Basic Concepts & Applications of An Image.

1. Basic Concepts & Applications of An Image.

2. Digital Images Digital picture produced in 1921 from a coded tape by a telegraph printer with special type faces.

3. Digital Images Digital picture made in 1922 from a tape punched after the signals had crossed the Atlantic twice.

4. Digital Images Unretouched cable picture of Generals Pershing and Foch, transmitted in 1929 from London to New York by 15-tone equipment.

5. Digital Images The first picture of the moon by a U.S . spacecraft . Ranger took this image on July 31, 1964 at 9:09 A.M. EDT about 17 minutes before impacting the lunar surface .

6. Image Sources • Images can be categorized according to their source (e.g., visual, X-ray, and so on). Principal energy source for images • Electromagnetic energy spectrum. • acoustic, ultrasonic, • Electronic (in the form of electron beams used in electron microscopy). • Synthetic images, used for modeling and visualization,are generated by computer

7. Electron Microscopy • The electron microscope is a type of microscope that uses a beam of electrons to create an image of the specimen. It is capable of much higher magnifications and has a greater resolving power than a light microscope, allowing it to see much smaller objects in finer detail.

8. Fundamental Steps in Digital Image Processing • Image Acquisition Image acquisition is the first process. Acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling. • A digital image is produced by one or several image sensors. • These sensors may include: • Light-sensitive cameras • Range sensors • Tomography devices • Radar and ultra-sonic cameras, etc.

9. Fundamental Steps in Digital Image Processing • Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. • The pixel values typically correspond to light intensity in one or several spectral bands

10. Steps involved:

11. Fundamental Steps in Digital Image Processing • Image enhancement 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 is when we increase the contrast of an image because "it looks better."

12. Fundamental Steps in Digital Image Processing • 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.

13. Fundamental Steps in Digital Image Processing • 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. • Wavelets Wavelets are the foundation for representing images in various degrees of resolution.

14. Fundamental Steps in Digital Image Processing • 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.

15. Fundamental Steps in Digital Image Processing • Morphological processing Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. • 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.

16. Fundamental Steps in Digital Image ProcessingRepresentation and description (Feature Extraction) 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. 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. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus 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 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.

17. Summary of Previous Slide Feature extraction: • Image features at various levels of complexity are extracted from the image data. • Typical examples of such features are • Lines, edges and ridges. • Localized interest points such as corners, blobs or points. • More complex features may be related to texture, shape or motion.

18. Fundamental Steps in Digital Image Processing • Recognition Recognition is the process that assigns a label (e.g., "vehicle") to an object based on its descriptors.

19. Componets of general purpose image processing system

20. Componets of general purpose image processing system 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 output proportional to light intensity.The digitizer converts these outputs to digital data.

21. Componets of general purpose image processing system 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), which performs arithmetic and logical operations in parallel on entire images. One example of how an ALU is used is in averaging images as quickly as they are digitized, for the purpose of noise reduction. This type of hardware sometimes is called a front-end subsystem. 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. More sophisticated software packages allow the integration of those modules and general-purpose software commands from at least one computer language.

22. Componets of general purpose image processing system Mass storage capability is a must in image processing applications. An image of size 1024 X 1024 pixels, in which the intensity of each pixel is an 8-bit quantity, requires one megabyte of storage space if the image is not compressed. When dealing with thousands, or even millions, of images, providing adequate storage in an image processing system can be a challenge. Digital storage for image processing applications falls into three principal categories: • short term storage for use during processing, • on-line storage for relatively fast recall, and • archival storage, characterized by infrequent access. Image displays in use today are mainly color (preferably flat screen) TV monitors. Monitors are driven by the outputs of image and graphics display cards that are an integral part of the computer system.

23. Componets of general purpose image processing system Hardcopy devices for recording images include laser printers, film cameras, heat-sensitive devices, inkjet units, and digital units, such as optical and CD-ROM disks. 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. Fortunately, this situation is improving quickly as a result of optical fiber and other broadband technologies.

24. Simple Image Model • Images are denoted by two-dimensional functions of the form f(x, y). The value or amplitude of / at spatial coordinates (x, y) is a positive scalar quantity . • f(x, y) must be nonzero and finite; that is, • 0 < f(x, y) < The function f(x, y) may be characterized by two components: The amount of source illumination incident on the scene being viewed, The amount of illumination reflected by the objects in the scene. Appropriately, these are called the illumination and reflectance components and are denoted by i(x,y) and r(x, y), respectively.

25. What is an Image? • The two functions combine as a product to form f(x, y): • f(x,y) = i(x,y)r(x,y) where 0 < i(x, y) < and 0 < r(x,y) < 1. The following average numerical figures illustrate some typical ranges of i(x, y) for visible light. • On a clear day, the sun may produce in excess of 90,000 lm/m2 of illumination on the surface of the Earth. • On a cloudy day it decreases to less than 10,000 lm/m2. • On a clear evening, a full moon yields about 0.1 lm/m2 of illumination. • The typical illumination level in a commercial office is about 1000 lm/m2.

