- 1. Fuzzy image processing SUPERVISOR: ENSAF AL ZORQA
- 2. ----The most important topics-- --
- 4. What is an image? An image refers to a 2D light intensity function f(X,Y) denote spatial coordinates and the value of f at any point (X,Y) is proportional to the brightness or gray levels of the image at that points.
- 5. Image Processing Types of image: 1-Binary Image: It is the values of the image is zeros or ones and either black or white. Can be referred to within the binary image(1 bit per pixel). It can convert all kinds of pictures into a binary image by Threshold
- 6. 2-Gray _Scale _Image: This represents the kind of images on the basis of a single color of the image or the so-called (Monochrome).It has information just about lighting not about colors of image Image Processing
- 7. 3-Color Image : Color image has a specific model consists of (3 Band) every Band represent one color and every color referred to it's by specific representation it is red- green-blue,(RGB) . Each color takes 8-bit , So each color image has a 24-bit. Image Processing
- 8. 4-Multi spatial Image: it's consist of Gradients of colors . Image Processing 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, Pixel values typically represent gray levels, colors, heights, opacities etc..
- 10. Remember digitization implies that a digital image is an approximation of a real scene. Image Processing
- 11. Image Processing Set of computational techniques for analyzing, enhancing, compressing, and reconstructing image. Image Processing
- 12. The applications of image processing : Image Processing 1- news- paper industry
- 13. 2- in computing technology Image Processing
- 14. 3- in medical applications Image Processing
- 15. 4-In Image enhancement/restoration Image Processing
- 16. 4- Artistic effects Image Processing
- 18. 6- Law enforcement Image Processing
- 19. 7-Human computer interfaces Image Processing
- 20. step of image processing:- 1-Image acquisition: to acquire a digital image. 2-Image Pre-processing: to improve the image in ways that increase the chances for success of the other process. 3-Image Segmentation: to partition an input image in to its constituent parts or objects. 4-Image Representation: to convert the input data to a form suitable for computer processing. Image Processing
- 21. 5-Image Description: to extract features that results in some quantitative information of interest or features that are basic for differentiating one class of objects from another. 6-Image Recognition: to assign a label to an object based on the information provided by its description. 7-Image Interpretation: to assign meaning to an ensemble of recognized objects. Image Processing
- 22. The major topics within the field of image processing include: 1-Image restoration: The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image. Degradation comes in many forms such as motion blur, noise. Image Processing
- 23. a. Image with distortion b. Restored image Image Processing
- 24. Image Processing There are many methods used in the image processing world to restore images: 1-Inverse Filtering 2-blind deconvolution 3-Wavelet-based Image Restoration 4-Wiener Filtering
- 25. Image Processing 2-Image enhancement: Involves taking an image and improving it visually, typically by taking advantages of human Visual Systems responses. One of the simplest enhancement techniques is to simply stretch the contrast of an image.
- 26. Image Processing a. image with poor contrast b. Image enhancement by contrast stretching
- 27. Image Processing The enhancement methods can broadly be divided in to the following two categories: 1-Spatial Domain Methods:- we directly deal with the image pixels. The pixel values are manipulated to achieve desired enhancement. 2-Frequency Domain Method:- the image is first transferred in to frequency domain. It means that, the Fourier Transform of the image is computed first.
- 28. Image Processing 3-Image compression: Involves reducing the typically massive amount of data needed to represent an image. This done by eliminating data that are visually unnecessary and by taking advantage of the redundancy that is inherent in most images.
- 29. Image Processing a. Image before compression b. Image after compression
- 30. Fuzzy Logic
- 31. All the science was meant to benefit the people in their lives, and more precisely in solving their problems of life by simplifying the problem and deal with it then analyzed to reach the appropriate solutions. It was a ways to solve problems, how to express expressions that describe the amounts of stuff, expressions that are ambiguous, or somewhat similar, preoccupy scientists and specialists. Overview
- 32. What color is this leopard? Overview
- 33. Is this glass full or empty? Overview
- 34. A little hot air. Also varies depending on the expression of the atmosphere of the person and his place of residence .. temperature of 20 Celsius he may judge Gulf population cool While he sees the population of Scandinavia hot. Fuzzy logic
- 35. The previous example we use a lot in our daily lives, but if we come to the process of converting or translating these sentences to specific software for processing, we will see that programmers will suffer from how to deal with these sentences Fuzzy logic
- 36. -From the above examples it is clear the presence of gradients of any expression .. Hot air, cold, moderate, relatively cool, relatively hot. -So the problem is how to express what degree of belonging to the description described it, such as temperature, length, weight, speed, intelligence and beauty .... Etc. Fuzzy logic
- 37. The fuzzy logic is a form of logic, is used in some expert systems and applications of artificial intelligence, originated this logic in 1965 by scientist Azerbaijani origin, "Lotfi Zadeh,“ University of California but the theory did not receive attention until 1974. History of Fuzzy logic
- 38. where he noted that right and wrong are not enough to represent all forms of logical and especially the problems that confront us currently. History of Fuzzy logic
