Machine Vision
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Follow on Instagram for UpdatesMachine Vision Syllabus — Unit-wise Content
The official GTU curriculum for Machine Vision (subject code 2182006) as prescribed for Mechatronics · Semester 8 divides the course into 7 units, covering 60 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2182006)Unit 1 Introduction
- Comparison with human visual system and perception level
- digital image presentation
- Definitions of digital image
- Examples of the fields that use digital image processing
- fundamental steps in image processing
- components / elements of digital image processing systems
- image acquisition
- communication and display
Unit 2 Digital Image Fundamentals
- Elements of visual perception – brightness adaption and discrimination
- light and electromagnetic spectrum
- image sensing and acquisition
- sampling and quantization
- some basic relationships between pixels
- distance measures
- different types of image sensors
- different types of file formats. 5 12.5
Unit 3 Image Enhancement in Spatial Domain
- point processing and mask processing
- Basic gray level transformations
- histogram processing
- histogram equalization
- histogram matching
- local enhancement
- histogram statistics
- image subtraction
- image averaging
- basics of spatial filtering – smoothing
- sharpening filters and order statistics filters
Unit 4 Image Enhancement in Frequency domain
- introduction to Fourier Transform and frequency domain
- The discrete Fourier Transforms – properties of 2-D Fourier Transform
- smoothing frequency domain filters – ideal
- butter worth
- Gaussian low pass filters with additional examples of low pass filters
- sharpening frequency domain filters – ideal
- Gaussian and Laplacian filters
- unsharp masking
- high boost filtering
- homomorphic filtering
- convolution and correlation
- additional properties of 2-D Fourier transfer
- Periodicity and need for padding
Unit 5 Image Restoration
- model for image degradation/restoration
- noise models – probability density functions of noise
- periodic noise and estimation of noise parameters
- periodic noise reduction by frequency domain filtering
- Arithmetic mean filters
- geometric mean filters
- adaptive filters
- Band pass and band reject filters. 5 12.5
Unit 6 Image compression
- fundamentals of image compression and types of redundancy
- error free and lossy compression
- variable length coding – Huffman coding
- arithmetic coding
- LZW coding
- run length coding. 5 12.5
Unit 7 Morphological Image Processing
- Basic concept
- Dilation and Erosion process for binary and gray image with applications
- Opening & Closing for binary and gray image with applications
- Hit-or-Miss Transformation
- Basic Morphological Algorithms
- textural segmentation. 5 12.5
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