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Premium member Presentation Transcript Technical Seminar onIMAGE PROCESSING AND ITS APPLICATION : 1 Technical Seminar onIMAGE PROCESSING AND ITS APPLICATION Presented by Rakhi Ghosh CS200157261 Under the Guidance of Mr. Anisur Rahman Slide 2: 2 Digital image processing fundamentals Digital image processing methods stems from two principals application areas: Improvement of pictorial information. Processing of scene data. Slide 3: 3 IMAGES : Image is replica of object. An image defined in the "real world" is considered to be a function of two real variables x and y. TYPES OF IMAGES : Gray-tone image: Line copy images: Half-tone images Slide 4: 4 EXAMPLE: A digital image a[m,n] described in a 2D discrete space is derived from an analog image a(x,y). The 2D continuous image a(x,y) is divided into N rows and M columns. STEPS IN IMAGE PROCESSING : 5 STEPS IN IMAGE PROCESSING Image acquisition Preprocessing Segmentation Representation and Description Recognition Interpretation Knowledge base Slide 6: 6 IMAGING GEOMETRY TRANSLATION SCALING ROTATION PERSPECTIVE TRANSFORMATION IMAGE TRANSFORMATION : 7 IMAGE TRANSFORMATION FOURIER TRANSFORM The Fourier transform produces representation of a signal, as a weighted sum of complex exponentials. Because of Euler's formula: , The defining formulas for the forward Fourier and the inverse Fourier transforms are as follows. Slide 8: 8 The forward transform goes from the spatial domain, either continuous or discrete to the frequency domain which is always continuous . The inverse Fourier transform goes from the frequency domain back to the spatial domain. Slide 9: 9 The specific formulas for transforming back and forth between the spatial domain and the frequency domain are given below. In 2D continuous space and in discrete space WALSH TRANSFORM : 10 WALSH TRANSFORM The discrete Walsh transform of a function f(x), where N=2N denoted by W(u) is obtained by substituting the kernel as The inverse transform is the relation Slide 11: 11 The 1-D, forward Hadamard kernel is the relation HADAMARD TRANSFORM 1-D Hadamard transform Slide 12: 12 An inverse kernel that, except for the 1/N term, is equal to the forward Hedamard kernel The inverse Hadamard transform: For x=0,1,2…N-1. IMAGE ENHANCEMENT : 13 IMAGE ENHANCEMENT The process of image acquisition frequently leads (inadvertently) to image degradation. The principle objective of enhancement techniques is to process an image so, that the result is more suitable than the original image for specification application. Image enhancement techniques are used to increase the signal-to-noise ratio. Make certain features easier to see by modifying the colors or intensities of an image. Slide 14: 14 The approach of enhancement techniques falls into two categories: Spatial domain method : In this category are based on direct manipulation of pixels in an image, that is the gray values of the peels are directly manipulated to obtain the enhanced image. Frequency domain method: Processing techniques are based on modifying the Fourier transform of an image, that the image f(x,y) is Fourier transformed to F(u,v) before any modification is done : 15 BASIC IMAGE ENHANCEMENT TECHNIQUES: SPATIAL DOMAIN METHODS: The term spatial domain refers to the aggregate of pixels composing an image, and spatial domain methods are procedure that operates directly in this pixel Image processing function in the spatial domain may be expressed as g(x,y) =T[f(x,y)] Where f(x,y) is the input image and g(x,y) is the processed image, and T is an operator on f, defined over some neighborhood about(x,y) Slide 16: 16 FREQUENCY DOMAIN METHODS: The foundation of frequency domain technique is the convolution theorem .Let g(,y) be an image formed by the convolution of an image f(x,y)and a linear position, position invariant operator h(x,y) that is, g(x,y) = h(x,y) * f(x,y) Then from the convolution theorem, the following frequency domain relation holds: G( u,v) = H (u , v)F(u , v) Where G,F &H are the Fourier transforms of g,h &h respectively. Slide 17: 17 ENHANCEMENT BY POINT PROCESS: Contrast stretching. Gray-level slicing. Histogram processing. Histogram specification. Image subtracting. Image averaging. : 18 Spatial filtering: Mean Filter Median filter Smoothing filter Filtering in frequency domain: Low pass filter Ideal Low pass Filter Butter worth low pass filter Homomorphic filtering ENHANCEMENT BY DIFFERENT FILTERING Slide 19: 19 APPLICATION OF FILTERS Application of median filter Application of Smoothing filter Slide 20: 20 Morphology tools such as dilation and erosion can be used in conjunction with edge detection to detect and outline a prostate cancer cell. The effect of homorphic filtring on the noisy filter Slide 21: 21 IMAGE COMPRESSION MODELS: Source Encoder Channel Encoder Channel Source Decoder Channel Decoder F(x,y) F’(x,y) SOURCE ENCODER: The source encoder is responsible for reducing or eliminating any coding, interpixel, or psycho visual redundancies in the input image. SOURCE DECODER: The source decoder contains only two components the symbol decoder and an inverse mapped. Slide 22: 22 ERROR FREE COMPRESSION: VARIABLE LENGTH CODING Huffman Coding. Arithmetic Coding. Bit plane coding. LOSSY COMPRESSION: Slide 23: 23 IMAGE SEGMENTATION Thresholding. Fixed threshold. Istogram-derived thresholds. Edge finding. Slide 24: 24 IMAGE RESTORATION The ultimate goal of restoration techniques is to improve an image. The restoration techniques are oriented toward modeling the degradation applying the inverse process in order to recover the original image. Slide 25: 25 CONCLUSION The various aspects of Image processing and their practical usage and the steps involved in their processing are studied. This has given a good and practical idea of using various Transforms techniques on images . Slide 26: 26 THANK YOU You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.