SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)
Neural Style Transfer (NST) is a popular technique of computer vision where the content of an image is blended with the style of another, which results in a fused image with certain properties of both original images. This approach has practical applications in various domains and has garnered signi...
Main Authors: | Konstantinos Psychogyios, Helen C. Leligou, Filisia Melissari, Stavroula Bourou, Zacharias Anastasakis, Theodore Zahariadis |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10250775/ |
Similar Items
-
The Segment Anything Model (SAM) for accelerating the smart farming revolution
by: Alberto Carraro, et al.
Published: (2023-12-01) -
Crater Detection and Population Statistics in Tianwen-1 Landing Area Based on Segment Anything Model (SAM)
by: Yaqi Zhao, et al.
Published: (2024-05-01) -
GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model
by: Shuzhen Hua, et al.
Published: (2025-02-01) -
Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model
by: Andrés Larroza, et al.
Published: (2024-05-01) -
WaterSAM: Adapting SAM for Underwater Object Segmentation
by: Yang Hong, et al.
Published: (2024-09-01)