Practical aspects of Computer Vision — Winter 2023

Explain K-Nearest Neighbors method for classification and segmentation.

Explain Segmentation using Clustering.

Explain how to searching the Database for Images.

Explain about Indexing images in brief.

Explain techniques for matching Geotagged Images

Write a short note on epipolar geometry in detail.

Explain image registration process in detail.

Explain Spatial domain operations for Image processing.

Explain Image to Image Mapping.

Explain Harris Corner Detector algorithm.

Explain Frequency domain operations for Image processing.

Explain 3D reconstruction in brief.

Explain SIFT - Scale-Invariant Feature Transform. In detail.

How to Creating Panoramas to describe local images.

Explain below terms in the context of transformations in 2D.

Translation (ii) Rotation

Write a short note on homography & also explain direct linear transformation algorithm.

Write a note on RANSAC.

Explain below terms in the context of transformations in 2D.

Scaling (ii) Shearing

Write a note on Optical character recognition.

Explain K-means Clustering in brief.

Explain below terms in the context of transformations in 3D.

Translation (ii) Rotation about arbitrary axis

What is the significance of Principal Component Analysis (PCA)?explain in detail.

Explain Camera Calibration.

Explain geometry of perspective projection through pinhole camera.

Explain Composition of transformations in 2D and 3D with anyone example.1