Overview of Image Segmentation
The significance of image segmentation extends beyond mere visual understanding, permeating into diverse domains and industries. In medical imaging, for instance, segmentation plays a pivotal role in delineating anatomical structures, identifying lesions, and assisting in disease diagnosis and treatment planning. Similar to this, segmentation helps with urban planning, environmental monitoring, and land cover classification in satellite imaging analysis. Furthermore, precise environment segmentation is essential for path planning, obstacle detection, and scene comprehension in the context of autonomous driving.
There has been an unbroken link between the development of deep learning techniques and the growth of image segmentation techniques ever since the introduction of convolutional neural networks (CNNs). With their extraordinary ability to capture complex spatial relationships and hierarchical representations found in images, these deep-learning architectures have completely changed the field of image segmentation. Researchers and professionals have been able to accomplish previously unheard-of levels of precision and effectiveness in segmentation jobs across numerous areas because of CNNs.
Segment Anything : A Foundation Model for Image Segmentation
In computer vision, segmenting an image into separate segments or regions is a crucial operation. The article “Segment Anything – A Foundation Model for Image Segmentation” provides an introduction to Attention Res-UNet which is an essential model for making separate aspects visible through images.
In this article, we explore the idea of a foundation model designed for image segmentation, which includes its structure and how to execute it in several stages such as data preparation, creation, learning as well as outcome forecasts, also talk about performance evaluation measures of the product and offers some examples for a better understanding of its use across different fields too.
Table of Content
- Overview of Image Segmentation
- What is Attention Res-UNet
- Image Segmentation Stepwise Implementation
- Step 1: Import necessary libraries
- Step 2: Download and Extract Mask Images
- Step 3: Load Mask Images and Image Dataset
- Load Mask Images
- Load Image Dataset
- Step 4: Preprocessing
- Load and Resize Images and Masks
- Display Image and Mask
- Step 5: Model Building
- Define Helper Functions
- Attention block
- Encoder block
- Decoder block
- Define Attention ResUNet Model
- Model Summary
- Step 6: Model Training
- Prepare Data for Training
- Define Callbacks and Compile Model
- Train Model
- Step 7: Predictions
- Helper Functions to Calculate Area
- Predict and Display Results
- Application of Attention Res-UNet in Image Segmentation
- Performance Evaluation and Case Studies of Attention Res-UNet
- Case Studies:
- Conclusion
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