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AKTU Image Analytics (BCDS061) Syllabus PDF – B.Tech 3rd Year
Image Analytics BCDS061
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Official AKTU syllabus of Image Analytics (BCDS061) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
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Syllabus — Image Analytics
Download PDFBCDS061 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able to:
| CO 1 | Infer the basics and fundamentals of digital image processing and Apply the various techniques for intensity transformations functions. Implement Color image Smoothing and Sharpening. | K , K 1 2 |
| CO 2 | Illustrate Morphological operation and Apply Some Basic Morphological Algorithms. | K , K 2 3 |
| CO 3 | Apply image segmentation techniques such as Optimum Global Thresholding using Otsu’s Method, Active Contours: Snakes and Level Sets for various real-time applications. | K , K 3 4 |
| CO 4 | Analysis various Feature Extraction methods and Implement for various real-time applications. | K , K 3 4 |
| CO 5 | Apply and Analysis various Image Pattern Classification methods such as MinimumDistance Classification, Optimum (Bayes) Statistical Classification, and Deep Convolutional Neural Network. | K , K 3 4 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Fundamentals: Introduction – Fundamental steps in Image Processing Systems – Image Acquisition – Sampling and Quantization – Pixel Relationships – Mathematical Tools Used in Digital Image Processing. Some Basic Intensity Transformation Functions: Image Negatives, Log Transformations, Power-Law Transformations – Histogram Processing. Color Fundamentals – Fundamentals of Spatial Filtering – Smoothing Spatial Filters – Sharpening Spatial Filters. Practical Component: Use Python/ MATLAB 1. Apply various intensity transformations functions. 2. Computing and plotting image histograms and use standard image processing toolbox Spatial filters. 3. Implement color image Smoothing and Sharpening. | 08 |
| II | Morphological Image Processing: Morphological Image Processing: Fundamentals – Erosion and Dilation – Opening and Closing – Hit or Miss Transform – Some Basic Morphological Algorithms – Morphological Reconstruction – Grayscale Morphology Practical Component: Use Python/ MATLAB 1. Implement Morphological operations. 2. Implement Morphological Reconstruction. 3. Implement Grayscale Morphology. | 08 |
| III | Colour Image Processing: Colour fundamentals, colour models, pseudo colour image processing, basics of full colour image processing, colour transformation, smoothing and sharpening. Image Segmentation based on colour, Active Contours: Snakes and Level Sets, noise in colour images, colour image compressions. Thresholding: Foundation, Basic Global thresholding, Optimum Global Thresholding using Otsu’s Method, Multiple Thresholds, Variable Thresholding –Segmentation by Region Growing and by Region Splitting and Merging. Practical Component: Use Python/ MATLAB 1. Implement Optimum Global Thresholding using Otsu’s Method. 2. Implement Image smoothing and sharpening. 3. Implement Image Segmentation by Active Contours using anyone method Snakes and Level Sets | 08 |
| IV | Feature Extraction Background – Representation – Boundary Preprocessing – Boundary Feature Descriptors: Some Basic Boundary Descriptors, Shape Numbers, Fourier Descriptors, Statistical Moments – Regional Feature Descriptors: Some Basic Descriptors, Topological and Texture Descriptors, Moment Invariants – Principal Components as Feature Descriptors – Whole-image Features Object – Scale- Invariant Feature Transform (SIFT). Practical Component: Use Python/ MATLAB 1. Implement Boundary Feature Descriptors 2. Implement Topological and Texture Descriptors 3. Implement Scale-Invariant Feature Transform (SIFT) | 08 |
| V | Image Pattern Classification Background –Patterns and Pattern Classes – Pattern Classification by Prototype Matching: Minimum-Distance Classifier, Using Correlation for 2-D prototype matching, Matching SIFT Features, Matching Structural Prototypes – Optimum (Bayes) Statistical Classifiers – Neural Networks and Deep Learning: Background – The Perceptron – Multilayer Feedforward Neural Networks – Deep Convolutional Neural Networks Practical Component: Use Python/ MATLAB 1. Implement Minimum-Distance Classification Algorithm. 2. Implement Optimum (Bayes) Statistical Classification Algorithm. 3. Implement Deep Convolutional Neural Network. | 08 |
Text Books
- Rafael C Gonzalez, Richard E Woods, “Digital Image Processing”, 4th Edition, Pearson, 2018.
- Kenneth R. Castleman, Digital Image Processing Pearson, 2006.
- Anil K.Jain, “Fundamentals of Digital Image Processing”, Person Education, 2003.
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