Instance segmentation
Instance segmentation in image segmentation of computer vision task is a more sophisticated feature which involves identifying and delineating each individual object within an image. So instance segmentation goes beyond just identifying objects in an image, but also delineate the exact boundaries of each individual instance of that object.
So, the key focus of instance segmentation is to differentiate between separate objects of the same class. for example, if there are many cats in a image, instance segmentation would identify and outline each specific cat. The segmentation map is created for each individual pixel and separate labels are assigned to specific object instances by creating different coloured labels which will represent different ‘cat’ in the group of cats in an image.
Instance segmentation is useful in autonomous vehicles to identify individual objects like pedestrians, other vehicles and any objects along the navigation route. In medical imaging, analysing scan images for detection of specific abnormalities are useful for early detection of cancer and other organ conditions.
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: