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Fundamentals of Image Processing & Components.ppt
- 2. Unit-I • Introduction: DIGITAL IMAGE FUNDAMENTALS: Steps in Digital Image Processing – Components – Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels – Color image fundamentals – RGB, HSI models, Two-dimensional mathematical preliminaries, 2D transforms – DFT, DCT. Unit -II • IMAGE ENHANCEMENT : Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering– Smoothing and Sharpening Spatial Filtering, Frequency Domain: Introduction to Fourier Transform– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters, Homomorphic filtering, Color image enhancement. Unit - III • IMAGE RESTORATION : Image Restoration – degradation model, Properties, Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering Unit - IV • IMAGE SEGMENTATION: Edge detection, Edge linking via Hough transform – Thresholding – Region based segmentation – Region growing – Region splitting and merging – Morphological processing- erosion and dilation, Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed segmentation algorithm Unit -V • IMAGE COMPRESSION AND RECOGNITION: Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, JPEG standard, MPEG. Boundary representation, Boundary description, Fourier Descriptor, Regional Descriptors – Topological feature, Texture – Patterns and Pattern classes – Recognition based on matching.
- 5. What is a digital image? • A digital image is a representation of a two- dimensional image as a finite set of digital values, called picture elements or pixels
- 6. What is a digital image? (Cont.) • Pixel values typically represent gray levels, colours, heights, opacities etc • Remember digitization implies that a digital image is an approximation of a real scene 1 pixel
- 7. What is Digital Image Processing? • Digital image processing focuses on two major tasks – Improvement of pictorial information for human interpretation – Processing of image data for storage, transmission and representation for autonomous machine perception
- 17. Uses of DIP – Image enhancement/restoration – Artistic effects – Medical visualisation – Law enforcement – Human computer interfaces
- 18. Examples: Image Enhancement • One of the most common uses of DIP techniques: improve quality, remove noise etc
- 19. Examples: The Hubble Telescope •Launched in 1990 the Hubble telescope can take images of very distant objects •However, an incorrect mirror made many of Hubble’s images useless •Image processing techniques were used to fix this
- 20. Examples: Artistic Effects • Artistic effects are used to make images more visually appealing, to add special effects and to make composite images
- 21. Examples: Medicine Take slice from MRI (Magnetic Resounance Imaging) scan of a heart, and find boundaries between types of tissue – Image with gray levels representing tissue density – Use a suitable filter to highlight edges
- 22. Examples: GIS •Geographic Information Systems – Digital image processing techniques are used extensively to manipulate satellite imagery – Terrain classification ()التضاريس – Meteorology (الجوية )األرصاد
- 23. Examples: Law Enforcement •Image processing techniques are used extensively by law enforcers – Number plate recognition for speed cameras/automated toll systems – Fingerprint recognition
- 24. Examples: HCI •Try to make human computer interfaces more natural – Face recognition – Gesture recognition •Does anyone remember the user interface from “Minority Report”? •These tasks can be extremely difficult
- 25. Image Processing Fields • Computer Graphics: The creation of images • Image Processing: Enhancement or other manipulation of the image • Computer Vision: Analysis of the image content
- 26. Image Processing Fields Input / Output Image Description Image Image Processing Computer Vision Description Computer Graphics AI Sometimes, Image Processing is defined as “a discipline in which both the input and output of a process are images But, according to this classification, trivial tasks of computing the average intensity of an image would not be considered an image processing operation
- 27. Computerized Processes Types • Low-Level Processes: – Input and output are images – Tasks: Primitive operations, such as, image processing to reduce noise, contrast enhancement and image sharpening
- 28. Computerized Processes Types • Mid-Level Processes: – Inputs, generally, are images. Outputs are attributes extracted from those images (edges, contours, identity of individual objects) – Tasks: • Segmentation (partitioning an image into regions or objects) • Description of those objects to reduce them to a form suitable for computer processing • Classifications (recognition) of objects
- 29. Computerized Processes Types • High-Level Processes: – Image analysis and computer vision
- 30. Digital Image Definition • An image can be defined as a two- dimensional function f(x,y) • x,y: Spatial coordinate • F: the amplitude of any pair of coordinate x,y, which is called the intensity or gray level of the image at that point. • X,y and f, are all finite and discrete quantities.
- 31. Fundamental Steps in Digital Image Processing: Image Acquisition Image Restoration Morphological Processing Segmentation Object Recognition Image Enhancement Representation & Description Problem Domain Colour Image Processing Image Compression Wavelets & Multiresolution processing Knowledge Base Outputs of these processes generally are images Outputs of these processes generally are image attributes
- 32. Key Stages in Digital Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 33. Key Stages in Digital Image Processing: Image Aquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 34. Key Stages in Digital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 35. Key Stages in Digital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 36. Key Stages in Digital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 37. Key Stages in Digital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 38. Key Stages in Digital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 39. Key Stages in Digital Image Processing: Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 40. Key Stages in Digital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 41. Key Stages in Digital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 42. Fundamental Steps in DIP: (Description) Step 1: Image Acquisition The image is captured by a sensor (eg. Camera), and digitized if the output of the camera or sensor is not already in digital form, using analogue-to-digital convertor
- 43. Fundamental Steps in DIP: (Description) Step 2: Image Enhancement The process of manipulating an image so that the result is more suitable than the original for specific applications. The idea behind enhancement techniques is to bring out details that are hidden, or simple to highlight certain features of interest in an image.
