Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching

04/20/2018
by   Jing Chen, et al.
0

The seam-driven approach has been proven fairly effective for parallax-tolerant image stitching, whose strategy is to search for an invisible seam from finite representative hypotheses of local alignment. In this paper, we propose a graph-based hypothesis generation and a seam-guided local alignment for improving the effectiveness and the efficiency of the seam-driven approach. The experiment demonstrates the significant reduction of number of hypotheses and the improved quality of naturalness of final stitching results, comparing to the state-of-the-art method SEAGULL.

READ FULL TEXT

page 2

page 3

research
02/20/2018

Latent RANSAC

We present a method that can evaluate a RANSAC hypothesis in constant ti...
research
10/29/2020

Semi-Supervised Speech Recognition via Graph-based Temporal Classification

Semi-supervised learning has demonstrated promising results in automatic...
research
04/02/2020

Graph-based fusion for change detection in multi-spectral images

In this paper we address the problem of change detection in multi-spectr...
research
05/24/2018

Coarse-to-fine Seam Estimation for Image Stitching

Seam-cutting and seam-driven techniques have been proven effective for h...
research
08/29/2016

Correspondence Insertion for As-Projective-As-Possible Image Stitching

Spatially varying warps are increasingly popular for image alignment. In...
research
05/07/2016

On Improving Informativity and Grammaticality for Multi-Sentence Compression

Multi Sentence Compression (MSC) is of great value to many real world ap...

Please sign up or login with your details

Forgot password? Click here to reset