Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Autor:	Sarfaraz Ahmed Mohammed, Anca L. Ralescu
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	convolutional neural networks image segmentation computer vision instance segmentation semantic segmentation panoptic segmentation Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Algorithms, Vol 17, Iss 5, p 189 (2024)
Druh dokumentu:	article
ISSN:	17050189 1999-4893
DOI:	10.3390/a17050189
Popis:	Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies.
Databáze:	Directory of Open Access Journals
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