- 44. Fundamental Steps in DIP: (Description) Step 3: Image Restoration - Improving the appearance of an image - Tend to be mathematical or probabilistic models. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result.
- 45. Fundamental Steps in DIP: (Description) Step 4: Colour Image Processing Use the colour of the image to extract features of interest in an image
- 46. Fundamental Steps in DIP: (Description) Step 5: Wavelets Are the foundation of representing images in various degrees of resolution. It is used for image data compression.
- 47. Fundamental Steps in DIP: (Description) Step 6: Compression Techniques for reducing the storage required to save an image or the bandwidth required to transmit it.
- 48. Fundamental Steps in DIP: (Description) Step 7: Morphological Processing Tools for extracting image components that are useful in the representation and description of shape. In this step, there would be a transition from processes that output images, to processes that output image attributes.
- 49. Fundamental Steps in DIP: (Description) Step 8: Image Segmentation Segmentation procedures partition an image into its constituent parts or objects. Important Tip: The more accurate the segmentation, the more likely recognition is to succeed.
- 50. Fundamental Steps in DIP: (Description) Step 9: Representation and Description - Representation: Make a decision whether the data should be represented as a boundary or as a complete region. It is almost always follows the output of a segmentation stage. - Boundary Representation: Focus on external shape characteristics, such as corners and inflections - Region Representation: Focus on internal properties, such as texture or skeleton shape
- 51. Fundamental Steps in DIP: (Description) Step 9: Representation and Description - Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing (mainly recognition) - Description: also called, feature selection, deals with extracting attributes that result in some information of interest.
- 52. Fundamental Steps in DIP: (Description) Step 9: Recognition and Interpretation Recognition: the process that assigns label to an object based on the information provided by its description.
- 53. Fundamental Steps in DIP: (Description) Step 10: Knowledge Base Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database.
- 54. Components of an Image Processing System Network Image displays Computer Mass storage Hardcopy Specialized image processing hardware Image processing software Image sensors Problem Domain Typical general- purpose DIP system
- 55. Components of an Image Processing System 1. Image Sensors Two elements are required to acquire digital images. The first is the physical device that is sensitive to the energy radiated by the object we wish to image (Sensor). The second, called a digitizer, is a device for converting the output of the physical sensing device into digital form.
- 56. Components of an Image Processing System 2. Specialized Image Processing Hardware Usually consists of the digitizer, mentioned before, plus hardware that performs other primitive operations, such as an arithmetic logic unit (ALU), which performs arithmetic and logical operations in parallel on entire images. This type of hardware sometimes is called a front-end subsystem, and its most distinguishing characteristic is speed. In other words, this unit performs functions that require fast data throughputs that the typical main computer cannot handle.
- 57. Components of an Image Processing System 3. Computer The computer in an image processing system is a general-purpose computer and can range from a PC to a supercomputer. In dedicated applications, sometimes specially designed computers are used to achieve a required level of performance.
- 58. Components of an Image Processing System 4. Image Processing Software Software for image processing consists of specialized modules that perform specific tasks. A well-designed package also includes the capability for the user to write code that, as a minimum, utilizes the specialized modules.
- 59. Components of an Image Processing System 5. Mass Storage Capability Mass storage capability is a must in a image processing applications. And image of sized 1024 * 1024 pixels requires one megabyte of storage space if the image is not compressed. Digital storage for image processing applications falls into three principal categories: 1. Short-term storage for use during processing. 2. on line storage for relatively fast recall 3. Archival storage, characterized by infrequent access
- 60. Components of an Image Processing System 5. Mass Storage Capability One method of providing short-term storage is computer memory. Another is by specialized boards, called frame buffers, that store one or more images and can be accessed rapidly. The on-line storage method, allows virtually instantaneous image zoom, as well as scroll (vertical shifts) and pan (horizontal shifts). On-line storage generally takes the form of magnetic disks and optical-media storage. The key factor characterizing on-line storage is frequent access to the stored data. Finally, archival storage is characterized by massive storage requirements but infrequent need for access.
- 61. Components of an Image Processing System 6. Image Displays The displays in use today are mainly color (preferably flat screen) TV monitors. Monitors are driven by the outputs of the image and graphics display cards that are an integral part of a computer system.
- 62. Components of an Image Processing System 7. Hardcopy devices Used for recording images, include laser printers, film cameras, heat-sensitive devices, inkjet units and digital units, such as optical and CD-Rom disks.
- 63. Components of an Image Processing System 8. Networking Is almost a default function in any computer system, in use today. Because of the large amount of data inherent in image processing applications the key consideration in image transmission is bandwidth. In dedicated networks, this typically is not a problem, but communications with remote sites via the internet are not always as efficient.
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