2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)最新文献

Reduced complexity architecture for integral image generation 降低了整体图像生成的复杂性架构

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397509

M. A. Khorsandi, N. Karimi

引用次数: 2

3D reconstruction of non-rigid surfaces from realistic monocular video 真实感单目视频中非刚性表面的三维重建

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397536

M. Sepehrinour, S. Kasaei

{"title":"3D reconstruction of non-rigid surfaces from realistic monocular video","authors":"M. Sepehrinour, S. Kasaei","doi":"10.1109/IRANIANMVIP.2015.7397536","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397536","url":null,"abstract":"A novel algorithm for recovering the 3D shape of deformable objects purely from realistic monocular video is presented in this paper. Unlike traditional non-rigid structure from motion (NRSfM) methods, which have been studied only on synthetic datasets and controlled lab environments that needs some prior constraints (such as manually segmented objects, limited rotations and occlusions, or full-length trajectories), the proposed method has been described and tested on realistic video sequences, which have been downloaded from some social networks (such as Facebook and Twitter). In order to apply NRSfM to the realistic video sequences, because of no-prior information about the scene and camera parameters, one should employ different methods that can handle a huge amount of unknown parameters (such as 3D shape and camera parameters) and deal with some other ambiguities such as incomplete segmentation and Tracking. In this paper, this goal is concerned by first proposing a novel method for completing the missing trajectories (as the most important challenge in realistic videos due to occlusions and lighting changes) and then applying a method that formulates the NRSfM as an energy minimization problem. The proposed method is evaluated on popular video segmentation datasets and its performance is compared to other available methods.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541465","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

Reduced-Reference image quality assessment based on 2-D discrete FFT and Edge Similarity 基于二维离散FFT和边缘相似度的减少参考图像质量评估

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397496

Majid Khorrami, Zhila Azimzadeh, S. Nabipour

引用次数: 4

An efficient run-based method for connected component labeling 一种高效的基于运行的连通构件标注方法

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397514

M. M. Gharasuie, Aboozar Gaffari

引用次数: 1

Beyond bag-of-words: An improved Sparse Topical Coding for learning motion patterns in traffic scenes 超越词袋:用于交通场景运动模式学习的改进稀疏局部编码

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397491

P. Ahmadi, M. Tabandeh, I. Gholampour

引用次数: 0

Performance analysis of processing load distribution in camera networks for multi-target tracking 多目标跟踪摄像机网络中处理负荷分配的性能分析

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397544

Mehdi Jafarizadeh, A. Zakerolhosseini

引用次数: 5

Super resolution on the centroid reference grid 质心参考网格的超分辨率

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397541

H. Rezayi, S. Seyedin

引用次数: 0

Correlation estimation between nitrogen and bean plant colors 氮与豆类植物颜色的相关性分析

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397522

Tayebeh Valiollahi, A. Shahbahrami, M. Zavareh

{"title":"Correlation estimation between nitrogen and bean plant colors","authors":"Tayebeh Valiollahi, A. Shahbahrami, M. Zavareh","doi":"10.1109/IRANIANMVIP.2015.7397522","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397522","url":null,"abstract":"Processing digital images has a lot of applications in different sciences such as medicine, industry and agriculture. One of the uses of digital images is in agriculture industry, for instance digital images could be used in providing the nitrogen of the plant. This research aims to estimate the correlation between the amount of nitrogen in bean plants and its color parameters. For this goal an algorithm is proposed in this paper. First beans images are provided and some preprocessing operations such as resizing, noise removing are performed. Second, RGB color space is converted to HSV color space. Finally correlation between plant color and nitrogen is estimated using regression equation. Implementation results show that there is high, strong, and positive correlation between the color features and the amount of nitrogen in the tissue of the bean. Among RGB, green color has the highest correlation with nitrogen compared to other colors, it is about 0.62. In addition, we consider the combination of three colors for our estimations. Our study show that (G/G+R) has the highest correlations with nitrogen in comparison to other equations and results. It is about 0.81.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138584","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 decision fusion framework for high-resolution remote-sensing image classification 高分辨率遥感影像分类决策融合框架

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397540

Ali Jafari, Mostafa Heidarpour

{"title":"A decision fusion framework for high-resolution remote-sensing image classification","authors":"Ali Jafari, Mostafa Heidarpour","doi":"10.1109/IRANIANMVIP.2015.7397540","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397540","url":null,"abstract":"Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A single voting algorithm is used for decision fusion and an ambiguity detection module is used to determine the ambiguous situations. An ambiguous situation will occur during multi-category voting, where more than one class got the maximum votes, and also when the number of the same votes doesn't exceeds a desired threshold. To resolve this situation we proposed to use the earth mover's distance (EMD) which is a metric for histogram matching. Indeed, we used the EMD to compare the BOF based histogram of images with the centroid classes. Finally, to evaluate the proposed framework, we used a multi-category remote-sensing image dataset and compared the proposed approach with several other similar approaches with BOF based classifiers. The experimental results demonstrate the effectiveness of the proposed framework.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125030079","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

Iterative semi-supervised learning approach for color image segmentation 彩色图像分割的迭代半监督学习方法

2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397508

M. Jafari, S. Samavi

引用次数: 3