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AKTU Image Analytics (BDS061) Syllabus PDF – B.Tech 3rd Year
Image Analytics BDS061
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Official AKTU syllabus of Image Analytics (BDS061) 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.
Syllabus — Image Analytics
Download PDFBDS061 · 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 Minimum- Distance 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 | Image Segmentation Introduction - Point, Line, and Edge Detection – 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 – Image Segmentation: Active Contours: Snakes and Level Sets. Practical Component: Use Python/ MATLAB 1. Implement Optimum Global Thresholding using Otsu’s Method. 2. Implement Image segmentation by Region Growing, Splitting and Merging 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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