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Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab (英语) 平装 – 2011年2月22日

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基本信息

  • 出版社: Wiley; 1 (2011年2月22日)
  • 平装: 344页
  • 语种: 英语
  • ISBN: 0470844736
  • 条形码: 9780470844731
  • 商品尺寸: 17 x 1.7 x 24.6 cm
  • 商品重量: 599 g
  • ASIN: 0470844736
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“Given the timely topic and its user-friendly structure, this book can therefore target a suite of users, from students to experienced researchers willing to integrate the science of image processing to strengthen their research.”  (Ethology Ecology & Evolution, 1 May 2013)

目录

Preface.

Using the book website.

1 Representation.

1.1 What is an image?

1.1.1 Image layout.

1.1.2 Image colour.

1.2 Resolution and quantization.

1.2.1 Bit-plane splicing.

1.3 Image formats.

1.3.1 Image data types.

1.3.2 Image compression.

1.4 Colour spaces.

1.4.1 RGB.

1.4.2 Perceptual colour space.

1.5 Images in Matlab.

1.5.1 Reading, writing and querying images.

1.5.2 Basic display of images.

1.5.3 Accessing pixel values.

1.5.4 Converting image types.

Exercises.

2 Formation.

2.1 How is an image formed?

2.2 The mathematics of image formation.

2.2.1 Introduction.

2.2.2 Linear imaging systems.

2.2.3 Linear superposition integral.

2.2.4 The Dirac delta or impulse function.

2.2.5 The point-spread function.

2.2.6?Linear shift-invariant systems and the convolution integral.

2.2.7?Convolution: its importance and meaning.

2.2.8 Multiple convolution: N imaging elements in a linear shift-invariant system.

2.2.9 Digital convolution.

2.3 The engineering of image formation.

2.3.1 The camera.

2.3.2 The digitization process.

2.3.3 Noise.

Exercises.

3 Pixels.

3.1 What is a pixel?

3.2 Operations upon pixels.

3.2.1?? ?Arithmetic operations on images.

3.2.1.2 Multiplication and division.

3.2.2 Logical operations on images.

3.2.3 Thresholding.

3.3 Point-based operations on images.

3.3.1 Logarithmic transform.

3.3.2 Exponential transform.

3.3.3 Power-law (gamma) transform.

3.4 Pixel distributions: histograms.

3.4.1 Histograms for threshold selection.

3.4.2 Adaptive thresholding.

3.4.3 Contrast stretching.

3.4.4 Histogram equalization.

3.4.5 Histogram matching.

3.4.6 Adaptive histogram equalization.

3.4.7 Histogram operations on colour images.

Exercises.

4 Enhancement.

4.1 Why perform enhancement?

4.2 Pixel neighbourhoods.

4.3 Filter kernels and the mechanics of linear filtering.

4.3.1 Nonlinear spatial filtering.

4.4 Filtering for noise removal.

4.4.1 Mean filtering.

4.4.2 Median filtering.

4.4.3 Rank filtering.

4.4.4 Gaussian filtering.

4.5 Filtering for edge detection.

4.5.1 Derivative filters for discontinuities.

4.5.2 First-order edge detection.

4.5.3 Second-order edge detection.

4.6 Edge enhancement.

4.6.1 Laplacian edge sharpening.

4.6.2 The unsharp mask filter.

Exercises.

5 Fourier transforms and frequency-domain processing.

5.1 Frequency space: a friendly introduction.

5.2 Frequency space: the fundamental idea.

5.2.1 The Fourier series.

5.3 Calculation of the Fourier spectrum.

5.4 5.4 Complex Fourier series.

