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Representing Digital Images
âş Discrete intensity interval [0, L-1], L=2k
âş The number b of bits required to store a M Ă N
digitized image
b = M Ă N Ă k](https://image.slidesharecdn.com/dip1introdipfundamentals-250116063325-227512ec/85/Digital-Image-Processing-fundamentals-pptx-57-320.jpg)
Digital Image Processing fundamentals.pptx
- 1. Course Code: CSDLO6013 CourseTitle: Image and Video Processing T.E. CSE (AI&ML) Prepared by: Mr. R. P. Tivarekar
- 2. Course Objectives 1. Module1: Digital Image Fundamentals - Introduce students to the fundamental concepts of digital image representation, sampling, quantization, and file formats. 2. Module 2: Image Enhancement in Spatial Domain - Equip students with knowledge of spatial-domain image enhancement techniques such as histogram processing and filtering. 3. Module 3: Image Segmentation - Develop skills to analyze image segmentation methods, edge detection techniques, and region-oriented segmentation. 4. Module 4: Image Transforms - Provide an understanding of unitary transforms and their applications, including Fourier and cosine transforms. 5. Module 5: Image Compression - Introduce students to image compression techniques, including lossless and lossy methods, for data reduction. 6. Module 6: Digital Video Processing - Familiarize students with digital video processing concepts, formats, and applications. 2
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- 6. Prepared by: Mr.R. P. Tivarekar 6 Module 01 Digital Image Fundamentals
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- 12. What is anImage? âşImage is a source of information according to information theory âşAn image may be defined as a two-dimensional function f(x,y) where x and y are spatial coordinates and amplitude of f at any pair of coordinates (x,y) is called the intensity or Gray level of the image at that point. 12
- 13. Digital Image âş Whenx,y and the amplitude values of f are all finite, discrete quantities, we call the image a Digital Image. âş A digital Image is composed of a finite number of elements each of which has a particular location and value âşThese elements are referred to as Picture Elements, Image Elements, Pels or Pixels. 13
- 14. Pixel âşIn digital imaging,a pixel is the smallest piece of information in an image. âşPixels are normally arranged in a regular 2- dimensional grid, and are often represented using dots or squares âşThe intensity of each pixel is variable; in color systems, each pixel has typically three or four components such as red, green, and blue, or cyan, magenta, yellow, and black 14
- 15. Image Representation âşHow isan image stored? âşHow is an image file structured? âşA digital image is the composition of individual pixels or picture elements. âşThe pixels are arranged in the form of row and column to form a picture area. âşThe number of pixels in an image is a function of the size of the image and number of pixels per unit length (e.g., inch) in horizontal as well as vertical direction. 15
- 16. Ěś a two-dimensional function Ěśx and y are spatial coordinates Ěś The amplitude of f is called intensity or gray level at the point (x, y) 16 Image Representation
- 17. What is DigitalImage Processing Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation Process digital images by means of computer, it covers low-, mid-, and high- level processes: ďź low-level: inputs and outputs are images ďź mid-level: outputs are attributes extracted from input images ďź high-level: an ensemble of recognition of individual objects 17
- 18. ELEMENTS OF DIGITALIMAGE PROCESSING SYSTEMS âşThe basic operations performed in a digital image processing systems include 1. Acquisition 2. Storage 3. Processing 4. communication and 5. display 18
- 19. 19 ELEMENTS OF DIGITALIMAGE PROCESSING SYSTEMS
- 20. 20 âşThe basic componentsused in a digital image processing systems include 1. Image Sensors 2. Specialized hardware 3. Computer 4. Software 5. Mass storage 6. Display 7. Hardcopy 8. Networking/ communication elements ELEMENTS OF DIGITAL IMAGE PROCESSING SYSTEMS
- 21. 21 1. Image Sensors:With reference to sensing, primary element required to acquire digital image is a physical device that is sensitive to the energy radiated by the object. Energy radiated by the object is converted to electrical energy at localized places. 2. Specialized image processing hardware: It consists of the hardware that performs other primitive operations such as an arithmetic logic unit, which performs arithmetic and logical operations in parallel on images.
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- 24. 24 3. Computer: Itis a general-purpose computer and can range from a PC to a supercomputer depending on the application. In dedicated applications, sometimes specially designed computer are used to achieve a required level of performance. 4. Software: It consists of specialized modules that perform specific tasks a well-designed package also includes capability for the user to write code, as a minimum, utilizes the specialized module. More sophisticated software packages allow the integration of these modules.
