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Free download of Sikkim Manipal University, SMU previous year, last year, sample question paper for  : [Digital Image Processing] MC0086. Find question and answer for Part-A 2 marks, Part-B 4 Marks and Part-C 8 marks. Free download of question paper of previous year test.   Digital Image Processing , MC0086 question paper includes multiple choice options and answer also.

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   SMU >> MCA >> MC0086

   Digital Image Processing

This is the collection of Sikkim Manipal University (SMU) question paper for MCA - Digital Image Processing. It will help you to prepare your examination. All questions are classified as per question type like PART - A of 2 marks, PART - B of 4 marks and PART - C of 8 marks same as actual examination. SMU question paper set which includes year 2018, 2017, 2016 SMU question papers of Digital Image Processing are updated regularly and it is absolutely free to use. SMU Question paper includes multiple choice question and answer as same as real and original examination. It will help you to study and prepare for your final SMU examination.

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  1. BCA
  2. MCA
  3. BSc IT
  4. MSc IT
  5. MBA

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Course Name
        MCA (Master of Computer Application)

Subject Code      
MC0086 (Digital Image Processing)

Get Questions        PART - A    PART - B    PART - C

Digital Image Processing Syllabus.

Part 1 Digital Image Processing – An Introduction
Introduction; The Origins of Digital Image Processing; Examples of Fields that use Digital Image Processing: Gamma-ray Imaging, X-ray Imaging, Imaging in the Ultraviolet Band, Imaging in the Visible and Infrared Bands, Imaging in the Microwave Band, Imaging in the Radio Band; Fundamental Steps in Digital Image Processing; Components of an Image Processing System.

Part 2 - Digital Image Fundamentals
Introduction; Elements of Visual Perception: Structure of the Human Eye, Image formation in the Eye, Brightness Adaptation and Discrimination; Light and the Electromagnetic Spectrum; Image Sensing and Acquisition; Image Acquisition using a Single Sensor, Image Acquisition using Sensor Strips, Image Acquisition using Sensor Arrays; A Simple Image formation Model.

Part 3 - Image Sampling and Quantization
Introduction; Basic concepts in Sampling and Quantization: Representation of Digital Images, Spatial and Gray-Level Resolution, Aliasing and Moiré Patterns, Zooming and Shrinking Digital Images; Some Basic Relationships between Pixels: Neighbors of a Pixel; Adjacency, Connectivity, Regions and Boundaries; Distance measures; Linear and Nonlinear Operations.

Part 4 - Image Enhancement
Introduction ; Contrast Manipulation : Amplitude Scaling; Histogram Modification; Noise Cleaning: Linear Noise Cleaning, Non Linear Noise Cleaning; Edge Crispening: Linear Technique, Statistical Differencing; Color Image Enhancement: Natural Color Image enhancement, Pseudo Color, False Color; Multispectral Image Enhancement.

Part 5 - Image Restoration
Introduction; General Image Restoration Models; Optical system Models; Photographic Process Models: Monochromatic Photography, Color Photography; Continuous Image Spatial Filtering Restoration: Inverse Filter, Wiener Filter; Blind Image Restoration: Direct Measurement Methods, Indirect Estimation Methods.

Part 6 - Morphological Image Processing
Introduction; Basic operations: Dilation, Erosion, Properties of Dilation and Erosion, Close and Open; Hit-or-miss transformation: Additive operators, Subtractive operators; Morphological algorithm operations on binary images: Shrinking, Thinning, Skeletonizing, Thickening; Morphological algorithm operations on gray scale images: Gray Scale Image Dilation and Erosion, Gray Scale Image Close and Open Operators.

Part 7 - Image Feature Extraction
Introduction; Image Feature Evaluation; Amplitude Features; Transform Coefficient Features; Texture Definition; Visual Texture Discrimination; Texture Features: Fourier Spectra Methods, Edge Detection Methods, Autocorrelation Methods.

Part 8 - Edge Detection
Introduction; Edge, Line and Spot models; First-Order Derivative Edge Detection: Orthogonal Gradient Generation; Second-Order Derivative Edge Detection; Edge-Fitting Edge Detection; Luminance Edge Detector Performance: Edge Detection Probability; Color Edge Detection; Line and Spot Detection.

Part 9 - Image Segmentation 1
Introduction; Detection of Discontinuities: Point Detection, Line Detection, Edge Detection; Edge Linking and Boundary Detection: Local Processing, Global processing via the HOUGH transform, Global processing via Graph-Theoretic Techniques.

Part 10 - Image Segmentation
Introduction; Thresholding: Foundation, The Role of Illumination, Global Thresholding, Adaptive Thresholding; Region-Based Segmentation: Basic Formulation, Region Growing, Region Splitting and Merging.

Part 11 - Shape Analysis
Introduction; Topological Attributes; Distance, Perimeter and Area Measurements: Distance Measures, Perimeter and Area Measures; Spatial Moments: Discrete Image Spatial Moments; Shape Orientation Descriptors; Fourier Descriptors; Thinning and Skeletonizing.

Part 12 - Wavelets and Multi Resolution Processing
Introduction; Image Pyramids; Series Expansion: Windowed Fourier transform; Scaling functions - Continuous Wavelet Transform; Haar transform; Subband coding; Wavelet functions; Discrete Wavelet transform in one dimension; Wavelet transforms in two dimensions: Standard decomposition, Non-Standard decomposition; Multiresolution processing and wavelets.

  Get Questions        PART - A    PART - B    PART - C

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