发布求助
文献互助 智能选刊 最新文献

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)最新文献

筛选
英文 中文
Quadrant segmentation and ring-like searching based FPGA implementation of ORB matching system for Full-HD video 基于象限分割和环状搜索的全高清视频ORB匹配系统的FPGA实现
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986797
T. Rao, T. Ikenaga
{"title":"Quadrant segmentation and ring-like searching based FPGA implementation of ORB matching system for Full-HD video","authors":"T. Rao, T. Ikenaga","doi":"10.23919/MVA.2017.7986797","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986797","url":null,"abstract":"Full-HD video has drawn more and more attention in advanced computer vision applications which rely on more details in image. Benefit from high resolution input, local feature based matching system which at base of various vision applications, can also get better performance due to more available information. However, high resolution brings massive data and makes it challenging to achieve real-time and low cost at the same time. This paper proposes an ORB-based matching system for Full-HD video which implemented on FPGA. To improve nonlinear functions and feature steering part of ORB in hardware, the Quadrant Segmentation based orientation detector and Ring-like Searching based feature steering are proposed to make original operation more suitable for hardware. Evaluation shows that the proposed ORB matching system can complete feature extraction and matching for one Full-HD(1920×1080) image within 13.37ms and save almost 75% resources on average in feature extraction part compared with SIFT-based design.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756060","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
Improving the performance of non-rigid 3D shape recovery by points classification 通过点分类提高非刚性三维形状恢复的性能
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986852
Junjie Hu, T. Aoki
{"title":"Improving the performance of non-rigid 3D shape recovery by points classification","authors":"Junjie Hu, T. Aoki","doi":"10.23919/MVA.2017.7986852","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986852","url":null,"abstract":"The goal of Non-Rigid Structure from Motion (NRSfM) is to recover 3D shapes of a deformable object from a monocular video sequence. Procrustean Normal Distribution (PND) is one of the best algorithms for NRSfM. It uses Generalized Procrustes Analysis (GPA) model to accomplish this task. But the biggest problem of this method is that just a few non-rigid points in 2D observations can largely affect the reconstruction performance. We believe that PND can achieve better reconstruction performance by eliminating the affection of these points. In this paper, we present a novel reconstruction method to solve this problem. We present two solutions to simply classify the points into non-rigid and nearly rigid points. After that, we use EM algorithm of PND to recover 3D structure again for nearly rigid points. Experimental results show that the proposed method outperforms the existing state-of-the-art algorithms.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969896","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
A preliminary study on extracting objects in sketches 草图中目标提取的初步研究
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986901
Bo Huang, Jiansheng Chen
{"title":"A preliminary study on extracting objects in sketches","authors":"Bo Huang, Jiansheng Chen","doi":"10.23919/MVA.2017.7986901","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986901","url":null,"abstract":"Humans have the incredible ability to interpret complex sketch images, but it remains a challenging task for computers to do the same thing. Researchers have made great progress on nature image detection and recognition, but little research has been done on object detection in sketch images. In this paper, we demonstrate that object extraction can be possibly conducted using a Single-Shot Multibox Detector(SSD) frame-work, without the guidance of segmentation information or user interaction. We train and test our model with a synthetic dataset based on TU-Berlin sketch dataset. Experiments on the synthetic dataset show reasonable object detection and recognition results in sketch images.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130986478","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
An MRF-based image segmentation with unsupervised model parameter estimation 基于mrf的无监督模型参数估计图像分割
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986893
Y. Toya, H. Kudo
{"title":"An MRF-based image segmentation with unsupervised model parameter estimation","authors":"Y. Toya, H. Kudo","doi":"10.23919/MVA.2017.7986893","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986893","url":null,"abstract":"This paper deals with image segmentation when the image consists of uniform background (b.g.) and uniform foreground (f.g.) with noise. We formulate this problem into the joint minimization of MRF energy with respect to a label image and density parameters corresponding to f.g. and b.g., and solve it exactly in reasonable computation time. The proposed method efficiently solves the joint minimization by utilizing the novel property that multiple minimizations of MRF energy, corresponding to different combinations of density parameters for b.g. and f.g., can be solved by a single total-variation minimization. In addition, we also extend the proposed method to the case where label images together with density values corresponding to multiple smoothing (regularization) parameters can be obtained, exactly and simultaneously with a much shorter computation time compared with the trivial exhaustive search.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130714867","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
3D convolutional object recognition using volumetric representations of depth data 三维卷积对象识别使用深度数据的体积表示
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986817
Ali Caglayan, Ahmet Burak Can
{"title":"3D convolutional object recognition using volumetric representations of depth data","authors":"Ali Caglayan, Ahmet Burak Can","doi":"10.23919/MVA.2017.7986817","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986817","url":null,"abstract":"Hand-crafted features are widely used in object recognition field. Recent advances in convolutional neural networks allow to extract features automatically and produce better results in object recognition without considering about feature design. Although RGB and depth data are used in some convolutional network based approaches, volumetric information hidden in depth data is not fully utilized. We present a 3D convolutional neural network based approach to utilize volumetric information extracted from depth data. Using a single depth image, a view-based incomplete 3D model is constructed. Although this method does not provide enough information to build a complete 3D model, it is still useful to recognize objects. To the best of our knowledge, the proposed approach is the first volumetric study on the Washington RGB-D Object Dataset and achieves results as competitive as the state-of-the-art works.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127920935","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: 7
Plane labeling trinocular stereo matching with baseline recovery 平面标记三维立体匹配与基线恢复
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986762
Luis Horna, Robert B. Fisher
