2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献_第5页

Texture features based on the use of the hough transform and income inequality metrics 纹理特征基于使用霍夫变换和收入不平等指标

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310094

H. Elsaid, G. Thomas, Dexter Williams

{"title":"Texture features based on the use of the hough transform and income inequality metrics","authors":"H. Elsaid, G. Thomas, Dexter Williams","doi":"10.1109/IPTA.2017.8310094","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310094","url":null,"abstract":"Texture analysis of digital images has potential applications to image segmentation and classification. The quality of texture features can significantly determine the outcome of these two important applications. For images that consist of textures defined as patterns of straight lines, the use of features extracted using the Gray Level Co-occurrence Matrix (GLCM) is a popular choice. For each line that has a particular slope, one has to define a different predicate so that the matrix can capture that particular part of the texture. On the other hand, the Hough transform is a popular technique that detects lines that appear at different angles. We proposed an innovative way to extract texture information from the Hough accumulator using four income inequality metrics for patterns consisting of lines at different angles. We showed that when compared to four common texture metrics extracted from the GLCM, these new features can offer better quality. We used a feature selection algorithm and a classification example to illustrate the results obtained using these new income inequality texture metrics.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122116145","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

Comparison of CNN and MLP classifiers for algae detection in underwater pipelines CNN与MLP分类器在水下管道藻类检测中的比较

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310098

E. Medina, M. R. Petraglia, J. Gomes, A. Petraglia

引用次数: 18

Towards light-compensated saliency prediction for omnidirectional images 全向图像的光补偿显著性预测

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310119

Sourodeep Biswas, Sid Ahmed Fezza, M. Larabi

引用次数: 7

Correlation-based 2D registration method for single particle cryo-EM images 基于相关的低温电镜单粒子图像二维配准方法

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310125

N. A. Anoshina, A. Krylov, D. Sorokin

引用次数: 1

Single parameter post-processing method for image deblurring 图像去模糊的单参数后处理方法

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310093

A. Krylov, A. Nasonov, Yakov Pchelintsev

引用次数: 5

Image matching using GPT correlation associated with simplified HOG patterns 使用GPT相关性与简化HOG模式相关联的图像匹配

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310122

Shizhi Zhang, T. Wakahara, Yukihiko Yamashita

{"title":"Image matching using GPT correlation associated with simplified HOG patterns","authors":"Shizhi Zhang, T. Wakahara, Yukihiko Yamashita","doi":"10.1109/IPTA.2017.8310122","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310122","url":null,"abstract":"GAT (Global Affine Transformation) and GPT (Global Projection Transformation) matchings proposed by Wakahara and Yamashita calculate the optimal AT (affine transformation) and PT (2D projection transformation), respectively. These image matching criteria realize deformation-tolerant matchings by maximizing the normalized cross-correlation between a template and a GAT/GPT-superimposed image. In order to shorten the calculation time, Wakahara and Yamashita also proposed the acceleration algorithms for GAT/GPT matchings. Later on, Wakahara et al. proposed the enhanced GPT matching to calculate the optimal PT parameters simultaneously which overcomes the incompatibility during the matching process. Zhang et al. figured out that these matching techniques do not take account of the conservation of the L2 norm, and introduced norm normalization factors that realize accurate and stable matchings. All these correlation-based matching techniques are well suited for “whole-to-whole” image matching, but are weak in “whole-to-part” image matching being cursed by complex backgrounds and noise. This research firstly proposes simplified HOG patterns for the enhanced GPT matching with norm normalization to obtain the robustness against noise and background. Secondly, this research also proposes the acceleration algorithm for the proposed matching criterion by creating several reference tables. Experiments using the Graffiti dataset show that the proposed method exhibits an outstanding matching ability compared with the original GPT correlation matching and the well-known combination of SURF feature descriptor and RANSAC algorithm. Furthermore, the computational complexity of the proposed method is significantly reduced below double figures via the acceleration algorithm.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576183","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: 4

An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network 基于卷积神经网络的车牌自动检测方法

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310092

J. Mistry, Aashish Kumar Misraa, Meenu Agarwal, Ayushi Vyas, Vishal M. Chudasama, Kishor P. Upla

{"title":"An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network","authors":"J. Mistry, Aashish Kumar Misraa, Meenu Agarwal, Ayushi Vyas, Vishal M. Chudasama, Kishor P. Upla","doi":"10.1109/IPTA.2017.8310092","DOIUrl":"https://doi.org/10.1109/IPTA.2017.8310092","url":null,"abstract":"Detection of helmeted and non-helmeted motorcyclist is mandatory now-a-days in order to ensure the safety of riders on the road. However, due to many constraints such as poor video quality, occlusion, illumination, and other varying factors it becomes very difficult to detect them accurately. In this paper, we introduce an approach for automatic detection of helmeted and non-helmeted motorcyclist using convolutional neural network (CNN). During the past several years, the advancements in deep learning models have drastically improved the performance of object detection. One such model is YOLOv2 [1] which combines both classification and object detection in a single architecture. Here, we use YOLOv2 at two different stages one after another in order to improve the helmet detection accuracy. At the first stage, YOLOv2 model is used to detect different objects in the test image. Since this model is trained on COCO dataset, it can detect all classes of the COCO dataset. In the proposed approach, we use detection of person class instead of motorcycle in order to increase the accuracy of helmet detection in the input image. The cropped images of detected persons are used as input to second YOLOv2 stage which was trained on our dataset of helmeted images. The non-helmeted images are processed further to extract license plate by using OpenALPR. In the proposed approach, we use two different datasets i.e., COCO and helmet datasets. We tested the potential of our approach on different helmeted and non-helmeted images. Experimental results show that the proposed method performs better when compared to other existing approaches with 94.70% helmet detection accuracy.","PeriodicalId":316356,"journal":{"name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134383050","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: 32

Unsupervised data analysis for virus detection with a surface plasmon resonance sensor 用表面等离子体共振传感器检测病毒的无监督数据分析

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310145

Dominic Siedhoff, M. Strauch, V. Shpacovitch, D. Merhof

引用次数: 4

Improving a deep learning based RGB-D object recognition model by ensemble learning 基于集成学习改进深度学习的RGB-D对象识别模型

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310101

Andreas Aakerberg, Kamal Nasrollahi, Thomas Heder

引用次数: 13

Sample-based regularization for support vector machine classification 基于样本的正则化支持向量机分类

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2017-11-01 DOI: 10.1109/IPTA.2017.8310103

D. Tran, Muhammad-Adeel Waris, M. Gabbouj, Alexandros Iosifidis

引用次数: 0