arXiv:2011.11956
공식 코드 · Papers with Code
Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models
원문 초록
Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic graph based on acoustic physical properties of ultrasound imaging. We demonstrate unique capabilities of our approach and compare it against previous confidence-measurement algorithms for shadow-detection and image-compounding tasks.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
When SAM2 Meets Video Shadow and Mirror Detection
Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era
RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification
Timeline and Boundary Guided Diffusion Network for Video Shadow Detection
Language-Driven Interactive Shadow Detection
SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.