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Image segmentation using wvlt trnsfrmtn and fuzzy logic. ppt


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This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or

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Image segmentation using wvlt trnsfrmtn and fuzzy logic. ppt

  1. 1. For more than a decade people have developed an interest forPROJECT the researching in the domainSEMINAR/GROUPNO.-7 IMAGE PROCESSING and now there’s more than a million patents have been submitted this year on this topic.And now we bring you… OUR PROJECT ON IMAGE PROCESSING
  2. 2. PART One1 INTRO TOIMAGE processing
  3. 3. Introduction Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
  4. 4. Types Image processing usually refers to digital image processing, but optical and analog image processing are also possible.
  5. 5. Image Processing Operations Geometric transformations such as enlargement, reduction, and rotation Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space Digital compositing or optical compositing (combination of two or more images). Used in filmmaking to make a "matte" Interpolation, demosaicing, and recovery of a full image from a raw image format using a Bayer filter pattern
  6. 6. Image ProcessingOperations(Contd.) Image editing (e.g., to increase the quality of a digital image) Image differencing Image registration (alignment of two or more images) Image stabilization Extending dynamic range by combining differently exposed images
  7. 7. Image Processing Applications Computer vision Face recognition Feature detection Non-photorealistic rendering Medical image processing Microscope image processing Morphological image processing Remote sensing
  9. 9. 2 PART  IMAGE
  10. 10. The Definition… The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application . Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. It basically identifies the pixels belonging to the desired object that we may want to extract from an input image.
  11. 11. More about segmentation…• To humans, an image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects.• There also exits a variety of images: natural scenes, paintings, etc. Despite the large variations of these images, humans have no problem to interpret them.
  12. 12. Segmentation can beIN TERMS OF:Grey level ColourTexture, Depth or motion
  13. 13. Introduction to imagesegmentation Example 1  Segmentation based on greyscale  Very simple ‘model’ of greyscale leads to inaccuracies in object labelling 13
  14. 14. Introduction to imagesegmentation  Example 2  Segmentation based on texture  Enables object surfaces with varying patterns of grey to be segmented 14
  15. 15. Introduction to imagesegmentation 15
  16. 16. Introduction to imagesegmentation Example 3  Segmentation based on motion  The main difficulty of motion segmentation is that an intermediate step is required to (either implicitly or explicitly) estimate an optical flow field  The segmentation must be based on this estimate and not, in general, the true flow 16
  17. 17. Introduction to imagesegmentation 17
  18. 18. TYPES OF IMAGE SEGMENTATIONA) Supervised.- These methods require the interactivity inwhich the pixels belonging to the same intensity rangepointed out manually and segmented.B) Automatic.- This is also known as unsupervised methods,where the algorithms need some prior information, so thesemethods are more complex.C) Semi-automatic.- That is the combination of manual andautomatic segmentation.
  19. 19. SEGMENTING CAN ALSO BE ONDISCONTINUITY : Partitioning an image basedon abrupt change.Edge detection in a image.SIMILARITY : Partitioning an image intoregions that are similar according to a set of predefined criteria.Thresholding , Region Growing , Clustering.
  21. 21. Definition of edge• Definition : Set of connected pixels that lie on the boundary b/w 2 regions.• Edge is a “local” concept & boundary is “global” concept.• Reasonable definition of edge requires ability to measure gray level transition in a meaningful way.
  22. 22. EDGE DETECTION• It is the most common approach for detecting meaningful discontinuities in gray level.• Process: By implementing the 1st order derivative 2nd order derivative ,edges in an image can be detected.
  23. 23. DIFFERENCE B/W EDGE & PRACTICAL EDGE IDEAL : Set of pixels ,each of which is located at an orthogonal step transition in gray level PRACTICAL : Used by optics sampling and other image acquisition imperfections and yield blurred edges where degree of blurring is determined by factors such as1. Quality of image acquisition system2. Sampling rate3. Illumination conditions under which image is acquired
  24. 24. EXAMPLE
  25. 25. Segmentation Techniques There are 2 very simple image segmentation techniques that are based on the grey level histogram of an image  Thresholding  Clustering  But in our project we will be using clustering so we will look into the details of clustering.
  26. 26. Clustering….• Similar data points grouped together into clusters.• In this , centroid is used to represent each cluster, based on the similarity with the centroid of cluster we can classify the patterns.
  27. 27. Clustering…Most popular clustering algorithms suffer from two major drawbacks First, the number of clusters is predefined, which makes them inadequate for batch processing of huge image databases Secondly, the clusters are represented by their centroid and built using an Euclidean distance therefore inducing generally an hyperspheric cluster shape, which makes them unable to capture the real structure of the data. This is especially true in the case of color clustering where clusters are arbitrarily shaped
  28. 28. Clustering Algorithms K-means K-medoids Hierarchical Clustering There are many other algorithms used for clustering.Here we would look into 2 algorithms mainly K-meansAnd Hierarchical Clustering.
  29. 29. HIERARCHICAL CLUSTERING The concept of hierarchical clustering is to construct a dendrogram representing the nested grouping of patterns (for image, known as pixels) and the similarity levels at which groupings change. We can apply the two-dimensional data set to interpret the operation of the hierarchical clustering algorithm
