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

Banknote portrait detection using convolutional neural network 基于卷积神经网络的纸币肖像检测

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986895

Ryutaro Kitagawa, Yoshihiko Mochizuki, S. Iizuka, E. Simo-Serra, Hiroshi Matsuki, N. Natori, H. Ishikawa

引用次数: 9

Robust registration of serial cell microscopic images using 3D Hilbert scan search 使用三维希尔伯特扫描搜索的连续细胞显微图像的鲁棒配准

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986917

Yongwen Lai, S. Kamata, Zhizhong Fu

引用次数: 0

Pedestrian near-miss analysis on vehicle-mounted driving recorders 基于车载行车记录仪的行人未遂分析

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986889

Teppei Suzuki, Y. Aoki, Hirokatsu Kataoka

引用次数: 7

Ball-like observation model and multi-peak distribution estimation based particle filter for 3D Ping-pong ball tracking 基于类球观测模型和多峰分布估计的三维乒乓球跟踪粒子滤波

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986883

Ziwei Deng, Xina Cheng, T. Ikenaga

引用次数: 3

Event based surveillance video synopsis using trajectory kinematics descriptors 利用轨迹运动学描述符的基于事件的监控视频摘要

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986848

Wei-Cheng Wang, P. Chung, Chun-Rong Huang, Wei-Yun Huang

引用次数: 12

Mixture particle filter with block jump biomechanics constraint for volleyball players lower body parts tracking 基于挡跳生物力学约束的混合粒子滤波在排球运动员下体跟踪中的应用

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986861

Fanglu Xie, Xina Cheng, T. Ikenaga

引用次数: 2

FPGA implementation of high frame rate and ultra-low delay vision system with local and global parallel based matching FPGA实现基于局部和全局并行匹配的高帧率和超低延迟视觉系统

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-07-19 DOI: 10.23919/MVA.2017.7986857

Tingting Hu, T. Ikenaga

{"title":"FPGA implementation of high frame rate and ultra-low delay vision system with local and global parallel based matching","authors":"Tingting Hu, T. Ikenaga","doi":"10.23919/MVA.2017.7986857","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986857","url":null,"abstract":"High frame rate and ultra-low delay image processing system plays an increasingly important role in human-machine interactive applications which call for a better experience. Current works based on vision chip target on video with simple patterns or simple shapes in order to get a higher speed, while a more complicated system is required for real-life applications. This paper proposes a BRIEF based matching system with high frame rate and ultra-low delay for specific object tracking, implemented on FPGA board. Local parallel and global pipeline based matching and 4-1-4 thread transformation are proposed for the implementation of this system. Local parallel and global pipeline based matching is proposed for high-speed matching. And 4-1-4 thread transformation is proposed to reduce the enormous resource cost caused by highly paralled and pipelined structure. In a broader framework, the proposed image processing system is made parallelized and pipelined for a high throughput which can meet the high frame rate and ultra-low delay system's demand. Evaluation results show that the proposed image processing core can work at 1306fps and 0.808ms delay with the resolution of 640×480. System using the image processing core and a camera with 784fps frame rate and 640×480 resolution is designed.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131799079","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: 9

Automatic extraction and recognition of shoe logos with a wide variety of appearance 自动提取和识别具有多种外观的鞋标

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986838

Kazunori Aoki, W. Ohyama, T. Wakabayashi

引用次数: 2

Real-time recognition of sign language gestures and air-writing using leap motion 实时识别手语手势和空中书写使用跳跃运动

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986825

Pradeep Kumar, Rajkumar Saini, S. Behera, D. P. Dogra, P. Roy

{"title":"Real-time recognition of sign language gestures and air-writing using leap motion","authors":"Pradeep Kumar, Rajkumar Saini, S. Behera, D. P. Dogra, P. Roy","doi":"10.23919/MVA.2017.7986825","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986825","url":null,"abstract":"A sign language is generally composed of three main parts, namely manual signas that are gestures made by hand or fingers movements, non-manual signs such as facial expressions or body postures, and finger-spelling where words are spelt out using gestures by the signers to convey the meaning. In literature, researchers have proposed various Sign Language Recognition (SLR) systems by focusing only one part of the sign language. However, combination of different parts has not been explored much. In this paper, we present a framework to recognize manual signs and finger spellings using Leap motion sensor. In the first phase, Support Vector Machine (SVM) classifier has been used to differentiate between manual and finger spelling gestures. Next, two BLSTM-NN classifiers are used for the recognition of manual signs and finger-spelling gestures using sequence-classification and sequence-transcription based approaches, respectively. A dataset of 2240 sign gestures consisting of 28 isolated manual signs and 28 finger-spelling words, has been recorded involving 10 users. We have obtained an overall accuracy of 63.57% in real-time recognition of sign gestures.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"25 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":"126830850","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: 47

A linear method for recovering the depth of Ultra HD cameras using a kinect V2 sensor 使用kinect V2传感器恢复超高清摄像机深度的线性方法

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) Pub Date : 2017-05-08 DOI: 10.23919/MVA.2017.7986908

Yuan Gao, M. Ziegler, Frederik Zilly, Sandro Esquivel, R. Koch

{"title":"A linear method for recovering the depth of Ultra HD cameras using a kinect V2 sensor","authors":"Yuan Gao, M. Ziegler, Frederik Zilly, Sandro Esquivel, R. Koch","doi":"10.23919/MVA.2017.7986908","DOIUrl":"https://doi.org/10.23919/MVA.2017.7986908","url":null,"abstract":"Depth-Image-Based Rendering (DIBR) is a mature and important method for making free-viewpoint videos. As for the study of the DIBR approach, on the one hand, most of current research focuses on how to use it in systems with low resolution cameras, while a lot of Ultra HD rendering devices have been launched into markets. On the other hand, the quality and accuracy of the depth image directly affects the final rendering result. Therefore, in this paper we try to make some improvements on solving the problem of recovering the depth information for Ultra HD cameras with the help of a Kinect V2 sensor. To this end, a linear least squares method is proposed, which recovers the rigid transformation between a Kinect V2 and an Ultra HD camera, using the depth information from the Kinect V2 sensor. In addition, a non-linear coarse-to-fine method, which is based on Sparse Bundle Adjustment (SBA), is compared with this linear method. Experiments show that our proposed method performs better than the non-linear method for the Ultra HD depth image recovery both in computing time and precision.","PeriodicalId":193716,"journal":{"name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","volume":"934 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":"116429471","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