BCDS061 Image Analytics Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCDS061 Image Analytics (also written as KCDS061) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University. The syllabus is divided into 5 units: Fundamentals and Practical Component, Morphological Image Processing, Colour Image Processing and Thresholding and Practical Component, Practical Component. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCDS061 Subject Details
| Subject Code | BCDS061 / KCDS061 |
|---|---|
| Subject Name | Image Analytics |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-19 |
BCDS061 Image Analytics Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: Fundamentals · Practical Component
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.
Unit 2: Morphological Image Processing
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.
Unit 3: Colour Image Processing · Thresholding · Practical Component
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
Unit 4: Practical Component
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)
Unit 5
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.
BCDS061 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 K1, K2
- CO 2 for intensity transformations functions. Implement Color image Smoothing and K2, K3
- Sharpening.
- Illustrate Morphological operation and Apply Some Basic Morphological Algorithms.
- CO 3 Apply image segmentation techniques such as Optimum Global Thresholding using Otsu’s K3, K4
- CO 4 Method, Active Contours: Snakes and Level Sets for various real-time applications. K3, K4
- Analysis various Feature Extraction methods and Implement for various real-time applications.
- CO 5 Apply and Analysis various Image Pattern Classification methods such as MinimumDistance K3, K4
- Classification, Optimum (Bayes) Statistical Classification, and Deep Convolutional Neural 3-0-0
- Network.
BCDS061 Reference 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.
Frequently Asked Questions about BCDS061
What is the syllabus of BCDS061 Image Analytics?
The AKTU BCDS061 Image Analytics syllabus is divided into 5 units: Fundamentals and Practical Component; Morphological Image Processing; Colour Image Processing and Thresholding and Practical Component; Practical Component. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCDS061?
BCDS061 Image Analytics has 5 units in the AKTU syllabus.
Is BCDS061 the same subject as KCDS061?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCDS061 and KCDS061 depending on the scheme year. The syllabus content is the same.
Where can I download BCDS061 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCDS061?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-19.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-19.