PRINCIPLES OF IMAGE PROCESSING

 Unit - I Introduction to Image Processing

1.1 Image : Pixel, Distance measures, Color fundamentals & models – RGB , HIS, YIQ, Types of an image : Vector and Raster , Types of image processing, Formation of digital image , resolution(Spatial and gray level), Megapixels and Image Quality. Image Acquisition: Cameras and sensors

1.2 Block Diagram: Fundamental Steps in Digital Image Processing, Advantages and disadvantages

1.3 Components of an Image Processing System, Difference between Image Sampling & quantization

1.4 Image file formats : JPEG , PNG, GIF, TIFF, BMP , Image Histogram

1.5 Operations on images : image addition, subtraction, logical operations, scaling, translation, rotation. Applications of Digital Image Processing

Unit - II Image Enhancement in Spatial Domain and Frequency Domain

2.1 Basic Gray Level Transformations ( Linear ,Logarithmic, Power – law) , Histogram Processing , Enhancement Using Arithmetic/Logic Operation
2.2 Spatial domain enhancement : Point operations- Log transformation, Power-law transformation, Piecewise linear transformations, Histogram equalization. Filtering operations- Image smoothing, Image sharpening
2.3 Frequency domain enhancement: 2-D Discrete Fourier Transform (DFT) ,Smoothing and Sharpening in frequency domain. Homomorphic filtering

Unit - III Image Compression and Image Segmentation

3.1 Types of redundancy: Spatial Redundancy, Spectral Redundancy, Temporal Redundancy
3.2 Fidelity criteria : Objectives, Importance. Image and Video Compression Standards – JPEG, MPEG-1,MPEG-3
3.3 Lossless compression: Run length coding, Huffman coding
3.4 Lossy compression techniques – Discrete Cosine Transform (DCT) based compression
3.5 Image Segmentation: Comparison of Point Detections, Line detection and Edge Detection, First order derivative -Prewitt and Sobel. Second order derivative – Laplacian of Gaussian (LoG), Difference of Gaussian (DoG)

Unit - IV Image Restoration

4.1 Image restoration: Definition, Concepts of restoration: constraint and unconstraint restoration, interactive restoration, Image Degradation/ Restoration Model, Difference between restoration and enhancement
4.2 Noise models: Gaussian Noise, Exponential Noise, Uniform Noise
4.3 Mean Filters : Overview of Arithmetic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Band reject Filters, Band pass Filters
4.4 Overview of Inverse Filtering and Wiener filtering, applications of Image restoration

Unit - V Image Analysis 

5.1 Feature Extraction:
Texture analysis: Definition, Importance of Texture Analysis, overview of Texture Analysis Methods
Shape analysis: Definition, Types of Shapes, concept of Shape Representation
Color analysis: Definition, Color Spaces, Color Feature Extraction : Color Histogram , Color Moments , Color Coherence Vector (CCV) , Color Correlogram , Color Transfer
5.2 Object Recognition : components of an object recognition system (Model database , Feature detector , Hypothesizer, Hypothesis verifier) , Complexity of Object Recognition : Two-dimensional, Three-dimensional. Overview of Methods for Object Recognition : Feature-Based Recognition, Template Matching, Deep Learning (CNNs), Point Cloud Matching (3D Recognition), Object Detection Algorithms (YOLO, SSD)
5.3 Feature Detection : Global Features, Local Features, Relational Features
5.4 Overview of Advanced Image Processing concepts: Deep Learning and Convolutional Neural Networks (CNNs), 3D Image Processing and Computer Vision, Computer-Aided Diagnosis (CAD) in Medical system