- 25. 25 5. Mass storage:This capability is a must in image processing applications. An image needs lot of storage space if the image is not compressed .Image processing applications falls into three principal categories of storage. i) Short term storage for use during processing ii) Online storage for relatively fast retrieval iii) Archival storage such as magnetic tapes and disks 6. Image display: Devices designed to present visual content in the form of images. They are widely used in various applications, including computers, televisions, mobile devices, and public presentations. Displays driven by the outputs of image and graphics displays cards that are an integral part of computer system. Types of Image Displays: LCD (Liquid Crystal Display), LED (Light Emitting Diode) Display, OLED (Organic Light Emitting Diode), Plasma Display.
- 26. 26 7. Hardcopy devices:The devices for recording image includes laser printers, film cameras, heat sensitive devices inkjet units and digital units such as optical and CD ROM disk. Films provide the highest possible resolution, but paper is the obvious medium of choice for written applications. 8. Networking/Communication: It 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 bandwidth.
- 27. Key Stages inDigital Image Processing Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression 27
- 28. Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain ColourImage Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 28 Key Stages in Digital Image Processing: Image Acquisition
- 29. âşAcquire images fromsensors âşPerform A/D conversion (Sampling and quantization) âşOutput are images 29
- 30. Key Stages inDigital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 30
- 31. âşManipulating image tobe more suitable than previous. âşIt is a subjective process. âşPerformed on Spatial and frequency domains. 31
- 32. Key Stages inDigital Image Processing: Image Restoration Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 32
- 33. âşRestore the imagesfrom the degradations / noises. âşIt is an objective process. âşBased on mathematical / probabilistic models 33 Keynote: Restoration is about fixing defects, while enhancement is about improving aesthetics or usability to make it more visually appealing/informative.
- 34. Key Stages inDigital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 34
- 35. âşTechnique used inimage analysis that focuses on the structure or shape of objects within an image to output image attributes. âşIt uses set theory concepts and operates based on the spatial arrangement of pixels. âşDeals with tools to extract image components useful for representation and description. âşe.g. Shrinking or expanding image boundaries 35
- 36. Key Stages inDigital Image Processing: Segmentation Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 36
- 37. âşPartition distinct regionsor segments to simplify or change its representation. âşIt separates objects from the background or partitions an image based on features like color, texture, or intensity. 37
- 38. Key Stages inDigital Image Processing: Object Recognition Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 38
- 39. Key Stages inDigital Image Processing: Representation & Description Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002) 39
- 40. âşRepresentation converts theraw image data to suitable form for computer analysis. âşBoundary Representation: Represents the shape of objects using edges or contours. âşRegion Representation: Focuses on the entire area or region occupied by an object, including its texture, intensity, or color. âşThe goal is to retain relevant information while reducing complexity. âşDescriptions involves extracting features or attributes from the represented image to characterize it (differentiates one class from other). 40
- 41. Key Stages inDigital Image Processing: Image Compression Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression 41
- 42. âşReduces the imagesize suitable for storage, transmission and manage bandwidth. 42
- 43. Key Stages inDigital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphologica l Processing Segmentation Representatio n & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression 43
- 44. âşPleasing presentations andmore understandings. 44
- 45. 45 A Simple ImageFormation Model đ (đĽ , đŚ)=đ(đĽ , đŚ).đ (đĽ, đŚ) e.g. reflectance: 0.01 for black velvet, 0.65 for stainless steel, 0.80 for flat-white wall paint, 0.90 for silver-plated metal, 0.93 for snow
- 46. 46 Image Sampling andQuantization Digitizing the coordinate values Digitizing the amplitude values
- 47. 47 Image Sampling andQuantization
- 48. How Black andWhite /Grayscale Digital Image is stored on Computer ? 48
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- 50. ⢠The resolutionof a digital image depends on two parameters i. No. of samples of the digital image (N) ii. No. of grey levels (m) ⢠The more these parameters are increased, the closer the digitized array approximates the original image. However, the amount of memory space and the processing requirements increase drastically as function of these two factors. ⢠A good image is difficult to define, because image quality not only is highly subjective, but is also strongly dependent on the requirements of a given application. The quality of an image is greatly varied by variation in the above-mentioned factors. Spatial Resolution and Tonal Resolution
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- 52. 52 âşSpatial resolution canbe defined as the smallest discernible detail in an image. (Digital Image Processing - Gonzalez, Woods). It is determined by the number of pixels per inch used to represent image. âşGenerally, number of pixels of digital image is changed as a power of two keeping the display area same to change spatial resolution. âşSpatial resolution is often measured in pixels per unit area (e.g., pixels per inch - PPI or dots per inch - DPI or lines per inch - LPI). âşHigher spatial resolution means more pixels, resulting in finer details. âşIf we have to compare the two images, to see which one is clearer or which has more spatial resolution, we must compare two images of the same size. Spatial Resolution
- 53. Intensity / TonalResolution 53
- 54. Intensity / TonalResolution 54 âşIn Tonal Resolution, the quality of images is varied by decreasing the number of bits used to represent the no. of gray levels in an image. âşThe effects on image quality produced by varying N (number of intensity levels) and m (number of bits per pixel) are subjectively analyzed by the level of detail pertaining to an image. âşIn black-and-white images, levels are seen as shades of gray. In color images, levels will be seen as specific color hues. âşGenerally, tonal resolution for black-and-white picture information should be at least 8 bits, and tonal resolution for full color pictures information should be at least 24 bits.
