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This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of applications, representing the latest trends in industry and academic research. To inspire further interest in the field, a selection of worked-out numerical problems is also included in the text. The content is presented in an accessible manner, examining each topic in depth without assuming any prior knowledge from the reader, and providing additional background material in the appendices. Features: covers image enhancement techniques in the spatial domain, the frequency domain, and the wavelet domain; reviews compression methods and formats for encoding images; discusses morphology-based image processing; investigates the modeling of object recognition in the human visual system; provides supplementary material, including MATLAB and C++ code, and interactive GUI-based modules, at an associated website.

Inhaltsverzeichnis

Frontmatter

1. Introduction to Digital Image

Abstract
The word “signal” carries a broad meaning in all the domains of knowledge gathering, ranging from electronics and computer engineering to deaf-and-dumb communication. The word is uttered by every professional ranging from journalists to linguists. In the present chapter, we have introduced signal and tried to make the definition generic. From the basic definition of image, we will try to fit image into the generic definition of signal. As the title of the book signifies, here we will address the subject digital image processing through the two sided merely correlated guides to signal processing and pattern recognition.
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2. Image Enhancement in Spatial Domain

Abstract
Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application may vary from thermal image to X-ray image and accordingly the process of image enhancement would differ. Generally, the effect of image enhancement can be perceived visually. Even to address/handle the regular artifacts due to geometric transformations of images, image enhancement is done in form of image interpolation. The spatial domain refers to the 2D image plane represented in terms of pixel intensities. When the image is enhanced by modifying the pixel intensities directly (not as an effect of some other parameter tuning in a different domain), the method is considered as spatial domain image enhancement methodology. Otherwise, the image can be transformed to some other domain—like one 2D image can be transformed to a 2D frequency domain by discrete Fourier transform (DFT). To achieve an enhanced image, the Fourier coefficients are modified. That family of image enhancement methodologies is considered as frequency domain image enhancement which is discussed in the subsequent chapters. Whatever be the domain of image enhancement (either spatial or frequency domain), by the term image enhancement we mean improvement of the appearance of an image (in all sense including human perception and machine perception) by increasing the dominance of some features, or by decreasing the ambiguity between different regions of the image. In most cases, the enhancement of certain features is achieved at the cost of suppressing few other features.
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3. Interpretation and Processing of Image in Frequency Domain

Abstract
In order to maintain a correlation between the current and the previous chapter, the title of the current chapter could probably be image enhancement in frequency domain or filtering of images in frequency domain. General readers may not have any idea what the term “frequency” is depicted in relation to image. Hence, the chapter title has been finally decided to be kept as interpretation and processing of image in frequency domain which includes the elaboration of the concept of the term “frequency” in an image. According to the promise of the author and editor, to understand the subject of image processing and pattern recognition through this book, the target readers should not be only the experts in signal processing. Therefore, to brush up the knowledge of signal analysis in frequency domain, the basic frequency domain transformations are presented and discussed as they have evolved depending on needs and applicability.
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4. Color Science and Color Technology

Abstract
We cannot see energy, we just can feel the effect of it. Light is also an energy and we can only see the objects after light falls on it and the reflected light reaches our eyes and psycho-visual system from the target object. The perception of color hence is dependent on light, reflectance spectra of object, and observer. The object property plays a very important role in color perception. Different devices responsible for color realization work in two major ways and the designation of primary colors also varies according to the system property (e.g., additive color and subtractive color model dependent on devices). In the current chapter, the visible electromagnetic spectra have been studied and primary colors are identified for additive color space. Next, depending on the color geometry, the primaries are derived for subtractive color space, too. The current chapter then shows how from reflectance spectra colors can be defined and derived. The psycho-visual model of color perception and its influence in derivation of device independent color space is discussed. Minute color measurement in order to ensure highest Color Image Quality (CIQ) is discussed in detail. In the color technology module, the concept of halftoning through error diffusion is presented along with gamut mapping with respect to different suitable rending intents.
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5. Wavelets: Multiresolution Image Processing

Abstract
Fourier transform has been considered to be a well-accepted transformation for both time domain and spatial signal analysis since the late 1950s. A relatively new transformation technique named wavelet transform has been utilized in an even better way for 1D and 2D signal decomposition, compression, encoding, and different methods of analysis and synthesis. Introduction to windowed Fourier transform or short-time Fourier transform (STFT) and its drawbacks have been discussed in detail. Next the suitable transformation technique for 1D and 2D signal is proposed as wavelet transform. Both the continuous and discrete wavelet series and transforms are interpreted physically with elegant mathematical supports. Dyadic decomposition for image processing is discussed for sub-band coding. The last section of this chapter deals with an application of the DWT to image compression. The technique of embedded zero-tree wavelet (EZW) coding for image compression is also discussed which is tuned with respect to bit-budget. Introduction to windowed Fourier transform or short-time Fourier transform (STFT) and its drawbacks have been discussed in detail. Next the suitable transformation technique for 1D and 2D signal is proposed as wavelet transform. Both the continuous and discrete wavelet series and transforms are interpreted physically with elegant mathematical supports. Dyadic decomposition for image processing is discussed for sub-band coding. The last section of this chapter deals with an application of the DWT to image compression. The technique of embedded zero-tree wavelet (EZW) coding for image compression is also discussed which is tuned with respect to bit-budget.
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6. Compression and Encoding of Image: Image Formats

