CVGIP: Graphical Models and Image Processing最新文献_第10页

Image contrast enhancement based on the intensities of edge pixels 基于边缘像素强度的图像对比度增强

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90069-A

Jia-Guu Leu

{"title":"Image contrast enhancement based on the intensities of edge pixels","authors":"Jia-Guu Leu","doi":"10.1016/1049-9652(92)90069-A","DOIUrl":"10.1016/1049-9652(92)90069-A","url":null,"abstract":"<div><p>Histogram modification can improve the contrast of an image. Histogram equalization has been the most popular histogram modification technique. However, the technique has the tendency to magnify local noise for images with large homogeneous regions. In this paper we suggest a new histogram modification technique which utilizes the intensity distribution of the edge pixels of an image. We first identify the edge pixels of an image. Then the intensity histogram of the edge pixels is constructed. An intensity transformation function is derived from the edge-pixel histogram and then applied to the entire image. In general, this transformation will increase the intensity difference between neighboring homogeneous regions. We also have suggested three tools to measure the performance of contrast-enhancing methods. The three measurements are image contrast value, image information loss value, and local intensity variance value. Our goal for enhancing is to significantly increase an image's contrast value while keeping both the information loss value and the local intensity variance value low. In the experiments, we have compared the performance of the suggested method with that of the ordinary histogram equalization technique and the local area histogram equalization (LAHE) technique using both synthetic and real images. The results were then evaluated by the three tools. The suggested method performed very well both analytically and visually.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1992-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1049-9652(92)90069-A","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283094","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: 30

The vector distance transform in two and three dimensions 矢量距离在二维和三维中变换

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90072-6

James C Mullikin

引用次数: 125

A generalization of dynamic constraints 动态约束的推广

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90071-5

John Platt

引用次数: 32

On edge preservation in multiresolution images 多分辨率图像的边缘保存

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90066-7

Ze-Nian Li, Gongzhu Hu

引用次数: 12

Discrete multidimensional Jordan surfaces 离散多维约当曲面

CVGIP: Graphical Models and Image Processing Pub Date : 1992-11-01 DOI: 10.1016/1049-9652(92)90070-E

Gabor T Herman

引用次数: 87

Generating skeletons and centerlines from the distance transform 从距离变换生成骨架和中心线

CVGIP: Graphical Models and Image Processing Pub Date : 1992-09-01 DOI: 10.1016/1049-9652(92)90026-T

C.Wayne Niblack, Phillip B Gibbons, David W Capson

{"title":"Generating skeletons and centerlines from the distance transform","authors":"C.Wayne Niblack, Phillip B Gibbons, David W Capson","doi":"10.1016/1049-9652(92)90026-T","DOIUrl":"10.1016/1049-9652(92)90026-T","url":null,"abstract":"<div><p>We describe an algorithm for generating connected skeletons of objects in a binary image. The algorithm combines essentially all desirable properties of a skeletonization method: (1) the skeletons it produces have the same simple connectivity as the objects; it is based on a distance transform and can use any “natural” distance metric (in particular those giving a good approximation to the Euclidean distance), resulting in skeletons that are both (2) well-centered and (3) robust with respect to rotation; the skeletons allow the objects to be reconstructed either (4) exactly or (5) approximately to within a specified error; (6) for approximate reconstruction, the skeletons are insensitive to “border noise” without image prefiltering or skeleton postpruning; (7) the skeletons can be thin; (8) the algorithm is fast, taking a fixed number of passes through the image regardless of the width of the objects; and (9) the skeletons have a pleasing visual appearance. Several of these properties may conflict. For example, skeletons cannot always be both thin and allow exact reconstruction and our algorithm can be run to give priority to either property. This paper describes the skeletonization algorithm, discusses the tradeoffs involved and summarizes the formal proofs of its connectivity and reconstructability properties. Because the algorithm is fast, robust, flexible, and provably correct, it is ideally suited for many of the applications of skeletonization—data compression, OCR, shape representation and binary image analysis. The quality of the skeletons produced is demonstrated with numerous examples.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1992-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1049-9652(92)90026-T","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825926","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: 165

A survey of moment-based techniques for unoccluded object representation and recognition 基于矩的非包含对象表示与识别技术综述

CVGIP: Graphical Models and Image Processing Pub Date : 1992-09-01 DOI: 10.1016/1049-9652(92)90027-U

Richard J Prokop, Anthony P Reeves

引用次数: 640

Texture classification using QMF bank-based subband decomposition 基于QMF库子带分解的纹理分类

CVGIP: Graphical Models and Image Processing Pub Date : 1992-09-01 DOI: 10.1016/1049-9652(92)90022-P

Amlan Kundu , Jia-Lin Chen

引用次数: 66

Statistical feature matrix for texture analysis 纹理分析的统计特征矩阵

CVGIP: Graphical Models and Image Processing Pub Date : 1992-09-01 DOI: 10.1016/1049-9652(92)90025-S

Chung-Ming Wu, Yung-Chang Chen

{"title":"Statistical feature matrix for texture analysis","authors":"Chung-Ming Wu, Yung-Chang Chen","doi":"10.1016/1049-9652(92)90025-S","DOIUrl":"https://doi.org/10.1016/1049-9652(92)90025-S","url":null,"abstract":"<div><p>A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major properties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1992-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1049-9652(92)90025-S","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91631740","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: 166

Parameter estimation and reconstruction of digital conics in normal positions 正态位置数字二次曲线的参数估计与重构

CVGIP: Graphical Models and Image Processing Pub Date : 1992-09-01 DOI: 10.1016/1049-9652(92)90023-Q

S Chattopadhyay, P.P Das

{"title":"Parameter estimation and reconstruction of digital conics in normal positions","authors":"S Chattopadhyay, P.P Das","doi":"10.1016/1049-9652(92)90023-Q","DOIUrl":"https://doi.org/10.1016/1049-9652(92)90023-Q","url":null,"abstract":"<div><p>Reconstruction of the original curve (and the estimation of its parameters) from its digitization is a challenging problem as quantization always causes some loss of information. So we often estimate <em>at least one</em> (or <em>all</em>) continuous curve(s) which is (are) isomorphic to the original one under discretization. Some work has already been done in this respect on straight lines, circles, squares, etc. In this paper, we have attempted this problem for a specialized class of conics which are said to be in normal positions. In <em>normal position</em> the center of the conic is situated at a grid point and its axes are parallel to the coordinate axes. For circles and parabolas, we can directly formulate the domain, i.e., the entire set of continuous curves which produces the same digitization. For ellipses (and this can be extended to hyperbolas too), we first compute the smallest rectangle containing the domain of the given digitization and then estimate the domain itself. The major contribution of this paper lies in the development of a new method of analysis (via the iterative refinement of parameter bounds) which can be easily extended to other 1- or 2-parameter piecewise monotonic shapes such as straight lines or circles with known radius.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1992-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1049-9652(92)90023-Q","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91631741","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