Threshold-Based Segmentation
Threshold-based segmentation is one of the simplest and most straightforward image segmentation techniques. It involves converting a grayscale image into a binary image by applying a threshold value. Pixels with intensity values above the threshold are classified into one category, while those below the threshold are classified into another.
Global Thresholding
Global thresholding applies a single threshold value to the entire image. This technique is effective when there is a clear contrast between the objects of interest and the background. For instance, in a document image, global thresholding can effectively separate the text from the white background.
Adaptive Thresholding
Adaptive thresholding, also known as local thresholding, calculates different threshold values for different regions of the image. This approach is useful for images with varying lighting conditions, where a single global threshold would not be effective. Adaptive thresholding ensures better segmentation by considering the local intensity distribution of the pixels.
Otsu’s Method
Otsu’s method is an automatic thresholding technique that determines the optimal threshold value by minimizing the intra-class variance of the pixel intensity distribution. It is widely used in scenarios where the histogram of the image intensity is bimodal, making it a popular choice for medical imaging and document analysis.
Image Segmentation Approaches and Techniques in Computer Vision
Image segmentation partitions an image into multiple segments that simplify the image’s representation, making it more meaningful and easier to work with. This technique is essential for various applications, from medical imaging and autonomous driving to object detection and image editing. Effective segmentation enables precise identification and localization of objects within an image, facilitating tasks like feature extraction, pattern recognition, and scene understanding.
The article aims to explore the approaches and techniques used for image segmentation in the computer vision domain.
Table of Content
- Image Segmentation Approaches
- Similarity Approach
- Discontinuity Approach
- Five Common Image Segmentation Techniques
- 1. Threshold-Based Segmentation
- Global Thresholding
- Adaptive Thresholding
- Otsu’s Method
- 2. Edge-Based Image Segmentation
- Sobel Operator
- Canny Edge Detector
- Laplacian of Gaussian (LoG)
- 3. Region-Based Image Segmentation
- Region Growing
- Region Splitting and Merging
- Watershed Segmentation
- 4. Clustering-Based Image Segmentation
- K-means Clustering
- Mean Shift Clustering
- Fuzzy C-means Clustering
- 5. Artificial Neural Network-Based Segmentation
- Conclusion
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