Your SlideShare is downloading.
×

×

Saving this for later?
Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.

Text the download link to your phone

Standard text messaging rates apply

Like this presentation? Why not share!

- Digital image processing by Deevena Dayaal 47 views
- Multiplexing ppt15 sep by Srashti Vyas 209 views
- 15857 cse422 unsupervised-learning by Anil Yadav 467 views
- Image Processing - Components of an... by Nidhal El Abbadi 6483 views
- Cluster analysis by Venkata Reddy Kon... 4785 views
- Introduction to Digital Image Proce... by Visvesvaraya Nati... 25098 views
- Digital image processing using matlab by Amr Rashed 705 views
- Image pre processing by Ashish Kumar 10566 views
- Noise filtering by Alaa Ahmed 4209 views
- Image Processing - Representing Dig... by Nidhal El Abbadi 1402 views
- Image Processing With Sampling and ... by International Jou... 1297 views
- Introduction to digital image proce... by Kalyan Acharjya 644 views

No Downloads

Total Views

383

On Slideshare

0

From Embeds

0

Number of Embeds

0

Shares

0

Downloads

15

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Digitized Images and its Properties Ashish Khare M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, PWS Publishing.12/06/12 1
- 2. Image Functions (cont.) A signal is a function depending on some variable with physical meaning. Signals can be one-dimensional (e.g., dependent on time), two-dimensional (e.g., images dependent on two co-ordinates in a plane), three-dimensional (e.g., describing an object in space), or higher-dimensional. A scalar function may be sufficient to describe a monochromatic image, while vector functions are to represent, for example, color images consisting of12/06/12 component colors. three 2
- 3. Image Functions (cont.) The image can be modeled by a continuous function of two or three variables. arguments are co-ordinates x, y in a plane, while if images change in time a third variable t might be added. The image function values correspond to the brightness at image points. The function value can express other physical quantities as well (temperature, pressure distribution, distance from the observer, etc.).12/06/12 3
- 4. Image Functions (cont.) The brightness integrates different optical quantities - using brightness as a basic quantity allows us to avoid the description of the very complicated process of image formation. The image on the retina or on a TV camera sensor is intrinsically 2D. We call such a 2D image bearing information about brightness points an intensity image. The 2D intensity image is the result of a perspective projection of the 3D scene.12/06/12 4
- 5. Image Functions (cont.) When 3D objects are mapped into the camera plane by perspective projection a lot of information disappears as such a transformation is not one-to-one. Recognizing or reconstructing objects in a 3D scene from one image is an ill-posed problem. Recovering information lost by perspective projection is only one, mainly geometric, problem of computer vision.12/06/12 5
- 6. Image Functions (cont.) The second problem is how to understand image brightness? The only information available in an intensity image is brightness, which is dependent on a number of independent factors such as object surface reflectance properties (given by the surface material, microstructure and marking), illumination properties, and object surface orientation with respect to a viewer and light source.12/06/12 6
- 7. Image Functions (cont.) Some scientific and technical disciplines work with 2D images directly; for example, a character drawn on a sheet of paper, the image of a fingerprint, etc. Many basic and useful methods used in digital image analysis do not depend on whether the object was originally 2D or 3D. Related disciplines are photometry which is concerned with brightness measurement, and colorimetry which studies light reflectance or emission depending on wavelength.12/06/12 7
- 8. Image Functions (cont.) A light source energy distribution C(x,y,t,λ) depends in general on image co-ordinates (x, y), time t, and wavelength λ. For the human eye and image sensors (e.g., TV cameras) the brightness f depends on the lightsource energy distribution C and the spectral sensitivity of the sensor, S(λ) (dependent on the wavelength). A monochromatic image f(x,y,t) provides the brightness12/06/12 distribution. 8
- 9. Image Functions (cont.) In a color or multispectral image, the image is represented by a real vector function f . Image processing often deals with static images, in which time t is constant. A monochromatic static image is represented by a continuous image function f(x,y) whose arguments are two co-ordinates in the plane. Brightness values bounded by the minimum (black) and the maximum (white) limits are gray levels.12/06/12 9
