieve accuracy above 90% for the given classification task. Final evaluation is achieved by voting of all three classifiers. Since the initial data set was small (242 instances), the data augmentation method was used to artificially increase the total number of drill hole images. The experiments performed confirmed that the presented approach can achieve high accuracy, even with such a limited set of training data.","PeriodicalId":39750,"journal":{"name":"Machine Graphics and Vision","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86617921","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

An ensemble feature method for food classification 食品分类的集成特征方法

Machine Graphics and Vision Pub Date : 2017-12-01 DOI: 10.22630/mgv.2017.26.1.2

N. Martinel, C. Micheloni, C. Piciarelli

引用次数: 2

Applied inverse kinematics for bipedal characters moving on the diverse terrain 应用逆运动学的两足人物移动在不同的地形

Machine Graphics and Vision Pub Date : 2017-12-01 DOI: 10.22630/mgv.2017.26.1.1

Ł. Burdka, P. Rohleder

引用次数: 0