Traditional image segmentation techniques
The traditional image segmentation techniques which formed the foundation of modern image segmentation methods using deep learning algorithms, uses thresholding, edge detection, Region-Based Segmentation, clustering algorithms and Watershed Segmentation. These techniques are more reliant on principle of image processing, mathematical operation and heuristics to separate an image into meaningful regions.
- Thresholding: This method involves selecting a threshold value and classifying image pixels between foreground and background based on intensity values
- Edge Detection: Edge detection method identify abrupt change in intensity or discontinuation in the image. It uses algorithms like Sobel, Canny or Laplacian edge detectors.
- Region-based segmentation: This method segments the image into smaller regions and iteratively merges them based on predefined attributes in colour, intensity and texture to handle noise and irregularities in the image.
- Clustering Algorithm: This method uses algorithms like K-means or Gaussian models to group object pixels in an image into clusters based on similar features like colour or texture.
- Watershed Segmentation:The watershed segmentation treats the image like a topographical map where the watershed lines are identifies based on pixel intensity and connectivity like water flowing down different valleys.
These traditional methods offer basic techniques of image segmentation with limitations, but provide foundation for more advanced methods.
Explain Image Segmentation : Techniques and Applications
Image segmentation is one of the key computer vision tasks, It separates objects, boundaries, or structures within the image for more meaningful analysis. Image segmentation plays an important role in extracting meaningful information from images, enabling computers to perceive and understand visual data in a manner that humans understand, view, and perceive. In this article let us discuss in detail image segmentation, types of image segmentation, how image segmentation is done, and its use cases in different domains.
Table of Content
- What is Image Segmentation?
- Why do we need Image Segmentation?
- Image segmentation vs. object detection vs. image classification
- Semantic Classes in Image Segmentation: Things and Stuff.
- Semantic segmentation
- Instance segmentation
- Panoptic segmentation
- Traditional image segmentation techniques
- Deep learning image segmentation models
- Applications of Image segmentation
- Conclusion:
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