  31. 31. K-means Clustering Algorithm Step1. Determine the number of clusters we want in the final classified result and set the number as N. Randomly select N patterns in the whole data bases as the N centroids of N clusters. Step2. Classify each pattern to the closest cluster centroid. The closest usually represent the pixel value is similarity, but it still can consider other features. Step3. Recompute the cluster centroids and then there have N centroids of N clusters as we do after Step1. Step4. Repeat the iteration of Step 2 to 3 until a convergence criterion is met.
  32. 32. APPLICATIONS OF IMAGE SEGEMENTATION Medical Imaging Tasks (detecting tumors,etc) Object recognitions in images of remote sensing via satellite on aerial platforms. Automated recognition systems to inspect the electronic assemblies Biometrics Automated traffic control system.
  34. 34. More Facts about Wavelets :• Wavelets are localized in frequency as well as in space having an advantageover the Fourier transform which is only localized in frequency• As a result temporal-spatial information is maintained during the wavelet transformation process which is extremely important for edge detection.Two methods based on wavelets from the multiresolution point of view havebeen introduced -• The first method was based on the two-dimensional fast wavelet transform using the Biorthogonal Mother Wavelet• The second method was based on a new wavelet named “Contourlet” which has been developed recently as an improvement of the classical wavelets.
  35. 35. Some facts about Fourier To Wavelet Analysis• The Fourier transform has been the mainstay of transform-based image processing since the late 1950s but they have a serious drawback as only frequency information remains while the local one is lost which means change in Fourier coefficients has a global effect on the image.• This means, that any modification of the Fourier coefficients has a global effect on the image. In order to involve localization on the analysis, the Short Time Fourier transform (STFT) is adapted.• In this case, the image is windowed, and thus the information has a precision relevant to the size of the window used. The drawback is that the window is the same in all frequencies.• Wavelet analysis allows the variation of the window based on the frequency information. As a result, long time intervals are used in low-frequency information and short time intervals in high-frequency information.
  36. 36. Fast Wavelet Transform using DWT :
  38. 38. Some Fuzzy Background• Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960’s.• Fuzzy logic includes 0 and 1 as extreme cases of truth (or "the state of matters" or "fact") but also includes the various states of truth in between
  39. 39. Fuzzy Vs. ProbabilityThe difference between probability and fuzzy logic is clearwhen we consider the underlying concept that each attemptsto model. Probability is concerned with the undecidability inthe outcome of clearly defined and randomly occurringevents, while fuzzy logic is concerned with the ambiguity orundecidability inherent in the description of the event itself.Fuzziness is often expressed as ambiguity rather thanimprecision or uncertainty and remains a characteristic ofperception as well as concept.
  40. 40. Membership Functions (MFs)  What is a MF?  Linguistic Variable  A Normal MF attains ‘1’ and ‘0’ for some input x1 , x2 A x1 1, A x2 0  How do we construct MFs?  Heuristic  Rank ordering  Mathematical Models  Adaptive (Neural Networks, Genetic Algorithms …)
  41. 41. Membership Function Examples Gaussian x c 2 Sigmoid 2 2 f gmf x; , c e 1 f smf x, a , c a x c 1 e Triangular Trapezoidal x a c x x a d xf x; a, b, c max min , ,0 f x; a, b, c, d max min ,1, ,0 b a c b b a d c
  42. 42. Example: Finding an ImageThreshold Membership Value 1 f smf x, a, c a x c 1 e Gray Level
  43. 43. Crisp Vs. Fuzzy  Fuzzy Sets  Crisp Sets• Membership values on [0,1] • True/False {0,1}• Law of Excluded Middle and Non- • Law of Excluded Middle and Non- Contradiction do not necessarily Contradiction hold: hold: A A A A A A A A• Fuzzy Membership Function • Crisp Membership Function• Flexibility in choosing the • Intersection (AND) , Union (OR), Intersection (T-Norm), Union (S- and Negation (NOT) are fixed Norm) and Negation operations
  44. 44. Image ProcessingBinaryGray LevelColor (RGB,HSV etc.) Can we give a crisp definition to light blue?
  45. 45. Feature Vector• Feature • Feature is any distinctive aspect, quality or characteristic  Features may be symbolic (i.e., color) or numeric (i.e., height) • The combination of d features is represented as a d-dimensional column vector called a feature vector  The d-dimensional space defined by the feature vector is called feature space  Objects are represented as points in feature space. This representation is called a scatter plot
  46. 46. Fuzzy C-means ClusteringIn fuzzy clustering, each point has a degree of belonging to clusters, as in fuzzylogic, rather than belonging completely to just one cluster. Thus, points on theedge of a cluster, may be in the cluster to a lesser degree than points in the centerof cluster.Any point x has a set of coefficients giving the degree of being in the kth clusterwk(x). With fuzzy c-means, the centroid of a cluster is the mean of allpoints, weighted by their degree of belonging to the cluster:
  47. 47. Example: Finding Edges 2 1  ijˆ mn min 1 , min  ij , W i j 1  ij g ij max gij min gij min ij spatial spatial spatial  ij ij 1 max gij max gij max ij spatial global spatial
  48. 48. Summary• Fuzzy Logic can be useful in solving Human related tasks• Evidence Theory gives tools to handle knowledge• Membership functions and Aggregation methods can beselected according to the problem at hand• Fuzzy logic can model nonlinear functions of arbitrarycomplexity.• Fuzzy logic is tolerant of imprecise data.
  49. 49. MODULE5
  50. 50. Acknowledgement•We are thankful to our mentor Mr. Soumyadip Dhar for guidingus through our project .
  51. 51. The presentation wasbrought to you by• Jishnu Mukherjee• Lahaul Seth• Rahul Kar