2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)最新文献

PFBIK-tracking: Particle filter with bio-inspired keypoints tracking PFBIK-tracking:具有仿生关键点跟踪的粒子过滤器

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013280

S. Filipe, Luís A. Alexandre

{"title":"PFBIK-tracking: Particle filter with bio-inspired keypoints tracking","authors":"S. Filipe, Luís A. Alexandre","doi":"10.1109/CIMSIVP.2014.7013280","DOIUrl":"https://doi.org/10.1109/CIMSIVP.2014.7013280","url":null,"abstract":"In this paper, we propose a robust detection and tracking method for 3D objects by using keypoint information in a particle filter. Our method consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation is made in order to remove all the background information, in order to reduce the number of points for further processing. In the initialization, we use a keypoint detector with biological inspiration. The information of the object that we want to follow is given by the extracted keypoints. The particle filter does the tracking of the keypoints, so with that we can predict where the keypoints will be in the next frame. In a recognition system, one of the problems is the computational cost of keypoint detectors with this we intend to solve this problem. The experiments with PFBIK-Tracking method are done indoors in an office/home environment, where personal robots are expected to operate. The Tracking Error evaluate the stability of the general tracking method. We also quantitatively evaluate this method using a “Tracking Error”. Our evaluation is done by the computation of the keypoint and particle centroid. Comparing our system with the tracking method which exists in the Point Cloud Library, we archive better results, with a much smaller number of points and computational time. Our method is faster and more robust to occlusion when compared to the OpenniTracker.","PeriodicalId":210556,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127087359","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

Unsupervised multiobjective design for weighted median filters using genetic algorithm 遗传算法加权中值滤波器的无监督多目标设计

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013281

Y. Hanada, Y. Orito

引用次数: 0

Self-localization method for three-dimensional handy scanner using multi spot laser 多光斑激光三维手持扫描仪的自定位方法

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013289

Kumiko Yoshida, K. Kawasue

引用次数: 1

Multivariate PDF matching via kernel density estimation 基于核密度估计的多元PDF匹配

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013285

D. Fantinato, L. Boccato, R. Attux, A. Neves

{"title":"Multivariate PDF matching via kernel density estimation","authors":"D. Fantinato, L. Boccato, R. Attux, A. Neves","doi":"10.1109/CIMSIVP.2014.7013285","DOIUrl":"https://doi.org/10.1109/CIMSIVP.2014.7013285","url":null,"abstract":"In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.","PeriodicalId":210556,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129097141","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: 10

Automatic tumor lesion detection and segmentation using histogram-based gravitational optimization algorithm 基于直方图的重力优化算法的肿瘤病灶自动检测与分割

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013271

Nooshin Nabizadeh, Mohsen Dorodchi

引用次数: 1

Clustering and visualization of geodetic array data streams using self-organizing maps 使用自组织地图的大地测量阵列数据流聚类和可视化

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013290

R. Popovici, Răzvan Andonie, W. Szeliga, T. Melbourne, C. Scrivner

{"title":"Clustering and visualization of geodetic array data streams using self-organizing maps","authors":"R. Popovici, Răzvan Andonie, W. Szeliga, T. Melbourne, C. Scrivner","doi":"10.1109/CIMSIVP.2014.7013290","DOIUrl":"https://doi.org/10.1109/CIMSIVP.2014.7013290","url":null,"abstract":"The Pacific Northwest Geodesic Array at Central Washington University collects telemetered streaming data from 450 GPS stations. These real-time data are used to monitor and mitigate natural hazards arising from earthquakes, volcanic eruptions, landslides, and coastal sea-level hazards in the Pacific Northwest. Recent improvements in both accuracy of positioning measurements and latency of terrestrial data communication have led to the ability to collect data with higher sampling rates. For seismic monitoring applications, this means 1350 separate position streams from stations located across 1200 km along the West Coast of North America must be able to be both visually observed and automatically analyzed at a sampling rate of up to 1 Hz. Our goal is to efficiently extract and visualize useful information from these data streams. We propose a method to visualize the geodetic data by clustering the signal types with a Self-Organizing Map (SOM). The similarity measure in the SOM is determined by the similarity of signals received from GPS stations. Signals are transformed to symbol strings, and the distance measure in the SOM is defined by an edit distance. The symbol strings represent data streams and the SOM is dynamic. We overlap the resulted dynamic SOM on the Google Maps representation.","PeriodicalId":210556,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123361294","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

Incremental semi-supervised fuzzy clustering for shape annotation 形状标注的增量半监督模糊聚类

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013291

G. Castellano, A. Fanelli, M. Torsello

引用次数: 1

A multi-modal moving object detection method based on GrowCut segmentation 基于GrowCut分割的多模态运动目标检测方法

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013295

Xiuwei Zhang, Yanning Zhang, S. Maybank, Jun Liang

引用次数: 3

Fast overcomplete topographical independent component analysis (FOTICA) and its implementation using GPUs 快速过完备地形独立分量分析(FOTICA)及其gpu实现

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013293

Chao-Hui Huang

引用次数: 0

Improving codebook generation for action recognition using a mixture of Asymmetric Gaussians 使用非对称高斯混合改进动作识别的码本生成

2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) Pub Date : 2014-12-01 DOI: 10.1109/CIMSIVP.2014.7013267

Tarek Elguebaly, N. Bouguila

{"title":"Improving codebook generation for action recognition using a mixture of Asymmetric Gaussians","authors":"Tarek Elguebaly, N. Bouguila","doi":"10.1109/CIMSIVP.2014.7013267","DOIUrl":"https://doi.org/10.1109/CIMSIVP.2014.7013267","url":null,"abstract":"Human activity recognition is a crucial area of computer vision research and applications. The goal of human activity recognition aims to automatically analyze and interpret ongoing events and their context from video data. Recently, the bag of visual words (BoVW) approach has been widely applied for human action recognition. Generally, a representative corpus of videos is used to build the Visual Words dictionary or codebook using a simple k-means clustering approach. This visual dictionary is then used to quantize the extracted features by simply assigning the label of the closest cluster centroid using Euclidean distance between the cluster centers and the input descriptor. Thus, each video can be represented as a frequency histogram over visual words. However, the BoVW approach has several limitations such as its need for a predefined codebook size, dependence on the chosen set of visual words, and the use of hard assignment clustering for histogram creation. In this paper, we are trying to overcome these issues by using a mixture of Asymmetric Gaussians to build the codebook. Our method is able to identify the best size for our dictionary in an unsupervised manner, to represent the set of input feature vectors by an estimate of their density distribution, and to allow soft assignments. Furthermore, we validate the efficiency of the proposed algorithm for human action recognition.","PeriodicalId":210556,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134276392","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