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Computer Vision




OpenCV, NumPy, SciPy, Scikit-learn, Matplotlib

Skills to be Learned:

Understanding of image segmentation, implementation of clustering algorithms

Image Segmentation By Clustering

Dive into the world of computer vision with our project-based course focusing on image segmentation. Learn to partition images using clustering techniques, extract features, and refine segmentation results. Ideal for those with basic programming knowledge and an interest in image analysis.

This project-based course delves into the fascinating field of computer vision and image analysis. Image segmentation is a critical task in computer vision, aiming to partition an image into distinct regions or objects based on their visual characteristics. Throughout this course, participants will explore various clustering-based techniques and tools to achieve accurate image segmentation. By learning to extract meaningful features, apply clustering algorithms, and perform post-processing, students will acquire valuable skills for image analysis applications.

Learning Outcomes:

By the end of this course, participants will:

- Gain a deep understanding of image segmentation concepts and its real-world applications.

- Develop proficiency in defining feature spaces for image analysis.

- Master the selection and implementation of appropriate clustering algorithms for segmentation.

- Learn techniques for determining the optimal number of clusters in images.

- Acquire skills in pixel assignment to clusters and post-processing for refined results.

- Evaluate and fine-tune segmentation models for improved accuracy.


- Basic knowledge of programming concepts.

- Familiarity with the Python programming language is advantageous but not mandatory.

- A keen interest in computer vision and image analysis.

Libraries and Programming Language Used:

- Programming Language: Python

- Libraries: OpenCV, NumPy, SciPy, Scikit-learn, Matplotlib

Course Syllabus:


   - Understanding the importance of image segmentation

   - Real-world applications and use cases

Defining the Feature Space

   - Extracting relevant features from images

   - Feature representation and selection

Choosing a Clustering Algorithm

   - Overview of clustering techniques (K-means, DBSCAN, etc.)

   - Selecting the most suitable clustering method for image data

Determine the Number of Clusters

   - Methods for estimating the optimal number of clusters

   - Techniques to avoid over-segmentation and under-segmentation

Assigning Pixels to Clusters

   - Implementing clustering algorithms on image data

   - Assigning pixels to clusters based on feature similarity


   - Refining segmentation results through techniques like merging and splitting

   - Noise reduction and region enhancement

Evaluation of Model

   - Metrics for assessing segmentation quality

   - Quantitative and qualitative evaluation methods

Prerequisites include a basic understanding of programming and familiarity with Python. The course uses Python and libraries like OpenCV, NumPy, and Scikit-learn, offering a mix of theoretical knowledge and practical skills.

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