Edge-Based Image Segmentation
Edge-based segmentation focuses on identifying the boundaries between different regions in an image. This technique detects significant changes in intensity or color, which typically indicate the presence of edges.
Sobel Operator
The Sobel operator is a gradient-based edge detection method that uses convolution with Sobel kernels to approximate the gradient of the image intensity. It highlights regions with high spatial frequency, effectively detecting edges. The Sobel operator is particularly useful for detecting horizontal and vertical edges.
Canny Edge Detector
The Canny edge detector is a multi-stage algorithm that includes noise reduction, gradient calculation, non-maximum suppression, and edge tracking by hysteresis. It is known for its ability to detect a wide range of edges while minimizing false positives. The Canny edge detector is widely used in applications requiring precise edge detection, such as object recognition and medical imaging.
Laplacian of Gaussian (LoG)
The Laplacian of Gaussian (LoG) method combines Gaussian smoothing with the Laplacian operator to detect edges. It involves smoothing the image to reduce noise and then applying the Laplacian operator to highlight regions of rapid intensity change. The LoG method is effective in detecting edges with high accuracy and is commonly used in image enhancement and feature extraction.
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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