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2006 IEEE International Conference on Video and Signal Based Surveillance最新文献

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A Novel Metric on Partitions for Image Segmentation 一种新的用于图像分割的分区度量
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.8
V. Mashtalir, E. Mikhnova, V. Shlyakhov, Elena Yegorova
{"title":"A Novel Metric on Partitions for Image Segmentation","authors":"V. Mashtalir, E. Mikhnova, V. Shlyakhov, Elena Yegorova","doi":"10.1109/AVSS.2006.8","DOIUrl":"https://doi.org/10.1109/AVSS.2006.8","url":null,"abstract":"The explosion of image content is closely connected with segmentations efficiency. However, there is no agreement as to what a good segmentation is due to hard data and applications dependence. To reduce the gap between low-level features and high-level semantic, collections of image partitions produced by different segmentation algorithms are often considered. We propose, theoretically ground and experimentally explore a new metric on segmented images or on arbitrary partitions of finite sets in general.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114004542","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
Steganographic Scheme for VQ Compressed Images Using Progressive Exponential Clustering 基于渐进指数聚类的VQ压缩图像隐写方案
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.107
Yue Li, Chang-Tsun Li
{"title":"Steganographic Scheme for VQ Compressed Images Using Progressive Exponential Clustering","authors":"Yue Li, Chang-Tsun Li","doi":"10.1109/AVSS.2006.107","DOIUrl":"https://doi.org/10.1109/AVSS.2006.107","url":null,"abstract":"In this work, we propose a steganographic scheme for embedding secret message in VQ-compressed images, which aims to achieve high embedding capacity while keeping distortion low. To achieve these objectives, we develop a progressive exponential clustering (PEC) algorithm for partitioning the VQ codebook into a set of clusters. To embed the secret message based on the clustering, we substitute the indexes of the codewords with others within the same cluster according to the size of the cluster and the secret message bits.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"4 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113932264","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: 7
Analysis of LIDAR Data Fused with Co-Registered Bands 融合共配波段激光雷达数据分析
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.24
M. Bartels, Hong Wei, J. Ferryman
{"title":"Analysis of LIDAR Data Fused with Co-Registered Bands","authors":"M. Bartels, Hong Wei, J. Ferryman","doi":"10.1109/AVSS.2006.24","DOIUrl":"https://doi.org/10.1109/AVSS.2006.24","url":null,"abstract":"In the past decade, LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sector as a reliable and accurate source for land surveying. Object classification in LIDAR data tends towards data fusion by employing additional simultaneously recorded bands. In this paper, a rule-based approach is presented for improving classification accuracy obtained in a supervised Maximum Likelihood classification. Simultaneously recorded co-registered bands are used such as high resolution LIDAR first, last echo and intensity data, aerial and near infra-red photos. Issues regarding feature and class selection and differentiated accuracy assessment are addressed. Furthermore, the individual influence of each band on the classification is investigated. The results show that incorporating additional knowledge and considering contextual relationships among classes is beneficial for improving classification accuracy in fused LIDAR datasets.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121258000","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
Improving the Speed of Kernel PCA on Large Scale Datasets 提高大规模数据集核主成分分析的速度
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.66
Tat-Jun Chin, D. Suter
{"title":"Improving the Speed of Kernel PCA on Large Scale Datasets","authors":"Tat-Jun Chin, D. Suter","doi":"10.1109/AVSS.2006.66","DOIUrl":"https://doi.org/10.1109/AVSS.2006.66","url":null,"abstract":"This paper concerns making large scale Kernel Principal Component Analysis (KPCA) feasible on regular hardware. The KPCA has been proven a useful non-linear feature extractor in several computer vision applications. The standard computation method for KPCA, however, scales badly with the problem size, thus limiting the potential of the technique for large scale data. We propose a novel method to alleviate this problem. The essence of our solution lies in partitioning the data and greedily filtering each partition for a sparse representation. Incremental KPCA is then utilized to merge each partition to arrive at the overall KPCA. We also provide experimental results which demonstrate the effectiveness of the approach.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116329141","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: 7
A Smith-Waterman Local Alignment Approach for Spatial Activity Recognition 空间活动识别的Smith-Waterman局部对齐方法
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.13
D.E. Riedel, S. Venkatesh, Wanquan Liu
{"title":"A Smith-Waterman Local Alignment Approach for Spatial Activity Recognition","authors":"D.E. Riedel, S. Venkatesh, Wanquan Liu","doi":"10.1109/AVSS.2006.13","DOIUrl":"https://doi.org/10.1109/AVSS.2006.13","url":null,"abstract":"In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"38 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116657073","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: 7