- 10. Quality of Images The quality of a digital image grows in proportion to the spatial, spectral, radiometric, and time resolution. The spatial resolution is given by the proximity of image samples in the image plane. The spectral resolution is given by the bandwidth of the light frequencies captured by the sensor. The radiometric resolution corresponds to the number of distinguishable gray levels. The time resolution is given by the interval between time samples at which images are captured.12/06/12 10
- 11. Images as a Stochastic Process Images f(x,y) can be treated as deterministic functions or as realizations of stochastic processes. Mathematical tools used in image description have roots in linear system theory, integral transformations, discrete mathematics and the theory of stochastic processes.12/06/12 11
- 12. Image Digitization An image captured by a sensor is expressed as a continuous function f(x,y) of two co- ordinates in the plane. Image digitization means that the function f(x,y) is sampled into a matrix with M rows and N columns. The image quantitation assigns to each continuous sample an integer value. The continuous range of the image function f(x,y)12/06/12 is split into K intervals. 12
- 13. Image Digitization The finer the sampling (i.e., the larger M and N) and quantitation (the larger K) the better the approximation of the continuous image function f(x,y). Two questions should be answered in connection with image function sampling: First, the sampling period should be determined -- the distance between two neighboring sampling points in the image Second, the geometric arrangement of sampling points (sampling grid) should be set.12/06/12 13
- 14. Sampling A continuous image function f(x,y) can be sampled using a discrete grid of sampling points in the plane. Two neighboring sampling points are separated by distance ∆x along the x axis and ∆y along the y axis. ∆x and ∆y are called the sampling interval and the matrix of samples constitutes the discrete image.12/06/12 14
- 15. Sampling (cont.) The ideal sampling s(x,y) in the regular grid can be represented using a collection of Dirac distributions The sampled image is the product of the continuous image f(x,y) and the sampling function s(x,y)12/06/12 15
- 16. Sampling (cont.) The collection of Dirac distributions can be regarded as periodic with period x, y and expanded into a Fourier series where the coefficients of the Fourier expansion can be calculated as12/06/12 16
- 17. Sampling (cont.) In frequency domain,12/06/12 17
- 18. Sampling (cont.) Thus the Fourier transform of the sampled image is the sum of periodically repeated Fourier transforms F(u,v) of the image. Periodic repetition of the Fourier transform result F(u,v) may under certain conditions cause distortion of the image which is called aliasing; this happens when individual digitized components F(u,v) overlap. There is no aliasing if the image function f(x,y) has a band limited spectrum ......... its Fourier transform F(u,v)=0 outside a certain interval of frequencies |u| > U; |v| > V.12/06/12 18
- 19. Sampling (cont.) From general sampling theory, overlapping of the periodically repeated results of the Fourier transform F(u,v) of an image with band limited spectrum can be prevented if the sampling interval is chosen according to This is the Shannon sampling theorem that has a simple physical interpretation in image analysis: The sampling interval should be chosen in size such that it is less than or equal to half of the smallest interesting detail in the image.12/06/12 19
- 20. Sampling (cont.) The sampling function is not the Dirac distribution in real digitizers -- narrow impulses with limited amplitude are used instead. As a result, in real image digitizers a sampling interval about ten times smaller than that indicated by the Shannon sampling theorem is used - because the algorithms for image reconstruction use only a step function.12/06/12 20
- 21. Sampling (cont.) Original image of Golden Gate Bridge:12/06/12 21
- 22. Sampling (cont.) Image resampled at half the original resolution: With image size adjusted12/06/12 22
- 23. Sampling (cont.) Without image size adjusted:12/06/12 23
- 24. Sampling (cont.) Image resampled at twice the original resolution With image size adjusted12/06/12 24
- 25. Sampling (cont.) Without image size adjusted12/06/12 25
- 26. Sampling (cont.) Image which has been downsampled at one- tenth resolution, and upsampled at 10x resolution12/06/12 26
