Building Resilient Vision Systems: Addressing Distortions in Real-World Images
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Beschreibung
Consequently, temporal prediction has been established as a way to achieve a deeper understanding of the data as it requires an implicit understanding of the structure of the observed visual features and the rules they follow while they change over time. Given this, recent approaches in the field usually extract the temporal properties of the data by learning a strong temporal regularization [85, 147], which helps to find clusters over time but may result in a lower ability to identify segments based on their visual representation.
This book targets computer vision engineers and researchers developing systems that analyze real-world images, facing challenges like lens distortion and variations in lighting.
Autor*in
Shah
Dr. Shah, a distinguished neurologist and healthcare economist, spearheads the "Neuro Care Revolution" initiative, merging clinical expertise with financial acumen to optimize patient outcomes and reduce healthcare costs. With extensive experience in neurology and healthcare management, Dr. Shah is committed to reshaping the landscape of neurological care.
Themen in »Building Resilient Vision Systems: Addressing Distortions in Real-World Images«
Computer vision Image distortion Real-world image processing Image pre-processing Noise reduction techniques Geometric distortions Photometric distortions Deep learning image restoration Domain adaptation