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2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)最新文献

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Ellipse detection method based on the advanced three point algorithm 基于先进三点算法的椭圆检测方法
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103741
Bae-keun Kwon, D. Kang
{"title":"Ellipse detection method based on the advanced three point algorithm","authors":"Bae-keun Kwon, D. Kang","doi":"10.1109/FCV.2015.7103741","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103741","url":null,"abstract":"In this paper, we propose a fast ellipse detection method using the geometric properties of three points, which are the components of an ellipse. As many conventional ellipse detection methods carry out the detection using five points, a random selection of such points requires much redundant processing. Accordingly, in order to search for an ellipse with minimum number of points, this paper uses the normal and differential equation of an ellipse which requires three points based on their locations and edge angles. First, in order to reduce the number of candidate edges, the edges are divided into 8 groups depending on the edge angle, and then a new geometric constraint called quadrant condition is introduced for the reduction of noisy candidate edges. Clustering is employed to find prominent candidates in the space of some ellipse parameters. Experiments through real images show that our method satisfies both the reliability and detection speed of ellipse detection.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132850210","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: 2
Evaluating freshness of produce using transfer learning 用迁移学习评价农产品的新鲜度
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103747
Antony Lam, Y. Kuno, Imari Sato
{"title":"Evaluating freshness of produce using transfer learning","authors":"Antony Lam, Y. Kuno, Imari Sato","doi":"10.1109/FCV.2015.7103747","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103747","url":null,"abstract":"Automated quality control of produce such as fruits and vegetables is of great importance to industry. In particular, the ability to evaluate the state of decay for various produce items would allow for efficient sorting of produce such that the freshest items could be more quickly shipped to consumers. Unfortunately, training an accurate classifier for determining how decayed produce is can require a large amount of data. This problem is further exacerbated by the large variety of produce available as different items would exhibit decay in different ways. In this paper, we propose an algorithm that can learn an accurate ranking classifier for sorting produce using only a small amount of data. We achieve this through our proposed transfer learning algorithm that is able to automatically select good preexisting source task training data to supplement insufficient training data in the given target task. We show how much our algorithm improves over standard training on real images of produce items captured at various stages of decay.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396688","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
Binarization of music score images using line width transform 使用线宽变换的乐谱图像二值化
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103736
Vo Quang Nhat, Gueesang Lee
{"title":"Binarization of music score images using line width transform","authors":"Vo Quang Nhat, Gueesang Lee","doi":"10.1109/FCV.2015.7103736","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103736","url":null,"abstract":"Although the original Gaussian Mixture Markov Random Field model can generate good binarization results for scene text images, it still has some issues needed to be solved in case of music score images. The difficulty is the ineffective seeding algorithm when it is applied to music score images which consist of thin lines, and isolated and complex background regions. A wrong seeding makes the false positive and false negative in foreground and background labelling. In this paper, a new adaptive model for the binarization of complex background music score image is proposed. We suggest a line width transform based seeding method for a better GMMs initialization of foreground and background color distribution in music score image. The result is the better binarization with cleaner background and clearer foreground compared to previous binarization techniques.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124531868","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 system architecture for real time traffic monitoring in foggy video 雾天视频实时交通监控系统架构
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103720
Sangkyoon Kim, Soonyoung Park, Kyoungho Choi
{"title":"A system architecture for real time traffic monitoring in foggy video","authors":"Sangkyoon Kim, Soonyoung Park, Kyoungho Choi","doi":"10.1109/FCV.2015.7103720","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103720","url":null,"abstract":"In foggy video, the visibility and contrast of objects are decreased dramatically, which causes the performance degradation of traffic monitoring systems. In this paper, an architecture for real-time traffic monitoring system is presented for foggy video. For the real-time traffic monitoring, it is required to satisfy two major constraints. First, the quality of an image after fog removal is good enough for further processing such as object detection and tracking. Second, it has to be computationally cheap for real-time processing. In this paper, a parallel architecture is proposed, consisting of N threads, for a real-time traffic monitoring system. The proposed parallel architecture shows the significant reduction of processing time for the development of real-time traffic monitoring systems.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115497223","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
Object classification using CNN for video traffic detection system 利用CNN进行对象分类的视频流量检测系统
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103755
Hyeok Jang, Hunjun Yang, D. Jeong, Hun Lee
{"title":"Object classification using CNN for video traffic detection system","authors":"Hyeok Jang, Hunjun Yang, D. Jeong, Hun Lee","doi":"10.1109/FCV.2015.7103755","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103755","url":null,"abstract":"Recently, a lot of research on the use of big data is made, and this paper was aimed to perform classification experiments using CNN for the detected object collected from traffic detectors. In addition the experimental results were compared with the HOG descriptor that is commonly used in existing pedestrian and object classification and wavelet, texture and descriptor that are used in the road surface condition classification. According to the results after applied to the collected RVFTe-10 data, the performances of HOG SVM and CNN were excellent by showing 99.9% and 99.5% respectively.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"203 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094372","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: 20
Analysis of BRDF/BTDF for the texture representation of woven fabrics based on the impression-evaluation model 基于印象评价模型的BRDF/BTDF机织物纹理表征分析
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103751
Lisa Park, Kensuke Tobitani, Kenji Katahira, N. Nagata
{"title":"Analysis of BRDF/BTDF for the texture representation of woven fabrics based on the impression-evaluation model","authors":"Lisa Park, Kensuke Tobitani, Kenji Katahira, N. Nagata","doi":"10.1109/FCV.2015.7103751","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103751","url":null,"abstract":"To represent the texture of woven fabrics in computer graphics (CG), it is important to reveal the relation between their physical properties and the texture caused by them. For efficient representation of a realistic texture, a new layered model that links visual impressions with physical properties is required. Physical properties of woven fabrics include optical properties. They are reproduced by bidirectional reflectance distribution function (BRDF) and the bidirectional transmittance distribution function (BTDF). However, it is not easy to handle BRDF and BTDF because of the enormity of these data. Therefore, it is necessary to effectively integrate the dimensionality of BRDF and BTDF. In this paper, we propose the impression-evaluation model to link visual impressions with physical properties of woven fabrics. Moreover, to incorporate physical properties into the model, we investigate the main physical factors of reflection and transmission in woven fabrics using the multivariable analysis technique of principal component analysis (PCA) of BRDF and BTDF. As a result, three principal components of BRDF and two principal components of BTDF are obtained. Therefore, it becomes possible for inexpert people to make CG with realistic texture of woven fabrics more intuitively and easily by incorporating physical factors into the impression-evaluation model.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129567860","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: 2
Movement direction-based approaches for pedestrian detection in road scenes 基于运动方向的道路场景行人检测方法
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103727
Seongyoung Jeon, Yoon Suk Lee, K. Choi
{"title":"Movement direction-based approaches for pedestrian detection in road scenes","authors":"Seongyoung Jeon, Yoon Suk Lee, K. Choi","doi":"10.1109/FCV.2015.7103727","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103727","url":null,"abstract":"Pedestrian Detection is a critical technique for avoiding the collision between the vehicle and people, and it can be used in the advanced driver assistance system. Most research of the pedestrian detection areas are focused on the standing or walking people at the training process. INRIA's pedestrian dataset is composed of persons standing and facing the front, however another datasets comprise various types of pedestrian without classification for direction. In other words, movement directions of the pedestrian are not considered on creating detectors. In this paper, we propose a pedestrian detection method using pedestrian data classified into four by moving directions such as front, back, left and right. Each of detectors created by categorized data are integrated, which are used for pedestrian detection. For the training, we use histograms of oriented gradients using the direction distribution of the edges. In the experiments, we use the pedestrian datasets obtained by moving vehicle in order to enhance public confidence. Our result shows the improved detection ratio in comparison to existing methods underutilized the moving direction.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128215448","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
Efficient feature selection method using contribution ratio by random forest 基于随机森林贡献率的高效特征选择方法
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103746
R. Murata, Yohei Mishina, Yuji Yamauchi, Takayoshi Yamashita, H. Fujiyoshi
{"title":"Efficient feature selection method using contribution ratio by random forest","authors":"R. Murata, Yohei Mishina, Yuji Yamauchi, Takayoshi Yamashita, H. Fujiyoshi","doi":"10.1109/FCV.2015.7103746","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103746","url":null,"abstract":"In the field of image recognition, a high-dimensional feature vector is often used to construct a classifier. This presents a problem, however, since using a large number of features can slow down training and degrade model readability. To alleviate this problem, sequential backward selection (SBS) has come to be used as a method for selecting an effective number of features for classification. However, as a type of wrapper method, SBS iteratively constructs and evaluates classifiers when selecting features, which is computationally intensive. In this study, we define the contribution ratio of features by random forest and use it to create an efficient feature selection method. We performed an evaluation experiment to compare the proposed method with SBS and found that the former could significantly reduce feature selection time for the same dimension reduction rate.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"43 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227099","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: 5
Hash function for 3D mesh model authentication 哈希函数用于3D网格模型认证
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103711
Suk-Hwan Lee, Ki-Ryong Kwon, Dong Kyue Kim, Oh-Jun Kwon
{"title":"Hash function for 3D mesh model authentication","authors":"Suk-Hwan Lee, Ki-Ryong Kwon, Dong Kyue Kim, Oh-Jun Kwon","doi":"10.1109/FCV.2015.7103711","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103711","url":null,"abstract":"3D content-based hashing has not been as widely used as compared to 2D content-based hashing in the case of multimedia content such as images and videos. In this study, we develop a robust 3D mesh-model hashing based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications; we also discuss the robustness, uniqueness, security, and model space of the hash for 3D model hashing.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579645","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: 2
Vehicle detection using PLS Hough transform 基于PLS霍夫变换的车辆检测
2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103738
R. Takeuchi, K. Kato, David Harwood, L. Davis
{"title":"Vehicle detection using PLS Hough transform","authors":"R. Takeuchi, K. Kato, David Harwood, L. Davis","doi":"10.1109/FCV.2015.7103738","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103738","url":null,"abstract":"This paper proposes extended Generalized Hough Transform (GHT) to introduce training process by using Partial Least Squares (PLS) regression analysis. Hough transform can robustly detect patterns against noise and occlusions, and GHT is adapted to perform the generic object detection. In this study, we introduced training process to determine the voting weight of GHT by using PLS regression analysis. Thereby, it becomes possible to generic object detection, while maintaining the framework of Hough-based object detection. In this paper, we applied PLS Hough transform to the vehicle detection from satellite images. In addition, we compared PLS Hough transform with the previous approach (original GHT) on the vehicle detection, and our proposed method achieved high detection accuracy.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868368","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
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