Image Processing and Computer Vision (CS-803 (A)) - Important Questions

Explain the concept of computer vision and its role and necessity in image processing.

Define digital image and explain image digitization via sampling and quantization.

Explain the different types of computer vision models and their applications in detail.

Define morphological image processing and describe the operations of dilation and erosion on binary images.

Describe thinning and thickening morphological operations and give a useful application example.

Explain the steps involved in image conditioning, labeling, and grouping for image recognition.

Explain image segmentation and discuss thresholding and connected component labeling techniques.

Explain boundary descriptors in image processing in detail with a neat diagram.

Explain the Hough transform with suitable derivations.

Explain spatial moments and gray-level moments in region analysis and how they are used in object recognition.

Explain the backtracking algorithm and illustrate its application with an example such as consistency labelling or shape recognition.

Explain perspective projective geometry and how inverse perspective projection enables interpretation of 3D objects from 2D images.

Explain how neural network structures are used for pattern / image shape recognition with an example.

Explain the Hough transform and its use for detecting simple objects / simple object recognition.

Write short notes on (any two): i) Principal Component Analysis (PCA) and its role in feature extraction for object recognition ii) Knowledge-based vision iii) Curve fitting and shape matching.