Object Contour Tracking in Videos by Matching Finite Mixture Models 基于匹配有限混合模型的视频目标轮廓跟踪
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.83
M. S. Allili, D. Ziou
{"title":"Object Contour Tracking in Videos by Matching Finite Mixture Models","authors":"M. S. Allili, D. Ziou","doi":"10.1109/AVSS.2006.83","DOIUrl":"https://doi.org/10.1109/AVSS.2006.83","url":null,"abstract":"In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328372","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
Visual Speech Recognition Method Using Translation, Scale and Rotation Invariant Features 基于平移、缩放和旋转不变性特征的视觉语音识别方法
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.118
W. C. Yau, D. Kumar, S. Arjunan
{"title":"Visual Speech Recognition Method Using Translation, Scale and Rotation Invariant Features","authors":"W. C. Yau, D. Kumar, S. Arjunan","doi":"10.1109/AVSS.2006.118","DOIUrl":"https://doi.org/10.1109/AVSS.2006.118","url":null,"abstract":"This paper reports on a visual speech recognition method that is invariant to translation, rotation and scale. Dynamic features representing the mouth motion is extracted from the video data by using a motion segmentation technique termed as motion history image (MHI). MHI is generated by applying accumulative image differencing technique on the sequence of mouth images. Invariant features are derived from the MHI using feature extraction algorithm that combines Discrete Stationary Wavelet Transform (SWT) and moments. A 2-D SWT at level one is applied to decompose MHI to produce one approximate and three detail sub images. The feature descriptors consist of three moments (geometric moments, Hu moments and Zernike moments) computed from the SWT approximate image. The moments features are normalized to achieve the invariance properties. Artificial neural network (ANN) with back propagation learning algorithm is used to classify the moments features. Initial experiments were conducted to test the sensitivity of the proposed approach to rotation, translation and scale of the mouth images and obtained promising results.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122008364","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
Global-to-Local Histogram Match Culling for Epipolar Geometry Estimation 极极几何估计的全局到局部直方图匹配剔除
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.54
M. Evans, J. Ferryman
{"title":"Global-to-Local Histogram Match Culling for Epipolar Geometry Estimation","authors":"M. Evans, J. Ferryman","doi":"10.1109/AVSS.2006.54","DOIUrl":"https://doi.org/10.1109/AVSS.2006.54","url":null,"abstract":"Estimation of the Epipolar Geometry and Fundamental Matrix for a pair of stereo images often begins with a stage of feature (corner) detection and correlation based feature matching. This generally results in a set of matches where features are matched multiple times and frequently incorrectly. This paper introduces a simple and fast histogram based technique for refining the set of matches prior to a final stage of robust Fundamental Matrix estimation that compares favourably to previously published techniques such as SVD and Relaxation.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"96 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124162630","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
Detecting Changes in Grey Level Sequences by ML Isotonic Regression ML等渗回归检测灰度序列变化
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.40
A. Lanza, L. D. Stefano
{"title":"Detecting Changes in Grey Level Sequences by ML Isotonic Regression","authors":"A. Lanza, L. D. Stefano","doi":"10.1109/AVSS.2006.40","DOIUrl":"https://doi.org/10.1109/AVSS.2006.40","url":null,"abstract":"We present a robust and efficient change detection algorithm for grey-level sequences. A deep investigation of the effects of disturbance factors (illumination changes and automatic or manual adjustments of the camera transfer function, such as AGC, AE and gamma-correction) on image brightness allows to assume locally an order-preservation of pixel intensities. By a simple statistical modelling of camera noise, an ML isotonic regression procedure can thus be applied to perform change detection. Although the proposed approach may be used as a stand-alone pixel-level change detector, here we apply it to reduced-resolution images. In fact, we aim at using the algorithm as the coarse-level of a coarse-to-fine change detector we presented in [2].","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131318069","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: 14
Pedestrian Detection and Tracking for Counting Applications in Crowded Situations 行人检测与跟踪在拥挤情况下的计数应用
2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.91
O. Sidla, Y. Lypetskyy, Norbert Brändle, S. Seer
{"title":"Pedestrian Detection and Tracking for Counting Applications in Crowded Situations","authors":"O. Sidla, Y. Lypetskyy, Norbert Brändle, S. Seer","doi":"10.1109/AVSS.2006.91","DOIUrl":"https://doi.org/10.1109/AVSS.2006.91","url":null,"abstract":"This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 % can be achieved.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128215124","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: 108
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