Fundamentals of Digital Image Processing By: Dr G R Sinha, IEEE Senior Member & Fellow IETE Professor, Department of Electronics and Communication Engineering CMR Technical Campus, Hyderabad ISTE National Award, TCS Award, IEI Award, Expert Engineer Award, Young Engineer Award, Young Scientist Award Recipient Email: drgrsinha@ieee.org

Outline of Presentation Motivation Digital Images and Computer Vision Digital Image Processing Resources Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017 2

Motivation/Preamble Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

My Invited Talk at NRSC Hyderabad on 7th Feb, 2017 Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Remote Sensing and Image Classification Remote Sensing helps to acquire and interpret geospatial data to develop information about features, objects, and classes on Earth's land surface, oceans, and atmosphere. It detects and measures electromagnetic energy emanating from distant objects made of various materials, so that we can identify and categorize these object by class or type, substance, and spatial distribution. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Remote Sensing and Image Classification (contd..) Data Acquision and Survey of Remotely Sensed Images Pre-processing of Images Developing Image Enhancement Methods Assessment of Enhancement Methods Image Segmentation and Feature Extraction Classification and Regional Description Study of Lands, Forests, Crop Assessment and Impact of Environmental Changes Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Application in Agriculture Crop modeling for yield & production forecast / estimation Crop growth monitoring Soil status monitoring Regular reports regarding total area under cultivation Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Application in Forestry Forest change detection; forest resource inventory Planning for a-forestation strategies Futuristic resource planning; Sustainability of environment Wild life conservation & development for recreation purpose Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image and Computer Vision Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image One picture is worth more than ten thousand words Digital Image: Two-dimensional function, f(x,y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinate (x,y) is called the intensity or gray level of the image at that point; x, y and the amplitude f(x,y) of an image are all finite, discrete quantities. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image (contd..) Digital image = a multidimensional array of numbers Each component in the image: Pixel Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image (contd..) Intensity image or monochrome image: Each pixel corresponds to light intensity normally represented in gray scale (gray level). Gray scale values Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image (contd..) RGB components Color image or RGB image: each pixel contains a vector representing red, green and blue components. RGB components Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image (contd..) Binary data Binary image or black and white image: Each pixel contains one bit : 1 represent white 0 represents black Binary data Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image (contd..) Index value Index image: Each pixel contains index number pointing to a color in a color table Color Table Index No. Red component Green Blue 1 0.1 0.5 0.3 2 1.0 0.0 3 4 5 0.2 0.8 0.9 … Index value Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Representing Digital Images The representation of an M×N numerical array as Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Computer Vision Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Computer Vision (contd..) Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image Processing Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Digital Image Processing Low Level Process Input: Image Output: Image Examples: Noise removal Mid Level Process Input: Image Output: Attributes Examples: Recognition, Segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, Autonomous navigation Important Stages in Digital Image Processing Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Morphological Processing Representation & Description Image Acquisition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Colour Image Processing Image Compression Representation & Description Problem Domain Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Morphological Processing Image Enhancement Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Image Compression Representation & Description Colour Image Processing Problem Domain Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Image Restoration Problem Domain Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Morphological Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Segmentation Morphological Processing Image Enhancement Image Restoration Image Restoration Morphological Processing Morphological Processing Image Enhancement Image Enhancement Segmentation Segmentation Image Acquisition Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Colour Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Colour Image Processing Representation & Description Problem Domain Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Pre-processing Pre-processing is used to improve quality of image by removing noise signals in images. To provide `better' input for automated image processing techniques. Categories of Image Enhancement: Spatial domain and Transform domain. Spatial domain methods are based on direct manipulation of pixels in an image; and Frequency domain processing techniques are based on modifying the Fourier transform of an image. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017 28

Image Enhancement         Spatial domain: Directly operate over the pixels, for example contrast enhancement, Gray-level clustering histogram equalization (GLC-CE)            g(x, y) = T[ f(x, y)] where f(x, y) is the input image, g(x, y), the processed image, and T, an operator on f(x, y) defined over some neighborhood of (x, y). Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Histogram Equalization (Left) Contrast Enhancement (Right) Examples        Histogram Equalization (Left) Contrast Enhancement (Right) Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Spatial Domain Methods Spatial Domain Techniques Point Processing Image Subtraction Image Averaging Spatial Filtering Gabor Filtering Weiner Filtering Histogram Processing Gray Scale Modification Log Transformation Power Law Transformation Piecewise Transformation Contrast Stretching Gray Level Slicing Bit Plane Slicing Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Contrast Stretching & Linear Stretching Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Contrast Enhancement Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Low-pass Filtering Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Inverse Fourier Transform Frequency Domain Methods  Low pass filtering or smoothing domain filters, High pass filtering or sharpening domain filters, Homomorphic filtering, and Color image enhancement. Pre-processing Fourier Transform Post-Processing Filter Function H(u,v) Inverse Fourier Transform f(x,y) Input Image F(u,v) H(u,v)F(u,v) g(x,y) Enhanced Image Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Noise Statistics Characteristics of noise: Noise type (additive, signal-dependent, multiplicative, impulse etc.) PDF, variance, relative variance, probability of impulse noise, etc. There are no universal methods for coping well with any type and level of noise. Noise analysis and removal helps in change and target detection, multi-temporal image analysis, underwater imaging, ocean imaging, multimodal analysis, quality assessment, and image restoration. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Statistical parameters for Evaluation PSNR (Peak-signal-to-noise-ratio) CNR (Contrast-to-noise-ratio) Invariant moments Mean and Variance values Histogram SSIM Invariant Moments Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Segmentation Main types: Pixel based, Region based and Contour based Segmentation methods Pixel based: Thresholding, Histogram based adaptive thresholding, k-means clustering etc. Region based: Region growing method Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017 38

Post Processing: Performance Measures Size, shape, area, dimension, Shadow, tone, Color, Texture and Pattern Shape parameters: Perimeter, distance, aspect ratio, major and minor axis PSNR and CNR Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017 39

Spatial resolution High vs. Low? Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Spatial resolution affects 2-bit Image (4 gray levels) 8-bit Image (256 gray levels) Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Resources Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Companies in India Sarnoff Corporation Kritikal Solutions National Instruments GE Laboratories Ittiam, Bangalore Interra Systems, Noida Yahoo India (Multimedia Searching) nVidia Graphics, Pune (have high requirements) Microsoft research DRDO labs ISRO labs … Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Books -------------------------------------- Digital Image Processing Rafael C. Gonzalez and Richards E. Woods, Addison Wesley Rafael Gonzalez and Paul Wintz Fundamentals of Digital Image Processing Anil K. Jain Prentice Hall, 1989. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Concluding Remarks with Roadmap The applications of Image Processing strengthen the scope for potential research in the field of remote sensing, Astronomical Image Processing, underwater imaging, soil & plant health monitoring, biometrics, Medical Image Processing etc. Image de-noising method (optimal implementation using neuro-fuzzy method) and a good segmentation method with optimal set of parameters would help in achieving good data classification and interpretation results. Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017

Sincere Thanks with Inspiring Equation c = 2 (Doubly enthused) E =4 E= mc2 E= Excellence, m = Motivation, C =Commitment c = 0.5 (Half hearted) E = ¼ c = 2 (Doubly enthused) E =4 Fundamentals of Image Processing Dr G R Sinha, FDP on DIP and Computer Vision-2017 24th June 2017