Image_Processing.ppt

1、DSP ClubCepstrum Presents- Talk on Image Processing,Speakers: Turaga Pavan Nishant Mohan,Purpose of Talk,Preview of the field Starting point for future IPians Knowledge of Modern Image Processing,Plan of Talk,Basics of image storage Major issues in IP Some Matlab functions Interesting Readings Whats

2、 on in IIT-G,Image as a Signal,Image 2D information of light intensity. Information Source, Focussing Agent, Sensor. Nearly always digital.,Digital Images,2D array of numbers representing intensity. Numbers stored as bits. Generally used 8 bit/pixel, 16 bit/pixel.,Gray Scales,Low intensity- Lower gr

3、ay scale value Highest possible gray level=2n-1,Color Image,Primary Colors- Red, Green, Blue. All colors can be formed by specifying relative values of R,G & B components. Each component is separately stored like gray levels (3D matrix). Hence color images can occupy 3 times the space required by b/

4、w images.,Image Storage Formats,Popular formats : BMP, JPG, GIF, TIFF etc Different formats Different levels and techniques of image compression Commercial Issues - Registered formats,Compression,Resolution of standard medical images is : 1024x1024 A complete data set may contain 100 slices Storing

5、as 8-bit/pixel Computer memory required = 800 Mb Hence compression is practically must,How to Compress,Psycho-visual redundancy Spatial redundancy Mathematical models Information theoretic redundancies Transform coding,Matlab Function,imread: reads image data in major formats imwrite: writes image f

6、rom given data imshow: shows image in matlab window,Till Now ,How to look at image as a signal Need for compression Why is compression possible Q?,Challenges in Image Processing,Image Enhancement Image Segmentation Image Understanding retrieving required information from an image.,Image Enhancement,

7、Common Problems with images Noise Blur Low Contrast,Noise Removal,Image Signal is generally base-band and noise has a broad spectrum Noise is removed by spatial filtering techniques (similar to 1D signals),Noise reduction example,Image De-blurring,Basically, a smoothed version of an image is called

8、blur De-blurring is equivalent to enhancing high frequency components of the image High Pass filters are used to get the high frequency components and added back to the original image,De-blurring,Histogram Processing,The histogram of a digital image with gray levels from 0 to L-1 is a discrete funct

9、ion h(rk)=nk, where: rk is the kth gray level nk is the # pixels in the image with that gray level n is the total number of pixels in the image k = 0, 1, 2, , L-1 Normalized histogram: p(rk)=nk/n sum of all components = 1,Image Enhancement in the Spatial Domain,Histogram Processing,Histogram is good

10、 indicator of image contrast Histogram equalization is technique that allows the image to take all gray scale values nearly equally Done by mapping value of each pixel to an appropriate value,Matlab Functions,hist: Gives histogram of a sequence histeq: Performs histogram equalisation,Till now .,Comp

11、ression and Storage Requirement of Enhancement Histogram an useful function Use of histogram for image enhancement Q ?,Image Segmentation,One of the most studied problems in Image Processing Identification of pixels belonging to the image Or marking the boundary of the object,Segmentation Methods,Th

12、resholding Decisions based on gray levels Region Growing Gray level + connectivity of object Active Contours Gradient + Connectivity + smoothness of shape,Thresholding Method,Basic Idea: Select a threshold. Pixels above threshold - region 1 Pixels below threshold - region 2,Main challenge - selectio

13、n of threshold,Thresholding Method,Advantages: 1) Simplicity of Implementation 2) Useful if histogram has well separated and distinct peaks,Disadvantages: 1) Poor noise performance. 2) Sensitive to intensity inhomogeneities,Thresholding Method,Deformable Models,Motivated by mechanical concept of ene

14、rgy minimization. An initial contour is defined that moves under influence of pre-defined forces. Forces: Internal - control shape of contour. External - control the position of contour.,Deformable Models,Internal energy: first derivative- elasticity. Second derivative- rigidity External energy: Min

15、imum at points where gradient is high. Hence above energy definitions give necessary contour characteristics.,Deformable Models,Deformable Models,Entropy based Segmentation,The technique is inspired by information theory approach We define Gray-level Image Entropy as:,- GIE measures homogeneity of t

16、he segmented region.,Entropy based Segmentation,Original Stage-1,Entropy based Segmentation,Our Hybrid Approach,Deformable models - Good noise performance - Use of local gradient information - Needs initialization Entropy based Approach - Region based method - No need for initialization - Not robust

17、 in presence of noise.,Our Hybrid Approach,Our Hybrid Approach,Our Hybrid Approach,Matlab Function,edge: Finds edges using different methods ( Cannys method is very popular),Till now .,Compression and Storage Enhancement and use of histogram Image segmentation Use of deformable models and entropy ba

18、sed histogram Q?,Medical Imaging,Aim is to observe internal structures Penetration of x-ray allows internal observation Information obtained is only average absorption by the body How to get internal 3D picture .,. Tomography,Tomography,Technique of obtaining 3d image by 2d projections from differen

19、t angles Radon showed absorption value of each point in the plane can be calculated using total absorption of rays at different angles Various slices obtained to get 3d info,Tomography,Tomography,Other Techniques,Magnetic Resonance Imaging Use of Radio Frequencies and spin of H nuclei Positron Emiss

20、ion Tomography Use of radioactive material many more,Computer VisionPattern RecognitionImage Understanding,Computer VisionPattern RecognitionImage Understanding,How do we understand images ? Object Recognition Character Identification Face Detection,Describing Image “Content”,How does a computer kno

21、w what an image means ? Descriptors/ Features : Numbers that try to capture some feature of the image Examples: Grayscale/Color Histogram, Wavelet Statistics, Cooccurence Matrix, Corellogram.,Measuring Similarity,When are two images similar ? Extremely subjective Do we recognize objects based on similarity ?,Content Based Image Retrieval,Aim: To retrieve images similar to a given image from a database of images Not Google Image Search ! What is similarity ? Can it be mathematicall