5.5 The 1-D Fourier transform.

5.6 The inverse Fourier transform and reciprocity.

5.7 The 2-D Fourier transform.

5.8 Understanding the Fourier transform: frequency-space filtering.

5.9 Linear systems and Fourier transforms.

5.10 The convolution theorem.

5.11 The optical transfer function.

5.12 Digital Fourier transforms: the discrete fast Fourier transform.

5.13 Sampled data: the discrete Fourier transform.

5.14 The centred discrete Fourier transform.

6 Image restoration.

6.1 Imaging models.

6.2 Nature of the point-spread function and noise.

6.3 Restoration by the inverse Fourier filter.

6.4 The Wiener?Helstrom Filter.

6.5 Origin of the Wiener?Helstrom filter.

6.6 Acceptable solutions to the imaging equation.

6.7 Constrained deconvolution.

6.8?Estimating an unknown point-spread function or optical transfer function.

6.9?Blind deconvolution.

6.10 Iterative deconvolution and the Lucy?Richardson algorithm.

6.11 Matrix formulation of image restoration.

6.12 The standard least-squares solution.

6.13 Constrained least-squares restoration.

6.14 Stochastic input distributions and Bayesian estimators.

6.15 The generalized Gauss?Markov estimator.

7 Geometry.

7.1 The description of shape.

7.2 Shape-preserving transformations.

7.3 Shape transformation and homogeneous coordinates.

7.4 The general 2-D affine transformation.

7.5 Affine transformation in homogeneous coordinates .

7.6 The Procrustes transformation.

7.7 Procrustes alignment.

7.8 The projective transform.

7.9 Nonlinear transformations.

7.10Warping: the spatial transformation of an image.

7.11 Overdetermined spatial transformations.

7.12 The piecewise warp.

7.13 The piecewise affine warp.

7.14 Warping: forward and reverse mapping.

8?Morphological processing.

8.1 Introduction.

8.2 Binary images: foreground, background and connectedness.

8.3 Structuring elements and neighbourhoods.

8.4 Dilation and erosion.

8.5 Dilation, erosion and structuring elements within Matlab.

8.6 Structuring element decomposition and Matlab.

8.7 Effects and uses of erosion and dilation.

8.7.1 Application of erosion to particle sizing.

8.8 Morphological opening and closing.

8.8.1 The rolling-ball analogy.

8.9 Boundary extraction.

8.10 Extracting connected components.

8.11 Region filling.

8.12 The hit-or-miss transformation.

8.12.1 Generalization of hit-or-miss.

8.13 Relaxing constraints in hit-or-miss: ?don?t care? pixels.

8.13.1 Morphological thinning.

8.14 Skeletonization.

8.15 Opening by reconstruction.

8.16 Grey-scale erosion and dilation.

8.17 Grey-scale structuring elements: general case.

8.18 Grey-scale erosion and dilation with flat structuring elements.

8.19 Grey-scale opening and closing.

8.20 The top-hat transformation.

8.21 Summary.

Exercises.

9?Features.

