CVGIP: Graphical Models and Image Processing最新文献_第9页

Quasi-circular Splines: A Shape-Preserving Approximation 拟圆样条:一种保形逼近

CVGIP: Graphical Models and Image Processing Pub Date : 1993-03-01 DOI: 10.1006/cgip.1993.1007

Howell G.W., Fausett D.W., Fausett L.V.

引用次数: 2

The Nonlinear Prefiltering and Difference of Estimates Approaches to Edge Detection: Applications of Stack Filters 边缘检测的非线性预滤波和差分估计方法:堆栈滤波器的应用

CVGIP: Graphical Models and Image Processing Pub Date : 1993-03-01 DOI: 10.1006/cgip.1993.1010

Yoo J., Bouman C.A., Delp E.J., Coyle E.J.

{"title":"The Nonlinear Prefiltering and Difference of Estimates Approaches to Edge Detection: Applications of Stack Filters","authors":"Yoo J., Bouman C.A., Delp E.J., Coyle E.J.","doi":"10.1006/cgip.1993.1010","DOIUrl":"10.1006/cgip.1993.1010","url":null,"abstract":"<div><p>The theory of stack filtering, which is a generalization of median filtering, is used in two different approaches to the detection of intensity edges in noisy images. The first approach is a generalization of median prefiltering: a stack filter or another median-type filter is used to smooth an image before a standard gradient estimator is applied. These prefiltering schemes retain the robustness of the median prefilter, but allow resolution of finer detail. The second approach, called the Difference of Estimates (DoE) approach, is a new formulation of a morphological scheme [Lee <em>et al., IEEE Trans. Robotics Automat</em>. <strong>RA-3,</strong> Apr. 1987, 142-156, Maragos and Ziff, <em>IEEE Trans. Pattern Anal. Mach. Intell</em>. <strong>12</strong>(5), May 1990.] which has proven to be very sensitive to impulsive noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the edge map. We find, for example, that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, but works much better when the noise is impulsive. In both approaches, the stack filters employed are trained to be optimal on images and noise that are \"typical\" examples of the target image. The robustness of stack filters leads to good performance for the target image, even when the statistics of the noise and/or image vary from those used in training. This is verified with extensive simulations.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 2","pages":"Pages 140-159"},"PeriodicalIF":0.0,"publicationDate":"1993-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132564174","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: 25

Codebook Edge Detection 码本边缘检测

CVGIP: Graphical Models and Image Processing Pub Date : 1993-01-01 DOI: 10.1006/cgip.1993.1003

Mclean G.F.

引用次数: 2

Holes and Genus of 2D and 3D Digital Images 二维和三维数字图像的孔和属

CVGIP: Graphical Models and Image Processing Pub Date : 1993-01-01 DOI: 10.1006/cgip.1993.1002

Lee C.N., Poston T., Rosenfeld A.

引用次数: 42

Gibbs Random Fields, Fuzzy Clustering, and the Unsupervised Segmentation of Textured Images 吉布斯随机场，模糊聚类和纹理图像的无监督分割

CVGIP: Graphical Models and Image Processing Pub Date : 1993-01-01 DOI: 10.1006/cgip.1993.1001

Nguyen H.H., Cohen P.

{"title":"Gibbs Random Fields, Fuzzy Clustering, and the Unsupervised Segmentation of Textured Images","authors":"Nguyen H.H., Cohen P.","doi":"10.1006/cgip.1993.1001","DOIUrl":"10.1006/cgip.1993.1001","url":null,"abstract":"<div><p>In this paper we present an unsupervised segmentation strategy for textured images, based on a hierarchical model in terms of discrete Markov Random Fields. The textures are modeled as Gaussian Gibbs Fields, while the image partition is modeled as a Markov Mesh Random Field. The segmentation is achieved in two phases: the first one consists of evaluating, from disjoint blocks which are classified as homogeneous, the model parameters for each texture present in the image. This unsupervised learning phase uses a fuzzy clustering procedure, applied to the features extracted from every pixel block, to determine the number of textures in the image and to roughly locate the corresponding regions. The second phase consists of the fine segmentation of the image, using Bayesian local decisions based on the previously obtained model parameters. The originality of the proposed approach lies in the three following aspects: (1) the Gibbs distribution corresponding to each texture type is expressed in terms of its <em>canonical</em> potential. This formulation leads to a compact formulation of the global field energy, in terms of the marginal probabilities over pixel cliques. A similar expression is also introduced in the partition model. Such formulations lead to the decomposition of the segmentation problem into a set of local statistical decisions; (2) the segmentation strategy consists of an unsupervised estimation, in which the model parameters are evaluated directly from the observation, by means of a fuzzy clustering technique; (3) no arbitrary assumption is made concerning the number of textures present. Rather, the fuzzy clustering procedure used to estimate the model parameters is applied in a hierarchical manner, searching for a cluster configuration of maximum plausibility.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 1","pages":"Pages 1-19"},"PeriodicalIF":0.0,"publicationDate":"1993-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133442370","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: 45

An Extension of Chaiken′s Algorithm to B-Spline Curves with Knots in Geometric Progression Chaiken算法在几何级数中带结点b样条曲线上的推广

CVGIP: Graphical Models and Image Processing Pub Date : 1993-01-01 DOI: 10.1006/cgip.1993.1004

Goldman R., Warren J.

引用次数: 10

Local Image Reconstruction and Subpixel Restoration Algorithms 局部图像重建和亚像素恢复算法

CVGIP: Graphical Models and Image Processing Pub Date : 1993-01-01 DOI: 10.1006/cgip.1993.1005

Boult T.E., Wolberg G.

{"title":"Local Image Reconstruction and Subpixel Restoration Algorithms","authors":"Boult T.E., Wolberg G.","doi":"10.1006/cgip.1993.1005","DOIUrl":"10.1006/cgip.1993.1005","url":null,"abstract":"<div><p>This paper introduces a new class of reconstruction algorithms that are fundamentally different from traditional approaches. We deviate from the standard practice that treats images as point samples. In this work, image values are treated as area samples generated by nonoverlapping integrators. This is consistent with the image formation process, particularly for CCD and CID cameras. We show that superior results are obtained by formulating reconstruction as a two-stage process: image restoration followed by application of the point spread function (PSF) of the imaging sensor. By coupling the PSF to the reconstruction process, we satisfy a more intuitive fidelity measure of accuracy that is based on the physical limitations of the sensor. Efficient local techniques for image restoration are derived to invert the effects of the PSF and estimate the underlying image that passed through the sensor. The reconstruction algorithms derived herein are local methods that compare favorably to cubic convolution, a well-known local technique, and they even rival global algorithms such as interpolating cubic splines. Evaluations are made by comparing their passband and stopband performances in the frequency domain, as well as by direct inspection of the resulting images in the spatial domain. A secondary advantage of the algorithms derived with this approach is that they satisfy an imaging-consistency property. This means that they exactly reconstruct the image for some function in the given class of functions. Their error can be shown to be at most twice that of the \"optimal\" algorithm for a wide range of optimality constraints.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 1","pages":"Pages 63-77"},"PeriodicalIF":0.0,"publicationDate":"1993-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126074768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

Fast generation and display of iso-surface wireframes 快速生成和显示等面线框

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90067-8

Michael J Laszlo

引用次数: 14

Author index for volume 54 第54卷作者索引

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90073-7

引用次数: 0

Page segmentation and classification 页面分割与分类

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90068-9

Theo Pavlidis, Jiangying Zhou

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引用次数: 291