- 39. The Classical logic (Boolean) depends on only 0 or 1, and this depends upon a lot of relationships, while there are other relationships where the position that it can be considered partly true or partly wrong at the same time. History of Fuzzy logic
- 40. definitions of fuzzy logic is a technique through which determine the degree of affiliation or degree of health, which is the extent of grades between right and wrong, and this is the difference between him and Boolean logic, which only knows right and wrong (True - False). Fuzzy logic
- 41. Fuzzy Sets :- - Any set of elements or objects with which it has a relationship with each other and belong to the same definition called the set . The contents of the set here called scientifically elements or members. -It must here to know that the set and its members must be available by the following conditions: 1- non-recurring. 2-Be clear in terms of the relationship that linked to each other. Fuzzy Sets
- 42. Suppose we wanted to buy a sports car ... the car will be evaluated by the maximum speed Fuzzy Sets
- 43. So we will create a standard or the following law:- ( which exceed the car a top speed of 280 km / h are fast) and symbolizes the requirement for speed sports car (μ), The following graph illustrates the selection process .. Fuzzy Sets
- 44. Fuzzy Sets
- 45. As it turns out the picture above:- 1- May draw the line between cars proposed the so-called status of sharp edged membership functions, and including means the process of separation between the two parts fully. 2- The maximum speed of the car, if they exceed the 280 it is fast. What is classified if the car speed of 270 or 279?? According to the law, and the drawing shown above, the car is slow because it did not exceed the maximum speed of 280 km / h. Fuzzy logic
- 46. * If we have a problematic with the speed one unit * In theory, the speed there is no difference between a car that exceeded the 280, which exceeded the 270 and even the 240 they are all fast cars because the difference is very simple * To resolve this issue has been the use of fuzzy set theory, which knew how to differentiate between the two cultivars in a satisfactory manner, depending on how the process of representation fuzzy set for these cases is much better than using the usual sports groups, they contain values and sequential gradient between zero and one Fuzzy logic
- 47. I meant between zero and one is between acceptable and unacceptable True or False, there is no barrier between the two cultivars, but it will be graded between car values that exceed a top speed of between 280 and a top speed in excess of 246, for example. Here would be a fair division process, as we see in the following image: Fuzzy logic
- 48. Fuzzy logic
- 49. In the picture above we found out something very important in the principle of uncertainty, which is a function of belonging, which means that the speed of 270, the proportion of affiliation to the required speed is 95%, which is better than the speed of 260 that the ratio of affiliation, for example, 85%, and this could be the car that speed within 270 fast cars because it has a higher percentage than other cars here have been proposed to solve the problem the process of developing a sequential ratios between the requirement for acceptance and rejection, or between the value of right and wrong. Fuzzy logic
- 50. Definitions Fuzzy set:- As defined by dr. Lotfi zadeh, in his famous publication in 1965, is a set of objects that possess valuable sequential and graded for affiliation to those group. Fuzzy logic
- 51. What is the function of belonging Membership Function? Is the function that determines the proportion of the element belonging to that fuzzy set. Fuzzy logic
- 53. Fuzzy logic
- 54. Applications of fuzzy logic in practical life 1-artificial intelligence 2-operational control Some applications are as follows: Video cameras Sensing the movement of things Fuzzy logic
- 55. Cars Possibility of controlling the speed Air conditioning Gradually reducing the heat Fuzzy logic
- 57. Fuzzy image processing Using fuzzy logic in image processing - fuzzy logic aims to model the vagueness and ambiguity in complex systems - In recent years the concept of fuzzy logic has been extended to image processing by Hamid Tizhoosh.
- 58. Fuzzy image processing Fuzzy image processing is not a unique theory . It is a collection of different fuzzy approaches to image processing. The representation and processing depend on the selected fuzzy technique and on the problem to be solved.
- 59. Fuzzy image processing Fuzzy image processing is divided into three main stages: image fuzzification . modification of membership values. image defuzzification.
- 61. Fuzzy image processing we encode image data (fuzzification) and decode the results (defuzzification) to process images by means of fuzzy techniques. -The main power of fuzzy image processing is mainly in the intermediate step, a change of membership function values.
- 62. Fuzzy image processing Fig.2. Steps of fuzzy image processing
- 63. The Advantages of Fuzzy Image Processing There are many reasons to use Fuzzy technique. The most important of them are as follows: Fuzzy techniques are powerful tools for knowledge representation and processing. Fuzzy techniques can manage the vagueness and ambiguity efficiently. • Fuzzy image processing
- 64. Fuzzy image processing three other kinds of imperfection in the image processing:- ● Grayness ambiguity ● Geometrical fuzziness ● Vague (complex/ill-defined) knowledge
- 65. Fuzzy image processing Fig.3. Uncertainty/imperfect knowledge in image processing
- 66. Fuzzy image processing -“There are many classical thresholding techniques used in image processing,” says Tizhoosh -and recently the concept of image fuzziness has been used to develop new thresholding techniques. For example, a standard S membership function can be moved pixel by pixel over the existing range of gray levels and in each position, a measure of fuzziness calculated.
