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

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Texture classification using dominant wavelet packet energy features 基于优势小波包能量特征的纹理分类
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839620
Moon-Chuen Lee, Chi-Man Pun
{"title":"Texture classification using dominant wavelet packet energy features","authors":"Moon-Chuen Lee, Chi-Man Pun","doi":"10.1109/IAI.2000.839620","DOIUrl":"https://doi.org/10.1109/IAI.2000.839620","url":null,"abstract":"This paper proposes a high performance texture classification method using dominant energy features from wavelet packet decomposition. We decompose the texture images with a family of real orthonormal wavelet bases and compute the energy signatures using the wavelet packet coefficients. Then we select a few of the most dominant energy values as features and employ a Mahalanobis distance classifier to classify a set of distinct natural textures selected from the Brodatz album. In our experiments, the proposed method employed a reduced feature set and involved less computation in classification time while still archiving high accuracy rate (94.8%) for classifying twenty classes of natural texture images.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"7 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":"123540423","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: 41
Non-invasive 3D dynamic object analysis 无创三维动态对象分析
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839602
Karl J. Sharman, M. Nixon, J. Carter
{"title":"Non-invasive 3D dynamic object analysis","authors":"Karl J. Sharman, M. Nixon, J. Carter","doi":"10.1109/IAI.2000.839602","DOIUrl":"https://doi.org/10.1109/IAI.2000.839602","url":null,"abstract":"A non-invasive system is required to obtain three-dimensional subject extraction and description for recognition by gait. Of current three-dimensional systems, multi-view approaches appear to be the most suitable. To handle not only concavities, but also noise and occlusion, the volume intersection approach has been formulated as an evidence gathering process for moving object extraction. Results on synthetic imagery show that the technique does indeed process a multi-view image sequence to derive parameters of interest as such giving a suitable basis for development as a marker-less gait analysis system.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"54 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":"126419046","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
Edge-retaining asymptotic projections onto convex sets for image interpolation 凸集上的渐近投影，用于图像插值
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839575
L. DeBrunner, V. DeBrunner, Minghua Yao
{"title":"Edge-retaining asymptotic projections onto convex sets for image interpolation","authors":"L. DeBrunner, V. DeBrunner, Minghua Yao","doi":"10.1109/IAI.2000.839575","DOIUrl":"https://doi.org/10.1109/IAI.2000.839575","url":null,"abstract":"We propose the concept of asymptotic projections onto convex sets (APOCS) in general and a wavelet (WT)-based, edge-retaining asymptotic POCS (ERAPOCS) algorithm for image interpolation in particular. APOCS differs from POCS in that the projections sequence in one (or more) of the convex sets can enter a desired region rapidly. Like POCS, our proposed algorithm is guaranteed to converge. Our new algorithm alleviates edge blurring by properly amplifying the WT of the image. The amplification in the WT domain biases the projection sequence to the subset of interpolated images that has less edge-bearing. Simulations show that our proposed algorithm performs better in the sense of PSNR and preserves the sharpness of the edges better than do the cubic interpolation and the POCS algorithms. In addition, our algorithm is more computationally efficient than the POCS algorithm since it converges in fewer iterations and has the same computational complexity.","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":"127586353","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: 8
An enhanced neural system for biomedical image classification 一种用于生物医学图像分类的增强神经系统
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839588
S. D. Bona, O. Salvetti
{"title":"An enhanced neural system for biomedical image classification","authors":"S. D. Bona, O. Salvetti","doi":"10.1109/IAI.2000.839588","DOIUrl":"https://doi.org/10.1109/IAI.2000.839588","url":null,"abstract":"Comparison and classification of images obtained from a single or more patients, at different times but with the same procedure, is important in evaluating the origin or the degree of several pathologies. As well, image classification fusing data acquired from different sources is often needed to locate regions or volumes, to analyse complex scenes or to simulate a diagnosis prediction. In this paper we present an enhanced neural system able to locate and classify tissue densitometric alterations in CT/MR image sequences; such a system has been optimised in order to reduce the computational complexity and the computational time.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"418 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":"134497079","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
Morphological pyramid image registration 形态金字塔图像配准
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839604
Zhongxiu Hu, S. Acton
{"title":"Morphological pyramid image registration","authors":"Zhongxiu Hu, S. Acton","doi":"10.1109/IAI.2000.839604","DOIUrl":"https://doi.org/10.1109/IAI.2000.839604","url":null,"abstract":"We propose an intensity-based morphological pyramid image registration algorithm. This approach utilizes the global affine transformation model, also considering radiometric changes between images. With the morphological pyramid structure, Levenberg-Marquardt optimization, and bilinear interpolation, this algorithm can be implemented hierarchically and iteratively with capability of measuring, to subpixel accuracy, the displacement between images subjected to simultaneous translation, rotation, scaling, and shearing. The morphological pyramid shows better performance than the Gaussian pyramid in this matching technique.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"27 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":"133573408","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: 11
Computer assisted landmarking of cephalometric radiographs 头颅x线片的计算机辅助标记
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839619
M. Desvignes, B. Romaniuk, R. Demoment, M. Revenu, M. Deshayes