9.1 Landmarks and shape vectors.

9.2 Single-parameter shape descriptors.

9.3 Signatures and the radial Fourier expansion.

9.4 Statistical moments as region descriptors.

9.5 Texture features based on statistical measures.

9.6 Principal component analysis.

9.7 Principal component analysis: an illustrative example.

9.8 Theory of principal component analysis: version 1.

9.9 Theory of principal component analysis: version 2.

9.10 Principal axes and principal components.

9.11 Summary of properties of principal component analysis.

9.12 Dimensionality reduction: the purpose of principal component analysis.

9.13 Principal components analysis on an ensemble of digital images.

9.14 Representation of out-of-sample examples using principal component analysis.

9.15 Key example: eigenfaces and the human face.

10 Image Segmentation.

10.1 Image segmentation.

10.2 Use of image properties and features in segmentation.

10.3 Intensity thresholding.

10.3.1 Problems with global thresholding.

10.4 Region growing and region splitting.

10.5 Split-and-merge algorithm.

10.6 The challenge of edge detection.

10.7 The Laplacian of Gaussian and difference of Gaussians filters.

10.8 The Canny edge detector.

10.9 Interest operators.

10.10 Watershed segmentation.

10.11 Segmentation functions.

10.12 Image segmentation with Markov random fields.

10.12.1 Parameter estimation.

10.12.2 Neighbourhood weighting parameter θn

10.12.3 Minimizing U(x|y): the iterated conditional modes algorithm.

11 Classification.

11.1 The purpose of automated classification.

11.2 Supervised and unsupervised classification.

11.3 Classification: a simple example.

11.4 Design of classification systems.

11.5 Simple classifiers: prototypes and minimum distance criteria.

11.6 Linear discriminant functions.

11.7 Linear discriminant functions in N dimensions.

11.8 Extension of the minimum distance classifier and the Mahalanobis distance.

11.9 Bayesian classification: definitions.

11.10 The Bayes decision rule.

11.11 The multivariate normal density.

11.12 Bayesian classifiers for multivariate normal distributions.

11.12.1 The Fisher linear discriminant.

11.12.2 Risk and cost functions.

11.13 Ensemble classifiers.

11.13.1 Combining weak classifiers: the AdaBoost method.

11.14 Unsupervised learning: k-means clustering.

Further reading.

Index.

商品评论

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此商品在美国亚马逊上最有用的商品评论 (beta) (可能包括"Early Reviewer Rewards Program"的评论)

美国亚马逊: 平均4.4 星 10 条评论
2/3 人认为此评论有用
平均5.0 星 Excellently Written 2013年7月21日
评论者 EWhit - 已在美国亚马逊上发表
版本: 平装 已确认购买
This book is well written and provides the basics to numerous image processing techniques. Authors do a good job explaining basic principles without bogging down with all the detail. I highly recommend this as an introductory book to image processing as it has been exceptionally useful to me.

It is highly recommended to make use of the website if you wish to get the full value from this book. The website provides all the matlab custom functions and examples used in this book as well as the images. Additionally, the website has an existing errata and also has a way to report errors you may find in the book. Very impressed.
平均4.0 星 Four Stars 2016年10月27日
评论者 Joseph Drori - 已在美国亚马逊上发表
版本: 平装 已确认购买
Took some time to receive and the quality is OK
1/2 人认为此评论有用
平均5.0 星 Good matlab examples 2013年8月31日
评论者 mmm713 - 已在美国亚马逊上发表
版本: 平装 已确认购买
Contents are OK, its same everywhere. But the examples are actually useful to learn DSP in matlab. quality is good too
4/6 人认为此评论有用
平均3.0 星 Its Okay 2013年3月9日
评论者 AK - 已在美国亚马逊上发表
版本: Kindle电子书 已确认购买
This book is okay for a beginner level understanding of what Image Processing is and what we do with it. However, it doesn't convey a good understanding of the fundamental concepts and where they are coming from. Its kind of like the "For Dummies" series books.
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平均5.0 星 Suitable for a practical introduction to the Image Processing 2013年4月25日
评论者 Alessandro - 已在美国亚马逊上发表
版本: 平装
The Book of Solomon and Breckon is a good "practical introduction" to the Image Processing.

It is an "introduction" because it covers all the basic subjects of Image Processing in just 300 pages; it is "practical" because it invites you to test the concepts with the help of MATLAB programs, the book is still useful regardless of the knowledge and the use of MATLAB.

The style of the authors is, for me, nice and close to the reader, the authors sometimes understand the doubts that a student may have and clarify them, I think for example at the explanation of the 2D Fourier transform where they explain that the double integral is not to be solved by hand! I think this kind of clarifications help the student, especially the one that is no longer in school / college but who needs to improve his knowledge in a self-study way (this is my case). In general, the style is not overly formal and resembles a live lecture rather than a treatise.

Although this is a book on the fundamentals of image processing there are also introductions to more advanced topics (for example, I really liked the introduction to image segmentation with Markov random fields) that arise, in my opinion, as effective incentives to deepen the discussion on specialized books (in relation to the example I think for example at Computer Vision: Models, Learning, and Inference by Simon J.D. Prince).

I conclude by emphasizing the friendliness and helpfulness of both authors, I contacted them via email for clarification and they answered promptly.