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4th IEEE Southwest Symposium on Image Analysis and Interpretation最新文献

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Robust image watermarking using inversely proportional embedding 基于反比嵌入的鲁棒图像水印
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839596
Te-shen Liang, Jeffrey J. Rodríguez
{"title":"Robust image watermarking using inversely proportional embedding","authors":"Te-shen Liang, Jeffrey J. Rodríguez","doi":"10.1109/IAI.2000.839596","DOIUrl":"https://doi.org/10.1109/IAI.2000.839596","url":null,"abstract":"In just a few years, robustly embedding an imperceptible watermark into a digital image for the purpose of ownership verification and copy control, has been widely investigated and studied. One of the remaining challenges for robust image watermarking is to design secure encoding/decoding schemes that reliably verify the embedded but possibly corrupted watermark. In this paper, we introduce a modified embedding scheme that can render more reliable watermark detection compared to some conventional image watermarking approaches. We first study common problems inherent in traditional watermarking, and then propose a better embedding rule as the remedy. Experimental results demonstrate that the new embedding rule enhances the performance of existing watermarking schemes for different capacities/payloads and under various types of common image processing.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131951279","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
Analysis of determining camera position via Karhunen-Loeve transform 利用Karhunen-Loeve变换确定摄像机位置的分析
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839577
P. Quick, D. Capson
{"title":"Analysis of determining camera position via Karhunen-Loeve transform","authors":"P. Quick, D. Capson","doi":"10.1109/IAI.2000.839577","DOIUrl":"https://doi.org/10.1109/IAI.2000.839577","url":null,"abstract":"The Karhunen-Loeve transform (KLT) can be used to compress sets of correlated visual data. Human faces and object recognition are popular areas of current research that use KLT-based methods. The KLT can also be used to compress visual data corresponding to a camera moved translationally and/or rotationally relative to a scene. Positioning of a camera relative to a scene can then be derived accurately using KLT feature vectors; this finds application in robotics and autonomous navigation. Various factors affect the accuracy and speed of such position determination including the number of KLT vectors used, the number of images used to perform the KLT, the number of images used in the comparison set and the size of the movement range. This paper investigates the performance of the KLT with a series of experiments determining a camera's rotational position relative to a generic laboratory scene.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130856232","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
A new Bayesian relaxation framework for the estimation and segmentation of multiple motions 一种新的多运动估计和分割的贝叶斯松弛框架
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839564
A. Strehl, J. Aggarwal
{"title":"A new Bayesian relaxation framework for the estimation and segmentation of multiple motions","authors":"A. Strehl, J. Aggarwal","doi":"10.1109/IAI.2000.839564","DOIUrl":"https://doi.org/10.1109/IAI.2000.839564","url":null,"abstract":"In this paper we propose a new probabilistic relaxation framework to perform robust multiple motion estimation and segmentation from a sequence of images. Our approach uses displacement information obtained from tracked features or raw sparse optical flow to iteratively estimate multiple motion models. Each iteration consists of a segmentation and a motion parameter estimation step. The motion models are used to compute probability density functions for all displacement vectors. Based on the estimated probabilities a pixel-wise segmentation decision is made by a Bayesian classifier which is optimal in respect to minimum error. The updated segmentation then relaxes the motion parameter estimates. These two steps are iterated until the error of the fitted models is minimized. The Bayesian formulation provides a unified probabilistic framework for various motion models and induces inherent robustness through its rejection mechanism. An implementation of the proposed framework using translational and affine motion models is presented. Its superior performance on real image sequences containing multiple and fragmented motions is demonstrated.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129984711","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: 13
Statistical gait description via temporal moments 基于时间矩的统计步态描述
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839618
J. Shutler, M. Nixon, Christopher J. Harris
{"title":"Statistical gait description via temporal moments","authors":"J. Shutler, M. Nixon, Christopher J. Harris","doi":"10.1109/IAI.2000.839618","DOIUrl":"https://doi.org/10.1109/IAI.2000.839618","url":null,"abstract":"Statistical recognition techniques have already been shown to achieve good performance in automatic gait recognition. However, the metrics were only statistical in nature and did not describe the intimate nature of gait. Accordingly, new velocity moments have been developed to describe an object and its motion throughout an image sequence. These moments are an extended form of centralised moments and compute descriptions of the object and its behaviour evaluation shows that the velocity moments have the required descriptive capability and analysis on synthetic imagery shows that the velocity moments are less sensitive to noise than an averaged comparator moment. This is largely due to the integration of data from the whole sequence. An extraction procedure has been developed to find moving human subjects and we are currently evaluating the performance of this promising new approach in automatic gait recognition.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962218","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: 50
Analysis of the spectral properties of the Radon transform for the design of optimal sampling grids 分析Radon变换的光谱特性，设计最优采样网格
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839585
F. Boschen, A. Kummert
{"title":"Analysis of the spectral properties of the Radon transform for the design of optimal sampling grids","authors":"F. Boschen, A. Kummert","doi":"10.1109/IAI.2000.839585","DOIUrl":"https://doi.org/10.1109/IAI.2000.839585","url":null,"abstract":"The sampling theorem also known as Shannon theorem is the basis for digital signal processing and computer based algorithms. A great number of publications is devoted to sampling and reconstruction of signals. Spectral properties of the underlying continuous signals and different kinds of applications require a specific approach in designing an optimal sampling grid. For doing this, in the domain of multidimensional signal processing a greater degree of freedom can be utilized. In this paper the spectral properties of the projection signal of a tomograph with respect to the bandwidth is analysed and an optimal sampling grid for projection data is presented.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125014048","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