26. Reflection r(x, y) Values Typical values of reflection r(x, y) are: • 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.

27. What is an Image? • Illumination is the amount of light falling on the object, and , this is property of light source. • Reflectance is the light reflected back from object and this remains between 0 & 1. • Reflectance=0 (Transparent objects) • Reflectance=1 (Opaque objects)

28. Digital Image: • A digital image is an image f(x,y) that has been “discritized” both in spatial & in brightness. • A 2D matrix whose rows & columns identify a unique point in the image. • The corresponding matrix element value identifies the gray-level level at that point. • The elements of such a digital array are called image elements ,picture elements , pixels or pel. • The size of the digital image varies with the application.

29. Digital Image:

30. Digitization: • A process of converting Analog Images in to Digital. • Consist of two steps. • Sampling • Digitization of spatial coordinates. • Quantization • Digitization of Amplitude Values.

31. Digitization: Analog Image Digital Image Quantization Sampling

32. Image Representation: • So, a Binary Image stored in computer can be shown as: 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 Memory Representation Image Displayed for a Digital Image

33. Sampling: • Digitization of spatial coordinates (x, y ) is referred to as Image Sampling. • How much samples are required to extract the enough information from Analog Image? • Decision is made by using famous “Sampling” Theorem. • Digitization process requires that a decision be made on the number of discrete grey levels allowed for each pixel. • The result of sampling and quantization is a matrix of real numbers.

34. Digital Image Approximation: • Suppose that a continuous Image f(x,y) is approximated by equally spaced samples to form a N*N array, such that: • f(x,y)= f(0,0) f(0,1) f(0,2) f(0,N-1) f(1,0) f(1,1) f(1,2) f(1,N-1) f(2,0) f(2,1) f(2,2) f(2,N-1) . . . . . . . . . . . . f(N-1,0) f(N-1,1) f(N-1,2) f(N-1,N-1)

36. Quantization: • Amplitude Digitization is called Gray-level Quantization. • In Digital Image Processing let these quantities be integer powers of two; that is, • N = 2n and G = 2m • Where G denotes number of Gray level and Discrete levels are equally spaced between 0-L

37. Spatial & Gray Level Resolution: • It may be defined as the degree of discrete details of an image which is strongly dependent on both n and m. • The more these parameters are increased, the closer the digitized array will approximate the original image. • By reducing the number of samples an image is distorted (less information is available). • By decreasing the number of gray levels we get imperceptible image and is called False Contouring.

38. Spatial & Gray Level Resolution The quality of an image strongly depends upon the number of samples and gray levels; the more are these two, the better would be the quality of an image. But, this will result in a large amount of storage space as well because the storage space for an image is the product of dimensions of an image and the number of bits required to store gray levels. At lower resolution, an image can result in checkerboard effect or graininess. When an image of size 1024 * 1024 is reduced to 512 * 512, it may not show much deterioration, but when reduced to 256 * 256 and then rescaled back to 1024 * 1024 by duplication, it might show discernible graininess.

39. Original Image (MUET central Library) Muet Central Library

40. 500*450 450*500 Muet Central Library

41. 300*200 150*100 Muet Central Library

42. 100*50 20*30 Muet Central Library

43. Gray- Level: • Intensity of monochrome image f at coordinates ( x, y ) is called gray level (L) of the image at that point of the image. • Where L lies in the range: L min <= L <= L max.

44. Gray-Level of Digital Image: 64 gray-level Image 32 gray-level Image Muet Central Library

45. Gray-Level of Digital Image: 16 gray-level Image 8 gray-level Image Muet Central Library

46. Gray-Level of Digital Image: 4 gray-level Image 2 gray-level Image Muet Central Library

47. Gray Scale: • The Intensity value of any Pixel is called as Gray Level Value, and it is denoted by ‘ L ’. • The value of ‘L’ lies in a certain range, and this is called as Gray Scale. • [Lmin , Lmax] is the Gray Scale, such that Lmin <L< Lmax . • For Binary Images, the Gray scale used is [0,1]. • For color Images, the Gray scale is [0,255]

48. Gray Scale: • The interval between the L min and L max is usually taken from 0 to 1 (for Binary Images). • We generally have the following conventions: [0, 7 ] 8-levels [0, 15 ] 16-levels [0, 31 ] 32-levels [0, 255] 256-levels • The Low value represents BLACK. • The high value represent WHITE. • Intermediate Values give different shades.

49. Spatial and Gray-Level Resolution a b • 1024*1024 image • (b) 512*512 image resampled into 1024*1024 pixels MUET Central Library

50. Spatial and Gray-Level Resolution c d (c)256*256 image resampled into 1024*1024 pixels (d) 128*128 image resampled into 1024*1024 pixels MUET Central Library