Elective 4:1:0 Course
Course Outcomes: After the end of the course student will be able to
UNIT 1
Digital Image Fundamentals: Elements of visual perception, Light and electromagnetic spectrum, image sensing and acquisition, Image sampling and quantization, Basic relationships between pixels.
Introduction to Open CV, Basics, installation, libraries
Image Enhancement in Spatial Domain: Basic gray level transformations, histogram processing, equalization, enhancement, image subtraction, averaging, smoothing and sharpening using spatial filters and their combination.
Image read write, enhancement in spatial domain using Open CV
12 hrs
UNIT 2
Image Enhancement in Frequency Domain: 2dimentional DFT, correspondence between filtering in spatial and frequency domain, smoothing and sharpening using Butterworth and Guassian Lowpass and highpass filters, Convolution, correlation, FFT and IFFT in 2d.
Image enhancement in frequency domain using Open CV 10 hrs
UNIT 3
Color image processing: Color models RGB, CMY, HSI, Color transformations, Smoothing and sharpening, Segmentation in HSI and RGB color space
Basic Morphological Algorithms: Dilation and erosion, Opening and closing, boundary extraction, region filling, extraction of connected components, thinning, thickening and pruning.
Color image segmentation and morphological operations using Open CV
12 hrs
UNIT 4
Image segmentation: Point, line and edge detection (Robert, Canny and Prewitt techniques). Character segmentation, circular object detection using Hough's transform. Segmentation using Open CV functions.
10 hrs
UNIT 5
Case studies: Character recognition, Braile recognition, Signature matching, face detection problems from recent journals 6 hrs
MATLAB Files
CIE Breakup
3 Tests 20 marks each,
+2 Miniprojects 20 marks
Related Links: