Practical Aspects of Computer Vision 3171113 Syllabus Download

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Total Weightage

Introduction to machine vision:

Introduction to Machine vision, Fundamentals of Image processing: The human eye-brain
system as a model for computer vision, Image formation, Image models, Basic image
processing:- Spatial domain operations and Frequency domain operations, Image
transforms

Local Image Descriptors and Mappings 

Harris corner detector ,SIFT – Scale-Invariant Feature Transform, Matching Geotagged  Images, Image to Image Mappings, Warping of Images, Creating Panoramas

Camera Geometry and Multiple View Geometry:

transformations in 2D and 3D with examples; concept of homogeneous coordinates in 2D  and 3D, Concept of pinhole camera, geometry of perspective projection through pinhole  camera, Camera Calibration, Epipolar Geometry , Computing with Cameras and 3D  Structure,. Multiple View Reconstruction, Stereo Images.

Machine Learning in computer vision:  

Clustering and Searching Images: K-means Clustering, Hierarchical Clustering, Spectral  Searching Images, content-based Image Retrieval, Visual Words, Indexing Images,  Searching the Database for Images, Ranking Results using Geometry Building Demos  and Web Applications.

Robust methods for classification and segmentation: 

Eigen decomposition and PCA, K-Nearest Neighbors, Bayes, Support Vector Machines,  Optical Character Recognition , Image Segmentation : Graph Cuts, Segmentation using  Clustering