Segmentation in Digital Image Processing

Segmentation_in_Image_Processing Intro

1. Segmentation in Image Processing An Overview of Techniques and Applications

2. Introduction • Image segmentation is the process of partitioning an image into meaningful regions. • Helps in simplifying image analysis. • Important for object recognition and scene understanding.

3. Segmentation Techniques • Thresholding (Global & Adaptive) • Edge-based Segmentation • Region-based Segmentation • Clustering-based (K-Means, Mean-Shift) • Watershed Segmentation • Deep Learning-based (FCN, U-Net, Mask R-CNN)

4. Applications • Medical Imaging (tumor detection, organ segmentation) • Object Detection & Recognition • Autonomous Vehicles (lane detection, road segmentation) • Remote Sensing (land cover classification, vegetation analysis)

5. Advantages & Limitations Advantages: • Simplifies image analysis • Enhances feature extraction • Improves accuracy in recognition tasks Limitations: • Sensitive to noise • Computationally expensive • Complex for natural scenes

6. Conclusion & Future Trends • Segmentation is a fundamental step in image processing. • Used in various domains from medical to autonomous systems. • Future trends: Deep learning, hybrid models, real-time segmentation.