2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献

Automated Classification of Wrinkle Levels in Seed Coat Using Relevance Vector Machine 基于相关向量机的种皮皱度自动分类

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457939

A. Shafiekhani, Arun Prabhu Dhanapal, J. Gillman, F. Fritschi, G. DeSouza

引用次数: 1

A Cost-Effective, Fast, and Robust Annotation Tool 一个经济、快速、健壮的注释工具

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457958

A. F. Said, Vinay Kashyap, Namrata Choudhury, F. Akhbari

引用次数: 1

Scene wireframes sketching for UAVs 场景线框图素描的无人机

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457938

R. Santos, X. López, Xosé R. Fernández-Vidal

引用次数: 0

Video-Level Binocular Tone-mapping Framework Based on Temporal Coherency Algorithm 基于时间相干算法的视频级双目色调映射框架

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457950

Mingyue Feng, M. Loew

引用次数: 2

Remote Sensing to Analyze Wealth, Poverty, and Crime 遥感分析财富、贫困和犯罪

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457971

J. Irvine, Richard J. Wood, Payden Mcbee

引用次数: 1

Robust and Accurate Objects Measurement in Real-World Based on Camera System 现实世界中基于相机系统的鲁棒精确目标测量

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457954

A. F. Said

引用次数: 1

Texture Classification using Multiple Local Descriptors 使用多个局部描述符的纹理分类

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457968

Zakariya A. Oraibi, Morgane Irio, A. Hafiane, K. Palaniappan

{"title":"Texture Classification using Multiple Local Descriptors","authors":"Zakariya A. Oraibi, Morgane Irio, A. Hafiane, K. Palaniappan","doi":"10.1109/AIPR.2017.8457968","DOIUrl":"https://doi.org/10.1109/AIPR.2017.8457968","url":null,"abstract":"Classifying images based on texture features is an active topic in computer vision and pattern recognition field. Many applications like biomedical image analysis, image retrieval, and face recognition emerged from texture classification task. In this paper, we propose a new method to classify texture images by combining multiple histogram-based texture descriptors. First, we compute new efficient features called Joint Motif Labels (JML) and Motif Patterns (MP) descriptors. Both descriptors are based on the motif Peano scan concept that traverses image pixels in a 2×2 neighborhood producing one of 12 motif patterns, according to certain criteria. JML uses additional information, mean and variance, as joint distribution with motif patterns. After that, texture descriptors like Rotation Invariance Co-occurrence Among Local Binary Pattern (RIC-LBP) and Joint Adaptive Median Binary Pattern (JAMBP) are combined along with the new JML and MP descriptors in order to improve the classification performance. Experiments are performed on challenging texture datasets namely, KTH- TIPS-2b and DTD using two classifiers, kNN and SVM. The experiments demonstrate that our approach performs better than the single best texture descriptor with an accuracy of 67.2% and 43.5% on both datasets respectively.","PeriodicalId":128779,"journal":{"name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582623","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

Tahitian Pearls' Luster Assessment Automation 塔希提珍珠光泽评估自动化

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457974

Gaël Mondonneix, S. Chabrier, Jean-Martial Mari, A. Gabillon

引用次数: 2

Restoration of Medical Images Using Genetic Algorithms 利用遗传算法恢复医学图像

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457940

A. Sheta

{"title":"Restoration of Medical Images Using Genetic Algorithms","authors":"A. Sheta","doi":"10.1109/AIPR.2017.8457940","DOIUrl":"https://doi.org/10.1109/AIPR.2017.8457940","url":null,"abstract":"Image restoration is still one of the most important areas of medical image processing. Image restoration concerns about the removal or reduction of degradations in an image that could happen during the acquisition process. Being able to restore a medical image helps providing a better diagnosis and treatment. One of the most common blurring is the motion blur. Many restoration algorithms were proposed to solve the image restoration problem such as Wiener Filter, Lucy-Richardson and Blind Deconvolution Algorithms. These algorithms have varied performance, computational complexity, and abilities to deal with noisy images. They also require the knowledge of the Point Spread function (PSF) such that image deconvolution can be implemented. Restoration of an image is extremely reliant on the quality of the estimation technique used to find an accurate PSF parameters (i.e. motion length and motion angle). In this paper, we adopt Genetic Algorithms (GAs) to find the optimal PSF parameters such that a Wiener filter can be used for image restoration. We adopted number of statistical evaluation criteria to asses the quality of our proposed method. We applied our method on a number of medical images with various additive Gaussian noise. The developed results show that our proposed algorithm, PSF generated by GAs, is showing better results compared to other known methods in the literature in the absence of the real PSF.","PeriodicalId":128779,"journal":{"name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326847","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

Benchmarking Convolutional Neural Networks for Object Segmentation and Pose Estimation 基于卷积神经网络的目标分割和姿态估计

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2017-10-01 DOI: 10.1109/AIPR.2017.8457942

T. Le, L. Hamilton, A. Torralba

引用次数: 0