Welcome to ## Digital Image Processing | <span style="color:yellow"> M Pardha Saradhi </span> | <span style="color:yellow"> Department of ECE </span> | <span style="color:yellow"> VVIT </span> | --- ## Lecture 1.2: Fundamental Steps in Image Processing --- ## Contents * Fundamental steps in image processing * Compnents of an Image processing system * Image acquisition --- ### Fundamental Steps 1. Image acquisition 2. Image enhancement 3. Image restoration 4. Color image processing 5. Wavelets and multiresolution processing 6. Compression 7. Morphological processing 8. Segmentation 9. Representation & description 10. Object recognition > All the above modules interact with eachother through knoweldge base ---  --- Image acquisition  --- Image enhancement  --- Image enhancement * 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 is when we increase the contrast of an image because “it looks better.” * It is important to keep in mind that enhancement is a very subjective area of image processing. --- Image restoration  --- Image restoration * 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 sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. * Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result. --- Morphological processing  --- Morphological processing * Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. * The techniques in this module begins a transition from processes that output images to processes that output image attributes. --- Wavelet & Multiresolution processing * Wavelets are the foundation for representing images in various degrees of resolution. * In particular, this material is used in for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions. --- Image segmentation  --- Image segmentation * 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 rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. * On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. * In general, the more accurate the segmentation, the more likely recognition is to succeed. --- Representation & Description  --- Representation & Description * Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. <!-- .element: class="fragment" --> * In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. <!-- .element: class="fragment" --> * Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. <!-- .element: class="fragment" --> * Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. <!-- .element: class="fragment" --> * In some applications, these representations complement each other. <!-- .element: class="fragment" --> --- Representation & Description > Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. > A method must also 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 quantitative information of interest or are basic for differentiating one class of objects from another. --- Object recognition  --- Image compression * 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 the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard. --- Color image processing * Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. * We covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image. --- ## Compnents of an Image processing system  --- # Image acquisition * The images are generated by the combination of an illumination source and the reflection or absorption of energy from that source by the elements of the scene being imaged. * Imaging sensors are used to transform the reflected energy into digital images. * Each sensor transforms the incoming energy into voltage by the combination of the input electrical power and the sensor material that is responsive to the particular type of energy being detected --- # Types of sensors * Single sensor * Line sensor * Array sensor ## Typical vision sensor  --- Image acquisition using single sensor  --- Line & Array sensors  --- Image acquisition using line sensor  --- Image acquisition using array sensor  --- # Questions? <style> .reveal { text-align: justify; } </style> <style> .reveal { font-size: 28px; } </style> ---