Course Name: ML for Image Analytics
Code (Credits): CUML2011 (0-2-2)
Still no participant
Still no reviews
Course Name: ML for Image Analytics
Code (Credits): CUML2011 (0-2-2)
Course Objectives:
Learning Outcomes
Image Pre-processing
Image Feature Extraction
Creation of Feature Matrix by combining Extracted Features
Classification algorithms
Complete Lab Manual: ImageAnalyticsLabManual
Each session is of 3hrs
Session 01: Loading an Image, Color Models, Accessing Pixel Intensity values, Color Space conversions
Session 02:Splitting and Merging Channels, Histograms
Session 03: Histogram Equalization, CLAHE
Session 04: Image Resizing and Rotation, Image Arithmetic, Image Bitwise Operations
Session 05: Morphological Operations
Session 06: Image Smoothening, Sharpening and Edge detection using Sobel, Robert Cross-Gradient
Session 07: Laplacian filters, Canny Edge detection
Session 08: Frequency domain: converting to frequency domain, Low pass, High pass, band pass and High boost filtering
Session 09: Thresholding
Session 10: Image Segmentation: Edge Based, Region Based and Cluster Based
Session 11: Matrix flattening, Horizontal stacking, Vertical stacking, padding, Splitting the feature matrix and labelling
Session 12: Feature extraction based on Color, shape and size
Session 13: Feature extraction using predefined functions: SIFT, SURF, STAR, ORB
Session 14: Support vector machine (SVM)- Linear and Kernel
Session 15: Gradient Boosting (GB)
Session 16: Multi-layer Perceptron (MLP) - CNN and Deep Learning