Abstract
Digital image is a discrete 2D signal having information of intensity in terms of primary colors (either red, green, blue (RGB), or cyan, magenta, yellow, black (CMYK)) for each separation of each pixel. Just by considering a small RGB image of size 10?×?10, we can measure the size of the image data as 3?×?8?×?10?×?10?=?2400 bits, where intensity of each color separation or each pixel is 1 byte or 8 bits. Hence, we can understand, it requires huge data to represent a sufficiently large image. In today’s world of image transmission through network, fast representation on webpage, and storing image information, the size of data plays a resistive role. Therefore, it is very important to employ image compression suitably. The term “suitable” is added intentionally to address the quality issue of image against the trade-off with compression ratio. It is well understood that there is a merely inverse relationship between the factors “quality” and “compression ratio”. The quality is a direct illustration of information in the image. As the amount of detailing in the image is varied, the difficulty in choice between quality and compression ratio is also varied. This chapter is especially important for any kind of application development in image processing. The lossless and lossy compression methodology with trade-off has been described in the first half of this chapter. Second half of the chapter describes the process and algorithm of encoding the raw and compressed image formats. We have presented one C++ code for reading 24-bit BMP image. The code is available in the supplementary electronic material (CD) also. The two popular compressed image formats, JPEG and GIF are also discussed and the required code snippets are presented.
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7. Morphology-Based Image Processing

Abstract
The word morphology refers to a discipline in biology where the shapes and structures of animals and trees are discussed and analyzed. In this chapter, we concentrate on the mathematics of morphology and apply them to image processing, especially binary images. To be precise, morphology-based image processing can be considered as the bridge between the signal and pattern property of image. This can be considered as both preprocessing algorithm and feature-extraction algorithm. Morphology-based image processing is specially used for shape-based feature extraction like boundary, convex, skeleton, and regions from an image.
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8. Patterns in Images and Their Applications

Abstract
“Recognition” is a very important task of any intelligent system. When we are particularly interested in enhancing the ability of a computer by incorporating a vision system in it, in other words the capability of perceiving an image and processing the same, the recognition becomes really important in order to declare a system to be intelligent.
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9. Psycho-visual pattern recognition: Computer Vision

Abstract
Object recognition is one of the most crucial and yet least understood aspects of visual perception. A simple answer to what we mean by visually recognizing an object may be, naming the object in sight. Humphreys et al. have identified several stages in visual processing that results in naming the object through recognition. However, while discussing this issue in detail, Ullman has shown that a natural association between naming and recognizing may not be all that unambiguous. This is because, an object may simultaneously belong to a number of classes like, for example, a book, my book, a comics book, Tintin in Tibet, and so on. From this example, it is clear that naming the object in sight depends upon the subjective recognition of the appropriate class as well, which in turn depends on the purpose of recognition under the given circumstances. Furthermore, even animals that cannot express themselves through language can still visually recognize objects. Humphreys et al. in their work have also acknowledged this issue of associated subjectivity in object recognition by making distinction among semantic representation, name representation, and semantic classification in their computational model that starts from a structural representation of the object. Significantly though, they have also demonstrated that a top-down intervention from semantic units to structural description units plays an important role in object naming in terms of top-down influence from higher to lower level in recognition of vision. However, in this chapter, our focus is on understanding the complex process of object recognition at the mid-level vision in terms of the several interacting components that are involved in it, especially the factors like intensity, orientation, and relative size of the region of attention.
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10. Appendix A: Digital Differentiation and Edge Detection

Abstract
This book has the prime objective to interpret an image in the form of both as a two-dimensional (2D) signal and as a pattern. In our introductory chapter, we have discussed the formation of image in nature and its digital coding for the ease of processing, communication, and storage. In this chapter, we physically interpret the method of convolution in the image-processing framework by the method of digital differentiation. Here, we take an example of edge detection from the digital image-processing domain to understand the convolution in light of digital differentiation.
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11. Appendix B: Elementary Probability Theory

Abstract
Everything is deterministic in nature. The limitations of measurement of a number of boundary conditions for a number of physical phenomena are the reason for which we need to depend on the concept of probability. In image processing, pattern recognition, signal processing and communication–noise accumulation probability, probability distribution of noise, modeling of noise and signal in image, understanding the tendency of image formation, and image understanding are really essential studies.
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12. Appendix C: Frequently Used MATLAB Functions

Abstract
The frequently used Matlab functions, throughout this book, are defined in this appendix with examples.
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Backmatter

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