{"title":"Plane labeling trinocular stereo matching with baseline recovery","authors":"Luis Horna, Robert B. Fisher","doi":"10.23919/MVA.2017.7986762","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986762","url":null,"abstract":"In this paper we present an algorithm which recovers the rigid transformation that describes the displacement of a binocular stereo rig in a scene, and uses this to include a third image to perform dense trinocular stereo matching and reduce some of the ambiguities inherent to binocular stereo. The core idea of the proposed algorithm is the assumption that the binocular baseline is projected to the third view, and thus can be used to constrain the transformation estimation of the stereo rig. Our approach shows improved performance over binocular stereo, and the accuracy of the recovered motion allows to compute optical flow from a single disparity map. These claims are validated with the KITTI 2012 data set.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132651783","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
Weak rocks disintegration patterns recognition through image analysis 基于图像分析的弱岩崩解模式识别
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986870
O. Rincon, M. Ocampo
{"title":"Weak rocks disintegration patterns recognition through image analysis","authors":"O. Rincon, M. Ocampo","doi":"10.23919/MVA.2017.7986870","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986870","url":null,"abstract":"This paper presents results about image analysis application for identification and classification of disintegration patterns upon weak rocks, clay-bearing rocks and Intermediate Geomaterials IGMs. The research was conducted in order to obtain a reliable method for field and laboratory because upon these materials, it is quite difficult to acquire unaltered samples due to the quick disintegration after the materials are exposed to environmental conditions. Thus, the application of image analysis and color changes produced by disintegration advance has shown reliable results to be used as an alternative method to replace the traditional human eye based classification charts. Several images were taken upon different disintegration states and environmental conditions and were correlated with changes in color channels, using colorimetric indices and statistical image descriptors. As a result, a disintegration classification method based on Image entropy and color changes was stated, the findings were validated and compared with results from traditional methods using both natural and artificial samples with controlled disintegration levels, in addition, Hyperspectral images, were used as well.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114319396","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
A new deep learning architecture for detection of long linear infrastructure 一种新的用于检测长线性基础设施的深度学习架构
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986837
J. Gubbi, Ashley Varghese, P. Balamuralidhar
{"title":"A new deep learning architecture for detection of long linear infrastructure","authors":"J. Gubbi, Ashley Varghese, P. Balamuralidhar","doi":"10.23919/MVA.2017.7986837","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986837","url":null,"abstract":"The use of drones in infrastructure monitoring aims at decreasing the human effort and in achieving consistency. Accurate aerial image analysis is the key block to achieve the same. Reliable detection and integrity checking of power line conductors in a diverse background are the most challenging in drone based automatic infrastructure monitoring. Most techniques in literature use first principle approach that tries to represent the image as features of interest. This paper proposes a machine learning approach for power line detection. A new deep learning architecture is proposed with very good results and is compared with GoogleNet pre-trained model. The proposed architecture uses Histogram of Gradient features as the input instead of the image itself to ensure capture of accurate line features. The system is tested on aerial image collected using drone. A healthy F-score of 84.6% is obtained using the proposed architecture as against 81% using GoogleNet model.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123211190","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: 16
Dynamic hand gesture recognition from cyclical hand pattern 基于周期性手势模式的动态手势识别
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986799
H. Doan, Hai Vu, Thanh-Hai Tran
{"title":"Dynamic hand gesture recognition from cyclical hand pattern","authors":"H. Doan, Hai Vu, Thanh-Hai Tran","doi":"10.23919/MVA.2017.7986799","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986799","url":null,"abstract":"In this paper, we tackle advantages of cyclical movement patterns of hand gestures. The cyclical patterns are defined as closed-form which hand moves away from a rest position, follows one or more of a series of the movement of hand shapes and returns to its rest position. Due to the cyclical pattern characteristic, phase of gestures are supportive cues for deploying robust recognition schemes. We conduct a spatial-temporal representation of the hand gestures which takes into account both hand shapes and its movements during a gesture. The phase alignment then is deployed in the conducted space. The proposed scheme ensures inter-period phase continuity as well as normalizes length of the hand gestures. Three different datasets of dynamic hand gestures consisting of non-cyclical and cyclical patterns are examined. Evaluation results confirm that the best accuracy rate achieves at 96% for cyclical pattern that is significantly higher than results for typical gestures. The proposed method suggests a feasible and robust solution addressing technical issues in developing human-computer interaction applications such as using hand gestures to control home appliance devices.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125977360","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
Crowd pedestrian detection using expectation maximization with weighted local features 基于加权局部特征期望最大化的人群行人检测
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986830
Shih-Shinh Huang, Chun-Yuan Chen
{"title":"Crowd pedestrian detection using expectation maximization with weighted local features","authors":"Shih-Shinh Huang, Chun-Yuan Chen","doi":"10.23919/MVA.2017.7986830","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986830","url":null,"abstract":"This study proposes a method for crowd pedestrian detection based on monocular vision using expectation maximization (EM) with weighted local features. The proposed method mainly consists of two stages: training and detection stages. During training stage, the proposed method firstly constructs a model for describing the pedestrian appearance based on a set of salient features. During detection stage, an algorithm called expectation maximization (EM) is applied to group the extracted corners to several pedestrians based on the constructed codebook through performing E-step and M-step iteratively. The use of EM algorithm makes the proposed method be capable of detecting partially occluded pedestrians, especially in crowded scenes. In the experiment, a well-known dataset called CAVIAR is used to validate the effectiveness of the proposed method.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127460300","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信