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Note for Digital Image Processing - DIP By vtu rangers

  • Digital Image Processing - DIP
  • Note
  • Visvesvaraya Technological University Regional Center - VTU
  • Electrical and Electronics Engineering
  • 13 Topics
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Fundamental Steps in Digital Image Processing Image acquisition is the first process shown in Fig. Note that acquisition could be as simple asbeing given an image that is already in digital form. Generally, the image acquisition stage involvespreprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement iswhen we increase the contrast of an image because “it looks better.” It is important to keep inmind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sensethat restoration techniques tend to be based on mathematical or probabilistic models of

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imagedegradation. Enhancement, on the other hand, is based on human subjective preferencesregarding what constitutes a “good” enhancement result. Color image processing is an area that has been gaining in importance because of the significantincrease in the use of digital images over the Internet. Wavelets are the foundation for representing images in various degrees of resolution. Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it. Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in theform of image file extensions, such as the jpg file extension used in the JPEG (JointPhotographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in therepresentation and description of shape. Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A ruggedsegmentation procedure brings the process a long way toward successful solution of imagingproblems that require objects to be identified individually. On the other hand, weak or erraticsegmentation algorithms almost always guarantee eventual failure. In general, the more accuratethe segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, whichusually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixelsseparating one image region from another) or all the points in the region itself. In either case,converting the data to a form suitable for computer processing is necessary. The first decisionthat must be made is whether the data should be represented as a boundary or as a completeregion.

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Boundary representation is appropriate when the focus is on external shapecharacteristics, such as corners and inflections. Regional representation is appropriate when thefocus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution fortransforming raw data into a form suitable for subsequent computer processing. A method mustalso be specified for describing the data so that features of interest are highlighted. Description,also called feature selection, deals with extracting attributes that result in some quantitativeinformation of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on itsdescriptors. We conclude our coverage of digital image processing with the development ofmethods for recognition of individual objects. Components of an Image Processing System As recently as the mid-1980s, numerous models of image processing systems being sold

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throughout the world were rather substantial peripheral devices that attached to equally substantial host computers. Late in the 1980s and early in the 1990s, the market shifted to imageprocessing hardware in the form of single boards designed to be compatible with industrystandard buses and to fit into engineering workstation cabinets and personal computers. Inaddition to lowering costs, this market shift also served as a catalyst for a significant number ofnew companies whose specialty is the development of software written specifically for imageprocessing. Although large-scale image processing systems still are being sold for massive imaging applications, such as processing of satellite images, the trend continues toward miniaturizing and blending of general-purpose small computers with specialized image processing hardware. Figure shows the basic components comprising a typical generalpurposesystem used for digital image processing. The function of each component is discussed in thefollowing paragraphs, starting with image sensing. With reference to sensing, two elements are required to acquire digital images. The first is a physical device that is sensitive to the energy radiated by the object we wish to image. The second, called a digitizer, is a device for converting the output of the physical sensing device into digital form. For instance, in a digital video camera, the sensors produce an electrical outputproportional to light intensity. The digitizer converts these outputs to digital data. Specialized image processing hardware usually consists of the digitizer just mentioned, plus hardware that performs other primitive operations, such as an arithmetic logic unit (ALU), whichperforms arithmetic and logical operations in parallel on entire images. One example of how anALU is used is in averaging images as quickly as they are digitized, for the purpose of noisereduction. This type of hardware sometimes is called a front-end subsystem, and its mostdistinguishing characteristic is speed. In other words, this unit performs functions that requirefast data throughputs (e.g., digitizing and averaging video images at 30 framess) that the typicalmain computer cannot handle The computer in an image processing system is a

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