Unsupervised Dempster-Shafer fusion of dependent sensors 相关传感器的无监督Dempster-Shafer融合
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839609
W. Pieczynski
{"title":"Unsupervised Dempster-Shafer fusion of dependent sensors","authors":"W. Pieczynski","doi":"10.1109/IAI.2000.839609","DOIUrl":"https://doi.org/10.1109/IAI.2000.839609","url":null,"abstract":"This paper deals with the problem of statistical unsupervised fusion of dependent sensors with its potential applications to multisensor image segmentation. On the one hand, Bayesian fusions can be of great efficiency, particularly when using hidden Markov models. On the other hand, we give some examples showing that there are situations in which the Dempster-Shafer fusion can be usefully integrated into the classical Bayesian models. The contribution of this paper is then to show how a recent parameter estimation of probabilistic models, valid in the dependent and possible non-Gaussian sensors case, can be extended to situations in which some of the sensors can be evidential. The proposed method allows one to imagine different unsupervised segmentation methods, valid in the Dempster-Shafer framework for dependent and possibly non-Gaussian sensors.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"7 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128693502","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: 22
Content based retrieval for remotely sensed imagery 基于内容的遥感图像检索
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839592
Badrinarayan Raghunathan, S. Acton
{"title":"Content based retrieval for remotely sensed imagery","authors":"Badrinarayan Raghunathan, S. Acton","doi":"10.1109/IAI.2000.839592","DOIUrl":"https://doi.org/10.1109/IAI.2000.839592","url":null,"abstract":"We present a framework for content based retrieval (CBR) of remotely sensed imagery. The main focus of our research is the segmentation step in CBR. A bank of Gabor filters is used to extract regions of homogeneous texture. These filter responses are utilized in a multiscale clustering technique to yield the final segmentation. Novel area morphological filters are utilized for the purpose of scaling. The resultant segmentation yields regions that are homogeneous in terms of texture and are significant in terms of scale. These regions are used for the purpose of extracting shape and textural features (on a global and local basis) that provide important similarity cues in CBR of remotely sensed imagery. In comparison to solutions which use region merging, the segmentation from the texture/scale space does not require heuristic post-processing, nor knowledge of the number of significant regions.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127546844","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
Multi-level shape recognition based on wavelet-transform modulus maxima 基于小波变换模极大值的多级形状识别
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839562
F. A. Cheikh, A. Quddus, M. Gabbouj
{"title":"Multi-level shape recognition based on wavelet-transform modulus maxima","authors":"F. A. Cheikh, A. Quddus, M. Gabbouj","doi":"10.1109/IAI.2000.839562","DOIUrl":"https://doi.org/10.1109/IAI.2000.839562","url":null,"abstract":"In this paper we propose a new approach to shape recognition based on the wavelet transform modulus maxima, and we apply it to the problem of content-based indexing and retrieval of fish contours. The description scheme and the similarity measure developed take into consideration the way our visual system perceives objects and compares them. The proposed scheme is invariant to translation, rotation, scale change and to noise corruption. Moreover, this description scheme allows accurate reconstruction of the shape boundary from the feature vector used to describe it. The experimental results and comparisons show the performance of the proposed technique.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493940","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
Using moment invariants and HMM in facial expression recognition 矩不变量和HMM在面部表情识别中的应用
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839621
Y. Zhu, L. D. Silva, C. Ko
{"title":"Using moment invariants and HMM in facial expression recognition","authors":"Y. Zhu, L. D. Silva, C. Ko","doi":"10.1109/IAI.2000.839621","DOIUrl":"https://doi.org/10.1109/IAI.2000.839621","url":null,"abstract":"Moment invariants are invariant under shifting, scaling and rotation. They are widely used in pattern recognition because of their discrimination power and robustness. The HMM method is natural and highly reliable way of recognition. In this paper we propose a method for using moment invariants as features and HMM as the recognition method in facial expression recognition. Sequences of four universal expressions, i.e., anger, disgust, happiness and surprise, are recognised. We attain accuracy as high as 93.75%.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130631994","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: 123
A novel hierarchical wavelet-based framework for pattern analysis and synthesis 一种新的基于分层小波的模式分析与综合框架
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839608
C. Scott, R. Nowak
{"title":"A novel hierarchical wavelet-based framework for pattern analysis and synthesis","authors":"C. Scott, R. Nowak","doi":"10.1109/IAI.2000.839608","DOIUrl":"https://doi.org/10.1109/IAI.2000.839608","url":null,"abstract":"We present a wavelet-based framework for modeling patterns in digital images. The wavelet coefficients of the underlying pattern template are modeled as independent Gaussian or Gaussian mixture random variables. Variations in pose and location of the pattern are accounted for by a finite collection of uniformly distributed transformations. The observation noise is assumed to be IID Gaussian. This hierarchical framework induces a statistical image model that can be used to synthesize instances of pattern observations. The underlying pattern, which is generally unknown, can be inferred from training data by means of an iterative alternating-maximization algorithm. This learning algorithm automatically infers a pattern template with a sparse wavelet representation. We can further promote an efficient representation by modeling the wavelet coefficients with a Gaussian mixture and placing a penalty on the number of \"high\" states.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084604","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
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