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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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emulate human vision, including learning andbeing able to make inferences and take actions based on visual inputs. This area itself is a branchof artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI isin its earliest stages of infancy in terms of development, with progress having been much slowerthan originally anticipated. The area of image analysis (also called image understanding) is inbetween image processing and computer vision. There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high-level processes. Lowlevelprocesses involve primitive operations such as image preprocessing to reduce noise,contrast enhancement, and image sharpening. A low-level process is characterized by the factthat both its inputs and outputs are images. Mid-level processing on images involves tasks suchas segmentation (partitioning an image into regions or objects), description of those objects toreduce them to a form suitable for computer processing, and classification (recognition) ofindividual objects. A mid-level process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and theidentity of individual objects). Finally, higher-level processing involves “making sense” of anensemble of recognized objects, as in image analysis, and, at the far end of the continuum,performing the cognitive functions normally associated with vision and, in addition,encompasses processes that extract attributes from images, up to and including the recognition ofindividual objects. As a simple illustration to clarify these concepts, consider the area ofautomated analysis of text. The processes of acquiring an image of the area containing the text,preprocessing that image, extracting (segmenting) the individual characters, describing thecharacters in a form suitable for computer processing, and recognizing those individualcharacters are in the scope of what we call digital image processing.

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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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