- 27. Sampling (cont.) A continuous image is digitized at sampling points. These sampling points are ordered in the plane and their geometric relation is called the grid. Grids used in practice are mainly square or hexagonal. 12/06/12 27
- 28. Sampling (cont.) One infinitely small sampling point in the grid corresponds to one picture element (pixel) in the digital image. The set of pixels together covers the entire image. Pixels captured by a real digitization device have finite sizes. The pixel is a unit which is not further divisible, sometimes pixels are also called points.12/06/12 28
- 29. Quantization A magnitude of the sampled image is expressed as a digital value in image processing. The transition between continuous values of the image function (brightness) and its digital equivalent is called quantitation. The number of quantitation levels should be high enough for human perception of fine shading details in the image.12/06/12 29
- 30. Quantization (cont.) Most digital image processing devices use quantitation into k equal intervals. If b bits are used ... the number of brightness levels is k=2b. Eight bits per pixel are commonly used, specialized measuring devices use twelve and more bits per pixel.12/06/12 30
- 31. Metric Properties of Digital Images Distance is an important example. The distance between (I,j) and (h,k) the Euclidean distance city block distance chessboard distance12/06/12 31
- 32. Metric Properties of Digital Images (cont.) Pixel adjacency is another important concept in digital images. 4-neighborhood 8-neighborhood12/06/12 32
- 33. Metric Properties of Digital Images (cont.) It will become necessary to consider important sets consisting of several adjacent pixels -- regions. Region is a contiguous set. Contiguity paradoxes of the square grid12/06/12 33
- 34. Metric Properties of Digital Images (cont.)12/06/12 34
- 35. Metric Properties of Digital Images (cont.)12/06/12 35
- 36. Metric Properties of Digital Images (cont.) One possible solution to contiguity paradoxes is to treat objects using 4-neighborhood and background using 8-neighborhood (or vice versa). A hexagonal grid solves many problems of the square grids ... any point in the hexagonal raster has the same distance to all its six neighbors.12/06/12 36
- 37. Metric Properties of Digital Images (cont.) Border R is the set of pixels within the region that have one or more neighbors outside R ... inner borders, outer borders exist. Edge is a local property of a pixel and its immediate neighborhood --it is a vector given by a magnitude and direction. The edge direction is perpendicular to the gradient direction which points in the direction of image function growth. Border and edge ... the border is a global concept related to a region, while edge expresses local properties of an image function.12/06/12 37
- 38. Metric Properties of Digital Images (cont.) Crack edges ... four crack edges are attached to each pixel, which are defined by its relation to its 4-neighbors. The direction of the crack edge is that of increasing brightness, and is a multiple of 90 degrees, while its magnitude is the absolute difference between the brightness of the relevant pair of pixels.12/06/12 38
- 39. Topological Properties of Images Topological properties of images are invariant to rubber sheet transformations. Stretching does not change contiguity of the object parts and does not change the number of holes in regions. One such image property is the Euler--Poincare characteristic defined as the difference between the number of regions and the number of holes in them.12/06/12 39
- 40. Topological Properties of Images (cont.) Convex hull is used to describe topological properties of objects. The convex hull is the smallest region which contains the object, such that any two points of the region can be connected by a straight line, all points of which belong to the region.12/06/12 40
- 41. Histograms Brightness histogram provides the frequency of the brightness value z in the image.12/06/12 41
- 42. Noise in Image Images are often degraded by random noise. Noise can occur during image capture, transmission or processing, and may be dependent on or independent of image content.12/06/12 42
- 43. Noise in Image (cont.) White noise - constant power spectrum (its intensity does not decrease with increasing frequency). Gaussian noise is a very good approximation of noise that occurs in many practical cases. During image transmission, noise which is usually independent12/06/12 of the image signal occurs. 43
- 44. Noise in Image (cont.) Noise may be additive noise multiplicative noise impulse noise (saturated = salt and pepper noise)12/06/12 44
- 45. Noise in Image (cont.)Original Image Noise Noisy Image 12/06/12 45

Be the first to comment