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Image Processing: Analysis and Machine Vision

TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Abstract: List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors propose a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive.
Abstract: Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and to a Bayesian comparison of binomial experiments.

6,188 citations

Journal ArticleDOI
TL;DR: This work was supported in part by the Royal Society of the UK, the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany.

2,404 citations

Book
01 Dec 1993
TL;DR: The geometric, random field, fractal, and signal processing models of texture are presented and major classes of texture processing such as segmentation, classification, and shape from texture are discussed.
Abstract: This chapter reviews and discusses various aspects of texture analysis. The concentration is o the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing pro lems such as segmentation, classification, and shape from texture are discussed. The possible applic tion areas of texture such as automated inspection, document processing, and remote sensing a summarized. A bibliography is provided at the end for further reading.

2,257 citations

Journal ArticleDOI
TL;DR: This paper identifies some promising techniques for image retrieval according to standard principles and examines implementation procedures for each technique and discusses its advantages and disadvantages.

1,910 citations


Cites methods from "Image Processing: Analysis and Mach..."

  • ...These simple global descriptors usually can only discriminate shapes with large di erences, therefore, they are usually used as 1lters to eliminate false hits or combined with other shape descriptors to discriminate shapes....

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  • ...Therefore, the feature vector [ 1; : : : ; m; != √ B]T is used as the shape descriptor....

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Proceedings ArticleDOI
26 Aug 2001
TL;DR: It is shown that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis: the similarity of data vectors is preserved well under random projection.
Abstract: Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however, empirical results are sparse. We present experimental results on using random projection as a dimensionality reduction tool in a number of cases, where the high dimensionality of the data would otherwise lead to burden-some computations. Our application areas are the processing of both noisy and noiseless images, and information retrieval in text documents. We show that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis: the similarity of data vectors is preserved well under random projection. However, using random projections is computationally significantly less expensive than using, e.g., principal component analysis. We also show experimentally that using a sparse random matrix gives additional computational savings in random projection.

1,470 citations


Cites background or methods from "Image Processing: Analysis and Mach..."

  • ...Its computational complexity is of the order O(dN log 2 (dN)) for a data matrix of size d × N [27]....

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  • ...The median is not affected by individual noise spikes and so median filtering eliminates impulse noise quite well [27]....

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  • ...DCT can be performed by simple matrix operations [23, 27]: an image is transformed to the DCT space and dimensionality reduction is done in the inverse transform by discarding the transform coefficients corresponding to the highest frequencies....

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