- 67. Fuzzy image processing Tizhoosh distinguishes between the following kinds of image fuzzification: 1.Histogram-based gray-level fuzzification (or briefly histogram fuzzification) Example: brightness in image enhancement 2.Local fuzzification (Example: edge detection) 3.Feature fuzzification (Scene analysis, object recognition)
- 68. Fuzzy image processing Theory of Fuzzy Image Processing the most important theoretical components of fuzzy image processing: # Fuzzy Geometry (Metric, topology, ...) # Measures of Fuzziness and Image Information (entropy, correlation, divergence, expected values, ...) # Fuzzy Inference Systems (image fuzzification, inference, image defuzzification) # Fuzzy Clustering (Fuzzy c-means, possibilistic c-means, ...)
- 69. Fuzzy image processing # Fuzzy Mathematical Morphology (Fuzzy ersion, fuzzy dilation, ...) # Fuzzy Measure Theory (Sugeno measure/integral, possibility measures, necessity measures,...) # Fuzzy Grammars # CombinedApproaches (Neural fuzzy/fuzzy neural approaches, fuzzy genetic algorithms, fuzzy wavelet analysis) # Extension of classical methods (Fuzzy Hough transform, fuzzy median filtering, ...)
- 70. Fuzzy image processing Applications of Fuzzy Logic in Image Processing Noise Detection and Removal Edge Detection Segmentation Contrast Enhancement Geometric measurement Scene analysis (Region Labeling)
- 71. Fuzzy image processing Fuzzy filter -The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges.1. -The main concern of the proposed filter is to distinguish between local variations due to noise and due to image structure.
- 72. Fuzzy image processing Noise Reduction by Fuzzy Image Filtering The filter consists of two stages:- - The first stage computes a fuzzy derivative for eight different directions. - The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions.Fuzzy techniques have already been applied in several domains of image processing (e.g., filtering, interpolation , and morphology )
- 75. Fuzzy image processing The further construction of the filter is then based on the observation that -a small fuzzy derivative most likely is caused by noise. -while a large fuzzy derivative most likely is caused by an edge in the image.
- 76. Fuzzy image processing Color Image Noise Reduction Using Fuzzy Filtering ( N. Baker et al., 2008)
- 77. Fuzzy image processing Edge detection :- is an essential feature of digital image processing .It is approach used most frequently in image segmentation based on abrupt changes in intensity
- 78. • Fuzzy image processing Fuzzy Inference System The new fuzzy rule based edge detection system is developed by designing a Fuzzy Inference System (FIS) of type using MATLAB toolbox The algorithm detects edges of an input image by using a window mask of 2x2 size that slides over the whole image horizontally pixel by pixel. - The FIS is implemented by considering four inputs which correspond to four pixels P1, P2, P3 and P4 of the 2*2 mask in Figure-1 and one output variable P2 x(I,1,j)P1 x(I,1,1) P4 x(I,j)P3x(I,j,1)
- 79. Fuzzy image processing Experiments -The new approach developed for edge detection has been tested for different images. -The algorithm is able to detect very sharp and distinct edges for different kind of images. -Some sample images are shown in Figure 5(a) and 6(a). -The algorithm is also tested with different image formats like JPG, PNG, BMP and it works suitably with all these variety of image formats.
- 80. Fuzzy image processing Figure 5(a): Sample Image -1 Figure 5(b): Edges detected by the algorithm
- 81. Fuzzy image processing Figure 6(a): Sample Image-2 Figure 6(b): Edges detected by the algorithm
- 82. • Fuzzy image processing SEGMENTATION USING FUZZY LOGIC The following points give a brief overview of different fuzzy approaches to image segmentation:- 1. Fuzzy Clustering: Algorithms Fuzzy clustering is the oldest fuzzy approach to image segmentation. Algorithms such as fuzzy c-means (FCM) can be used to build clusters (segments). The class membership of pixels can be interpreted as similarity or compatibility with an ideal object or a certain property.
- 83. • Fuzzy image processing 2. Fuzzy Rule-Based Approach: If we interpret the image features as linguistic variables, then we can use fuzzy if-then rules to segment the image into different regions. A simple fuzzy segmentation rule may seem as follows: IF the pixel is dark AND its neighbourhood is also dark AND homogeneous THEN it belongs to the background. 3.Fuzzy Integrals: - Fuzzy integrals can be used in different forms: Segmentation by weightening the features (fuzzy measures represent the importance of particular features). - Segmentation by fusion of different sensors (e.g. multi spectral images)
- 84. • Fuzzy image processing 4.Measures of Fuzziness and image: Measures of fuzziness (e.g. fuzzy entropy) and image information (e.g.fuzzy divergence) can be also used in segmentation and thresholding tasks. 5.Fuzzy Geometry: Fuzzy geometrical measures such as fuzzy compactness and index of area coverage can be used to measure the geometrical fuzziness of different regions of an image.
- 85. Thank You For Your Attention • Fuzzy image processing