- 55. 55 Representing Digital Images âşTherepresentation of an MĂN numerical array as x y
- 56. 56 Representing Digital Images âşTherepresentation of an MĂN numerical array as
- 57. 57 Representing Digital Images âşDiscrete intensity interval [0, L-1], L=2k âş The number b of bits required to store a M Ă N digitized image b = M Ă N Ă k
- 58. No. of bitsto store an Image Here assume that image is of size N X N: then number of bits= N X N X k 32 X 32 X 4 = 4096 58
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- 62. Connectivity in digitalimages 62 ⢠Connectivity in digital images refers to the way pixels are grouped based on their adjacency and intensity values, determining whether they belong to the same object or region. ⢠It plays a vital role in image analysis, particularly in tasks like segmentation, labeling, and object detection. ⢠Two pixels are connected if they are in the same class (i.e. the same color or the same range of intensity) and they are neighbors of one another.
- 63. 63 Basic Relationships BetweenPixels âşNeighborhood (4, D, 8) âşAdjacency (4, 8, m) âşConnectivity (Connected Pixels & Connected Component) âşPaths âşRegions and boundaries (Adjacent, Disjoint, Foreground, Background)
- 64. 64 Basic Relationships BetweenPixels âşNeighbors of a pixel p at coordinates (x,y) ⎠4-neighbors of p, denoted by N4(p): (x-1, y), (x+1, y), (x,y-1), and (x, y+1). ⎠4 diagonal neighbors of p, denoted by ND(p): (x-1, y-1), (x+1, y+1), (x+1,y-1), and (x-1, y+1). ⎠8 neighbors of p, denoted N8(p) N8(p) = N4(p) U ND(p)
- 65. 65 Basic Relationships BetweenPixels âşAdjacency Let V be the set of intensity values ⎠4-adjacency: Two pixels p and q with values from V are 4- adjacent if q is in the set N4(p). ⎠8-adjacency: Two pixels p and q with values from V are 8- adjacent if q is in the set N8(p). ⎠m-adjacency: Two pixels p and q with values from V are m- adjacent if (i) q is in the set N4(p), or (ii) q is in the set ND(p) and the set N4(p) ⊠N4(q) has no pixels whose values are from V.
- 66. 66 Basic Relationships BetweenPixels âş Path ⎠A (digital) path (or curve) from pixel p with coordinates (x0, y0) to pixel q with coordinates (xn, yn) is a sequence of distinct pixels with coordinates (x0, y0), (x1, y1), âŚ, (xn, yn) Where (xi, yi) and (xi-1, yi-1) are adjacent for 1 i n. ⤠⤠⎠Here n is the length of the path. ⎠If (x0, y0) = (xn, yn), the path is closed path. ⎠We can define 4-, 8-, and m-paths based on the type of adjacency used.
- 67. 67 Examples: Adjacency andPath 0 1 1 0 1 1 0 1 1 0 2 0 0 2 0 0 2 0 0 0 1 0 0 1 0 0 1 V = {1, 2}
- 68. 68 Examples of Adjacencyand Path 0 1 1 0 1 1 0 1 1 0 2 0 0 2 0 0 2 0 0 0 1 0 0 1 0 0 1 V = {1, 2} 8-adjacency of pixels m-adjacency of pixel 4-adjacency of pixels
- 69. 69 Examples: Adjacency andPath 01,1 11,2 11,3 0 1 1 0 1 1 02,1 22,2 02,3 0 2 0 0 2 0 03,1 03,2 13,3 0 0 1 0 0 1 V = {1, 2} 8-adjacent m-adjacent The 8-path from (1,3) to (3,3): (i) (1,3), (1,2), (2,2), (3,3) (ii) (1,3), (2,2), (3,3) The m-path from (1,3) to (3,3): (1,3), (1,2), (2,2), (3,3)
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- 71. 71 0 1 11 1 0 0 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 Solution of this example at: https://www.youtube.com/watch?v=5EAqF9f58P4 Find shortest 4-path, 8-path and m-path between highlighted pixels if V= { 1 } ⢠For 4 path: Go Horizonally or Vertically ⢠For 8 path: Go Horizontally, Vertically or Diagonally ⢠For m path: Go Horizontally or Vertically. If Horizontal or vertical path does not exists, then only go diagonally otherwise donât.
