Panoptic Segmentation: A comprehensive pathway to A Real-World AI Vision

For computer vision tasks like object detection, recognition, and classification, relay on feature extraction, labelling and segmentation of captured videos or images. Applications like smart city, health care, geoscience and remote sensing are based on video analysis. Image segmentation for video analysis plays an vital role. One of the novel segmentation strategies which has been recently developed is panoptic segmentation. Panoptic segmentation is a fusion of semantic and instance segmentation. In self autonomous driving, medical image analysis, crowd counting, etc. have complicated background components, the high variability of object appearances, numerous overlapping objects and ambiguous object boundaries makes the task challenging. For such applications panoptic segmentation is used which provides several state-of-art methods and robust learning.

@article{ijter2025212508172907,
  author = {Dr Nellutla Sasikala and Vemuri Pravalika},
  title = {Panoptic Segmentation: A comprehensive pathway to A Real-World AI Vision},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {4},
  pages = {63-70},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212508172907/panoptic-segmentation-a-comprehensive-pathway-to-a-real-world-ai-vision},
  abstract = {For computer vision tasks like object detection, recognition, and classification, relay on feature extraction, labelling and segmentation of captured videos or images. Applications like smart city, health care, geoscience and remote sensing are based on video analysis. Image segmentation for video analysis plays an vital role. One of the novel segmentation strategies which has been recently developed is panoptic segmentation. Panoptic segmentation is a fusion of semantic and instance segmentation. In self autonomous driving, medical image analysis, crowd counting, etc. have complicated background components, the high variability of object appearances, numerous overlapping objects and ambiguous object boundaries makes the task challenging. For such applications panoptic segmentation is used which provides several state-of-art methods and robust learning.},
  keywords = {panoptic segmentation, instance segmentation, semantic segmentation, data annotation, computer vision, medical image segmentation, object detection},
  month = {Aug},
}

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