B.E. (Computer Engineering) Machine Vision Lab Syllabus - Mumbai University 2026
The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.
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Syllabus for Machine Vision Lab
Module 1: Handling Files, Cameras, and GUIs
- Basic I/O scripts ,Reading/writing an image file ,Converting between an image and raw bytes ,Accessing image data with numpy.array ,Reading/writing a video file ,Capturing camera frames, Displaying images in a window, Displaying camera frames in a window
Module 2: Processing Images with OpenCV 3
- Converting between different color spaces, The Fourier Transform, High pass filter, Low pass filter,
Module 3: Edge detection with Canny,
- Contour detection, Contours – bounding box, minimum area rectangle, and minimum enclosing circle ,Contours – convex contours and the Douglas-Peucker algorithm ,Line and circle detection
Module 4: Depth Estimation
- Capturing frames from a depth camera Creating a mask from a disparity map Masking a copy operation Depth estimation with a normal camera
Module 5: Object segmentation using the Watershed and GrabCut algorithms Example of foreground detection with GrabCut
- Image segmentation with the Watershed algorithm
Module 6: Detecting and Recognizing Faces
- Conceptualizing Haar cascades Getting Haar cascade data Using OpenCV to perform face detection Performing face detection on a still image
Module 7: Performing face detection on video
- Performing face recognition Generating the data for face recognition Recognizing faces Preparing the training data Loading the data and recognizing faces Performing an Eigenfaces recognition
Module 8: Retrieving Images and Searching
- Using Image Descriptors , Feature detection algorithms, Defining features Detecting features – corners Feature extraction and description using DoG and SIFT Anatomy of a keypoint
Module 9: Detecting and Recognizing Objects
- Object detection and recognition techniques HOG descriptors The scale issue The location issue Non-maximum (or non-maxima) suppression Support vector machines People detection
Module 10: Creating and training an object detector
- Bag-of-words BOW in computer vision Detecting cars in a scene
Reference &Useful Links
- 1 Learning OpenCV 3 Computer Vision with Python Second Edition, by Joe Minichino Joseph Howse Published by Packt Publishing Ltd.
- 2 http://iitk.ac.in/ee/computer-vision-lab
- 3 https://nptel.ac.in/courses/108103174
- 4 https://docs.opencv.org/3.4/d9/df8/tutorial_root.html
Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.