- 72. 72 Basic Relationships BetweenPixels âşConnected in S Let S represent a subset of pixels in an image. Two pixels p with coordinates (x0, y0) and q with coordinates (xn, yn) are said to be connected in S if there exists a path (x0, y0), (x1, y1), âŚ, (xn, yn) Where ,0 ,( , ) i i i i n x y S ď˘ ďŁ ďŁ ď
- 73. 73 Basic Relationships BetweenPixels Let S represent a subset of pixels in an image âş For every pixel p in S, the set of pixels in S that are connected to p is called a connected component of S. âş If S has only one connected component, then S is called Connected Set. âş We call R a region of the image if R is a connected set âş Two regions, Ri and Rj are said to be adjacent if their union forms a connected set. âş Regions that are not to be adjacent are said to be disjoint.
- 74. 74 Basic Relationships BetweenPixels âş Boundary (or border) ⎠The boundary of the region R is the set of pixels in the region that have one or more neighbors that are not in R. ⎠If R happens to be an entire image, then its boundary is defined as the set of pixels in the first and last rows and columns of the image. âş Foreground and background ⎠An image contains K disjoint regions, Rk, k = 1, 2, âŚ, K. Let Ru denote the union of all the K regions, and let (Ru)c denote its complement. All the points in Ru is called foreground; All the points in (Ru)c is called background.
- 75. 75 Question 1 âşIn thefollowing arrangement of pixels, are the two regions (of 1s) adjacent? (if 8-adjacency is used) 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 Region 1 Region 2
- 76. 76 Question 2 âşIn thefollowing arrangement of pixels, are the two parts (of 1s) adjacent? (if 4-adjacency is used) 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 Part 1 Part 2
- 77. 77 âşIn the followingarrangement of pixels, the two regions (of 1s) are disjoint (if 4-adjacency is used) 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 Region 1 Region 2
- 78. 78 âşIn the followingarrangement of pixels, the two regions (of 1s) are disjoint (if 4-adjacency is used) 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 foreground background
- 79. 79 Question 3 âşIn thefollowing arrangement of pixels, the circled point is part of the boundary of the 1-valued pixels if 8-adjacency is used, true or false? 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0
- 80. 80 Question 4 âşIn thefollowing arrangement of pixels, the circled point is part of the boundary of the 1-valued pixels if 4-adjacency is used, true or false? 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0
- 81. 81 Question âşIn the followingarrangement of pixels, the circled point is part of the boundary of the 1-valued pixels if 8-adjacency is used, true or false? 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0
- 82. 82 Question âşIn the followingarrangement of pixels, the circled point is part of the boundary of the 1-valued pixels if 4-adjacency is used, true or false? 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0
- 83. Adjacency, Connectivity, Regionsand Boundaries 83
- 84. Representing Color âşComputer graphics/Images:RGB âşR: 0 to 255, G: 0 to 255, B: 0 to 255 Red ⢠255 ⢠255 Green ⢠255 ⢠255 Blue ⢠255 ⢠0 Color ⢠White ⢠Yellow 84
- 85. âşFew application discussionslides numbered from 85-110 (Can be ignored. Additional information) 85
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- 90. Fingerprint Identification Research atUNR Minutiae Matching Delaunay Triangulation 90
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- 92. Object Recognition Research referenceview 1 reference view 2 novel view recognized 92
- 93. Indexing into Databases âşShapecontent 93
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- 95. Target Recognition âşDepartment ofDefense (Army, Airforce, Navy) 95
- 96. Interpretation of aerialphotography is a problem domain in both computer vision and registration. Interpretation of Aerial Photography 96
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- 105. Hand Gesture Recognition âşSmartHuman-Computer User Interfaces âşSign Language Recognition 105
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- 107. Medical Applications âş skincancer breast cancer 107
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- 109. Inserting Artificial Objectsinto a Scene 109
- 110. Companies In thisField In India âşSarnoff Corporation âşKritikal Solutions âşNational Instruments âşGE Laboratories âşIttiam, Bangalore âşInterra Systems, Noida âşYahoo India (Multimedia Searching) âşnVidia Graphics, Pune (have high requirements) âşMicrosoft research âşDRDO labs âşISRO labs âşâŚ 110