Color Image Optical Flow Estimation Algorithm with Shadow Suppression

2013 ◽  
Author(s):  
Guojian Wei ◽  
Zhiqiang Hou ◽  
Wu Li ◽  
Wangsheng Yu
Keyword(s):  
Optical Flow ◽  
Color Image ◽  
Estimation Algorithm ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Shadow Suppression

Color Image Optical Flow Estimation Algorithm Fused with Color Invariants

2014 ◽  
Vol 35 (12) ◽  
pp. 2927-2933
Author(s):  
Guo-jian Wei ◽  
Zhi-qiang Hou ◽  
Wu Li ◽  
Wang-sheng Yu
Keyword(s):  
Optical Flow ◽  
Color Image ◽  
Estimation Algorithm ◽  
Flow Estimation ◽  
Optical Flow Estimation

scholarly journals Optical Flow Estimation with Occlusion Detection

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 92
Author(s):  
Song Wang ◽  
Zengfu Wang
Keyword(s):  
Optical Flow ◽  
Feature Matching ◽  
Estimation Algorithm ◽  
Occlusion Detection ◽  
Multiple Sources ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Dense Optical Flow ◽  
Video Frames ◽  
Optimization Function

The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.

GCPOF: a novel optical flow estimation algorithm leveraged by sparse ground control points

2016 ◽  
Vol 63 (16) ◽  
pp. 1592-1599
Author(s):  
Lai Kang ◽  
Yingmei Wei ◽  
Zheng Yang ◽  
Jie Jiang ◽  
Lingda Wu
Keyword(s):  
Optical Flow ◽  
Estimation Algorithm ◽  
Ground Control ◽  
Control Points ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Ground Control Points

scholarly journals Generating High-Quality Panorama by View Synthesis Based on Optical Flow Estimation

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 470
Author(s):  
Wenxin Zhang ◽  
Yumei Wang ◽  
Yu Liu
Keyword(s):  
Optical Flow ◽  
Subjective Experience ◽  
Image Quality Assessment ◽  
Estimation Algorithm ◽  
View Synthesis ◽  
High Quality ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Irregular Shapes ◽  
The Right

Generating high-quality panorama is a key element in promoting the development of VR content. The panoramas generated by the traditional image stitching algorithm have some limitations, such as artifacts and irregular shapes. We consider solving this problem from the perspective of view synthesis. We propose a view synthesis approach based on optical flow to generate a high-quality omnidirectional panorama. In the first stage, we present a novel optical flow estimation algorithm to establish a dense correspondence between the overlapping areas of the left and right views. The result obtained can be approximated as the parallax of the scene. In the second stage, the reconstructed version of the left and the right views is generated by warping the pixels under the guidance of optical flow, and the alpha blending algorithm is used to synthesize the final novel view. Experimental results demonstrate that the subjective experience obtained by our approach is better than the comparison algorithm without cracks or artifacts. Besides the commonly used image quality assessment PSNR and SSIM, we also calculate MP-PSNR, which can provide accurate high-quality predictions for synthesized views. Our approach can achieve an improvement of about 1 dB in MP-PSNR and PSNR and 25% in SSIM, respectively.

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