{"title":"Computer assisted landmarking of cephalometric radiographs","authors":"M. Desvignes, B. Romaniuk, R. Demoment, M. Revenu, M. Deshayes","doi":"10.1109/IAI.2000.839619","DOIUrl":"https://doi.org/10.1109/IAI.2000.839619","url":null,"abstract":"We address the problem of finding an initial estimation of the location of landmarks on an image, when the landmarks are difficult to distinguish on the image and when the locations are dependent together from external forces such as growth. Our method solves the problem using an adaptive coordinate space where locations are registered. In this space, variability is greatly reduced. A training set is observed to build automatically a mean and a variability model of the landmarks. This model is used to predict the initial estimation on a new image. This method is applied to the difficult problem of the interpretation of cephalograms, with good results.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"7 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":"126639671","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
Two-dimensional phase unwrapping using neural networks 使用神经网络的二维相位展开
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839615
W. Schwartzkopf, T. Milner, Joydeep Ghosh, B. Evans, A. Bovik
{"title":"Two-dimensional phase unwrapping using neural networks","authors":"W. Schwartzkopf, T. Milner, Joydeep Ghosh, B. Evans, A. Bovik","doi":"10.1109/IAI.2000.839615","DOIUrl":"https://doi.org/10.1109/IAI.2000.839615","url":null,"abstract":"Imaging systems that construct an image from phase information in received signals include synthetic aperture radar (SAR) and optical Doppler tomography (ODT) systems. A fundamental problem in the image formation is phase ambiguity, i.e., it is impossible to distinguish between phases that differ by 2/spl pi/. Phase unwrapping in two dimensions essentially consists of detecting the pixel locations of the phase discontinuities, finding an ordering among the pixel locations for unwrapping the phase, and adding offsets of multiples of 2/spl pi/. In this paper, we propose a new method for detecting phase discontinuities. The method is based on a supervised feedforward multilayer perceptron neural network. We train and test the neural network on simulated phase images formed in an ODT system. For the ODT phase images, the new method detects the correct unwrapping locations where some conventional methods fail. The key contribution of the paper is a one-pass pixel-parallel low-complexity method for detecting phase discontinuities.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"13 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":"126678521","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: 116
3D reconstruction of an urban scene from synthetic fish-eye images 从合成鱼眼图像三维重建城市场景
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839603
Huaibin Zhao, J. Aggarwal
{"title":"3D reconstruction of an urban scene from synthetic fish-eye images","authors":"Huaibin Zhao, J. Aggarwal","doi":"10.1109/IAI.2000.839603","DOIUrl":"https://doi.org/10.1109/IAI.2000.839603","url":null,"abstract":"3D scene reconstruction from 2D stereo pairs or sequences has been an interesting issue in computer vision for years. So far, most research has used images acquired by linear low-distortion lenses that can be modeled by pinhole mapping. In recent years, the fisheye lens has received increasing attention and use due to its extremely wide field of view (FOV). However, little has been published on stereo reconstruction of fish-eye images. In this paper, we develop an algorithm to compute 3D structure of feature points from correspondences in fisheye pairs, and identify the problems remaining to be solved. Test results on synthetic data show the efficacy of our algorithm.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"31 9 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":"116782134","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
Bayesian tree-structured image modeling 贝叶斯树结构图像建模
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839605
J. Romberg, Hyeokho Choi, Richard Baraniuk
{"title":"Bayesian tree-structured image modeling","authors":"J. Romberg, Hyeokho Choi, Richard Baraniuk","doi":"10.1109/IAI.2000.839605","DOIUrl":"https://doi.org/10.1109/IAI.2000.839605","url":null,"abstract":"Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). In this paper, we propose two reduced-parameter HMT models that capture the general structure of a broad class of grayscale images. The image HMT (iHMT) model leverages the fact that for a large class of images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters (independent of the size of the image and the number of wavelet scales). In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of image estimation/denoising experiments that these two new models retain nearly all of the key structures modeled by the full HMT. Based on these new models, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"98 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":"114698502","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: 21
Spectroscopic imaging using terahertz time-domain signals 使用太赫兹时域信号的光谱成像
4th IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2000-04-02 DOI: 10.1109/IAI.2000.839590
T. Dorney, Richard Baraniuk, D. Mittleman, R. Nowak
{"title":"Spectroscopic imaging using terahertz time-domain signals","authors":"T. Dorney, Richard Baraniuk, D. Mittleman, R. Nowak","doi":"10.1109/IAI.2000.839590","DOIUrl":"https://doi.org/10.1109/IAI.2000.839590","url":null,"abstract":"Imaging systems based on terahertz time-domain spectroscopy offer a range of unique modalities due to the broad bandwidth, sub-picosecond duration, and phase-sensitive detection of the terahertz pulses. Furthermore, an exciting possibility exists to combine spectroscopic characterization and/or identification with imaging because the radiation is broadband in nature. In order to achieve this, novel methods for real-time analysis of terahertz waveforms are required. Unfortunately, both the absorption and the phase delay of a transmitted terahertz pulse vary exponentially with the sample's thickness. We describe a robust algorithm for extracting both the thickness and the complex index of refraction of an unknown sample. In contrast, most spectroscopic transmission measurements require accurate knowledge of the sample's thickness to determine the optical parameters. We also investigate the limits of our method.","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":"129717333","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
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