IPDDF:an improved precision dense descriptor based flow estimation

• 作者: Tiensze Lim Voonchet Koo Weiyong Eng
• 来源期刊: 智能技术学报
• 学科: 工学
• 关键词: ESTIMATION FLOW MATCHING
• 年，卷（期）: 2020,（1）
• 页码范围: 49-54
• 页数: 6页
• 分类号: TP3

摘要

Large displacement optical flow algorithms are generally categorised into descriptor-based matching and pixel-based matching.Descriptor-based approaches are robust to geometric variation,however they have inherent localisation precision limitation due to histogram nature.This work presents a novel method called improved precision dense descriptor flow(IPDDF).The authors introduce an additional pixel-based matching cost within an existing dense Daisy descriptor framework to improve the flow estimation precision.Pixel-based features such as pixel colour and gradient are computed on top of the original descriptor in the authors’matching cost formulation.The pixel-based cost only requires a light-weight pre-computation and can be adapted seamlessly into the matching cost formulation.The framework is built based on the Daisy Filter Flow work.In the framework,Daisy descriptor and a filter-based efficient flow inference technique,as well as a randomised fast patch match search algorithm,are adopted.Given the novel matching cost formulation,the framework enables efficiently solving dense correspondence field estimation in a high-dimensional search space,which includes scale and orientation.Experiments on various challenging image pairs demonstrate the proposed algorithm enhances flow estimation accuracy as well as generate a spatially coherent yet edge-aware flow field result efficiently.