Deep learning image segmentation models
Deep learning image segmentation models are a powerful technique which leverages the neural network architecture to automatically divide an image into different segments and extract features from images for accurate analysis and segmentation tasks.
Below are some of the popular deep learning models used for image segmentation:
- U-Net: This model uses U-Shaped network to efficiently segment medical images. This model is very efficient in working with small amount of data and provide precise segmentation.
- Fully Convolutional Network (FCN):This model has the ability to process image of any size and output spatial maps. This is achieved by replacing fully connected layers in a conventional CNN with convolutional layers. This helps in segmenting an entire image pixel by pixel.
- SegNet: This model includes a encoder-decoder network, used for tasks like scene understanding and object recognition. The encoder here captures the context in the image and the decoder performs the precise localization and segmentation objects by using the context.
- DeepLab: The key feature of DeepLab is the use of atrous convolutions used to capture multi-scale context with multiple parallel filters.
- Mask R-CNN: This model extents the Faster R-CNN object detection framework, by adding a branch for predicting segmentation masks alongside bounding box regression.
- Vision Transformer (ViT): A new model that applies transformers to image segmentation. The image is divided into patches and processes them sequentially to understand the global context of the image.
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: