arXiv:2201.09286
공식 코드 · Papers with Code
How to scale hyperparameters for quickshift image segmentation
원문 초록
Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameters' influence on the number and shape of superpixels produced by the method. In this paper, we study theoretically a slightly modified version of the quickshift algorithm, with a particular emphasis on homogeneous image patches with i.i.d. pixel noise and sharp boundaries between such patches. Leveraging this analysis, we derive a simple heuristic to scale quickshift hyperparameters with respect to the image size, which we check empirically.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers
Rethinking Query-based Transformer for Continual Image Segmentation
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.