Artificial Neural Network-Based Segmentation
Artificial neural network-based segmentation leverages the power of deep learning to achieve high-precision segmentation results. This approach involves training neural networks on labeled datasets to learn the complex patterns and features within the images.
Convolutional Neural Networks (CNNs) are widely used for image segmentation tasks due to their ability to learn spatial hierarchies of features. CNN-based models, such as U-Net and SegNet, have shown remarkable performance in medical imaging, autonomous driving, and satellite image analysis.
- U-Net : U-Net is a fully convolutional network designed for biomedical image segmentation. It consists of a contracting path to capture context and a symmetric expanding path to enable precise localization. U-Net’s architecture allows for the efficient segmentation of high-resolution images with limited training data.
- SegNet : SegNet is another popular CNN-based model for semantic segmentation. It employs an encoder-decoder architecture, where the encoder captures spatial features, and the decoder performs upsampling to generate pixel-wise segmentations. SegNet is effective in applications requiring detailed segmentation, such as urban scene understanding and object detection.
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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