Panoptic segmentation
Panoptic segmentation goes a step further in image segmentation of computer vision tasks, by combining the features and processes of semantic and instance segmentation techniques. So the panoptic segmentation algorithm creates a comprehensive image analysis by simultaneously classifying every pixel and identifying distinct object instances of the same class.
So, from an image with multiple cars and pedestrians in an traffic signal, the panoptic segmentation would label all ‘pedestrians’ and ‘cars’ (semantic segmentation) and draw bounding boxes around them to identify and segment each individual persons and cars and also classifying the different surrounding scenarios like road signals, traffic lights and all other building or backgrounds. So panoptic segmentation detects and interprets everything within a given image.
Panoptic segmentation leverages the strengths of fully convolutional networks (FCN) for semantic context and Mask R-CNN for instance-specific details, which gives a combined output for achieving a more holistic and nuanced understanding of visual data.
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: