2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)最新文献

Skin lesion images classification using new color pigmented boundary descriptors 基于新颜色边界描述符的皮肤病变图像分类

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983026

Saeid Amouzad Mahdiraji, Y. Baleghi, S. M. Sakhaei

引用次数: 16

A feature-based approach for monocular camera tracking in unknown environments 未知环境中基于特征的单目摄像机跟踪方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983021

S. A. Hoseini, P. Kabiri

引用次数: 1

A new pansharpening method using multi resolution analysis framework and deep neural networks 一种基于多分辨率分析框架和深度神经网络的泛锐化方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983017

A. Azarang, H. Ghassemian

引用次数: 54

Hyperspectral anomaly detection using outlier removal from collaborative representation 基于协同表示的异常值去除的高光谱异常检测

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983039

M. Vafadar, H. Ghassemian

{"title":"Hyperspectral anomaly detection using outlier removal from collaborative representation","authors":"M. Vafadar, H. Ghassemian","doi":"10.1109/PRIA.2017.7983039","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983039","url":null,"abstract":"Hyperspectral imagery systems have the ability to collect 3D digital images with rich spatial and spectral information. Anomaly detection is one of the interesting applications over last two decades in hyperspectral imagery. In this paper, we propose Collaborative Representation-Based with Outlier Removal Anomaly Detector (CRBORAD) method for HSI Anomaly Detection. We use both spectral and spatial information for detecting anomalies instead of using only spectral information that was introduced in our previous work. The proposed detector can adaptively estimate the background by its adjacent pixels within a sliding dual window. Before estimating background pixels, we remove outlier pixels that are significantly different from majority of pixels. It leads us to precise background approximation and better accuracy for detecting anomalies in subsequent stages. The residual image is constituted by subtracting the predicted background from the original HSI, and anomalies can be determined in the residual image, finally. Kernel extension of the proposed approach is also presented. We implemented the proposed algorithms on San Diego airport hyperspectral data. CRBORAD results are illustrated using receiver-operating-characteristic (ROC) curves, Area Under Curve (AUC) values and intuitive images. Comparing the results of the current study with four popular and previous methods shows that CRBORAD provides us an accurate method for detecting anomalies.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902497","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: 16

Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm 使用3轴加速度传感器和MLF算法进行准确的跌倒检测

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983024

Anice Jahanjoo, M. Tahan, Mohammad J. Rashti

{"title":"Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm","authors":"Anice Jahanjoo, M. Tahan, Mohammad J. Rashti","doi":"10.1109/PRIA.2017.7983024","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983024","url":null,"abstract":"Nowadays, with the growing population of elderly people, the number of elderly without caregivers at home has also increased. It is clear that an elderly living alone at home is at higher risk of severe damage, due to potential delays in notifying caregivers and providing care at healthcare facilities. This especially becomes critical in case of high-risk incidents such as stroke or heart attack. To address this issue, an increasing number of methods have been proposed that employ various fall detection algorithms for elderly people. In this paper, we propose a new algorithm to detect falls, using a multi-level fuzzy min-max neural network. The proposed algorithm is compared with three other machine-learning algorithms (MLP, KNN, SVM). The main focus of this paper is on the effect of dimensionality reduction with using the Principal Component Analysis (PCA) method inside the proposed algorithm. The evaluations show that the multi-level fuzzy min-max neural network provides a high level of accuracy with a small number of dimensions. This is in contrast to the other algorithms, where accuracy is further lowered after applying dimensionality reduction. The performance evaluation of this algorithm on a public dataset obtained using accelerometer sensor data with using three dimensions indicates an accuracy of 97.29% for the sensitivity metric and 98.70% for the specifity metric.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"44 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421032","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: 37

Unknown aware k nearest neighbor classifier 未知的k近邻分类器

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983027

H. Khastavaneh, H. Ebrahimpour-Komleh, Amin Hanaee-Ahwaz

引用次数: 0

SAR image denoising using homomorphic and shearlet transforms 基于同态和剪切波变换的SAR图像去噪

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983022

Hossein Rezaei, A. Karami

引用次数: 7

Exploiting evidential theory in the fusion of textual, audio, and visual modalities for affective music video retrieval 利用证据理论融合文本，音频和视觉模式的情感音乐视频检索

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-19 DOI: 10.1109/PRIA.2017.7983051

Shahla Nemati, A. Naghsh-Nilchi

{"title":"Exploiting evidential theory in the fusion of textual, audio, and visual modalities for affective music video retrieval","authors":"Shahla Nemati, A. Naghsh-Nilchi","doi":"10.1109/PRIA.2017.7983051","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983051","url":null,"abstract":"Developing techniques to retrieve video contents with regard to their impact on viewers' emotions is the main goal of affective video retrieval systems. Existing systems mainly apply a multimodal approach that fuses information from different modalities to specify the affect category. In this paper, the effect of exploiting two types of textual information to enrich the audio-visual content of music video is evaluated; subtitles or songs' lyrics and texts obtained from viewers' comments in video sharing websites. In order to specify the emotional content of texts, an unsupervised lexicon-based method is applied. This method does not need any human-coded corpus for training and is much faster than supervised approach. In order to integrate these modalities, a new information fusion method is proposed based on the Dempster-Shafer theory of evidence. Experiments are conducted on the video clips of DEAP dataset and their associated viewers' comments on YouTube. Results show that incorporating songs' lyrics with the audio-visual content has no positive effect on the retrieval performance, whereas exploiting viewers' comments significantly improves the affective retrieval system. This could be justified by the fact that viewers' affective responses depend not only on the video itself but also on its context.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133530518","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: 12

Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping 用符号泊松映射分析颞叶癫痫海马形状

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983030

Mohammad Farazi, H. Soltanian-Zadeh

{"title":"Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping","authors":"Mohammad Farazi, H. Soltanian-Zadeh","doi":"10.1109/PRIA.2017.7983030","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983030","url":null,"abstract":"Complementary role of computer assisted models using machine learning methods in medical imaging has been a center of attention in recent years. Shape analysis of the brain structures can be used to evaluate their abnormalities and deformations, specifically in patients suffering from neurological diseases like epilepsy, Alzheimer, and Parkinson. We propose an automatic diagnosis and lateralization algorithm using Signed Poisson Mapping (SPoM), which has been recently proposed as a new framework for shape analysis of three-dimensional (3D) structures. In contrast to previous studies, we use a three-class classification to show the robustness of our algorithm in differentiating between normal, left temporal lobe epilepsy (LTLE), and right temporal lobe epilepsy (RTLE) subjects. We also use a support vector machine (SVM) classifier with a radial basic function (RBF) kernel for lateralization, i.e., differentiating between RTLE and LTLE patients. The classification accuracy for the three-class classifier is 94% and for the lateralization task is 95% which is superior to those reported in the related literature.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"21 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125685302","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

Combination of score level fusion methods in receiver operating characteristic space 接收机工作特征空间中分数水平融合方法的组合

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983025

Elham Sedighi, M. Analoui

{"title":"Combination of score level fusion methods in receiver operating characteristic space","authors":"Elham Sedighi, M. Analoui","doi":"10.1109/PRIA.2017.7983025","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983025","url":null,"abstract":"In fingerprint verification systems impressive improvements have been achieved through multi sample fusion methods. Among fusion methods, score level fusion with its simplicity and high performance is the most common and useful fusion method. But the quality of fingerprints has direct effect on performance and accuracy of these systems. In this paper, we present a combination approach in Receiver Operating Characteristic space using Support Vector Machine to combine score level fusion methods on multi sample fingerprints with small sample sizes. This approach uses False Match Rate from genuine class and True Match Rate from impostor class as one feature on training. For testing based on Bayesian decision theory one of the FMR or TMR is selected as a feature. We compared our combination approach with score fusion methods and combination based on FMR and FNMR as two features. The experimental results on Iran University of Science and Technology fingerprint Database show that the proposed approach with no need to normalization has doubled the distance between distribution of two classes and the accuracy has been improved to 0.997 for Equal Error Rate 0.129.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134158083","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