Visualization by P-flow: gradient- and feature-based optical flow and vector fields extracted from image analysis

2020 ◽  
Vol 37 (12) ◽  
pp. 1958
Author(s):  
Wataru Suzuki ◽  
Atsushi Hiyama ◽  
Noritaka Ichinohe ◽  
Wakayo Yamashita ◽  
Takeharu Seno ◽  
...  
Keyword(s):  
Image Analysis ◽  
Optical Flow ◽  
Vector Fields ◽  
Flow Gradient ◽  
Feature Based

Interframe registration of IVUS images due to catheter rotation with feature-based optical flow

2007 ◽  
Author(s):  
Mikhail G. Danilouchkine ◽  
Frits Mastik ◽  
Antonius F. W. van der Steen
Keyword(s):  
Optical Flow ◽  
Feature Based

Optimization of Image Zernike Moments Shape Feature Based on Evolutionary Computation

2013 ◽  
pp. 199-216
Author(s):  
Maofu Liu ◽  
Huijun Hu
Keyword(s):  
Image Analysis ◽  
Evolutionary Computation ◽  
High Dimension ◽  
Optimization Algorithm ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature ◽  
Original Image ◽  
Feature Based ◽  
Image Shape

The image shape feature can be described by the image Zernike moments. In this chapter, the authors point out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. Therefore, the optimization algorithm based on evolutionary computation is designed and implemented in this chapter to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

scholarly journals MOUNTAINOUS REMOTE SENSING IMAGES REGISTRATION BASED ON IMPROVED OPTICAL FLOW ESTIMATION

2019 ◽  
Vol IV-2/W5 ◽  
pp. 479-484 ◽  
Author(s):  
R. Feng ◽  
X. Li ◽  
H. Shen
Keyword(s):  
Remote Sensing ◽  
Optical Flow ◽  
Remote Sensing Image ◽  
Transformation Model ◽  
Geometric Distortion ◽  
Estimation Methods ◽  
Remote Sensing Images ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Feature Based

<p><strong>Abstract.</strong> Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To address this problem, we proposed an improved optical flow estimation concentrated on post-processing, namely displacement modification. First of all, the Laplacian of Gaussian (LoG) algorithm is employed to detect the abnormal value in color map of displacement. Then, the abnormal displacement is recalculated in the interpolation surface constructed by the rest accurate displacements. Following the successful coordinate transformation and resampling, the registration outcome is generated. Experiments demonstrated that the proposed method is insensitive in changeable region of mountainous remote sensing image, generating precise registration, outperforming the other local transformation model estimation methods in both visual judgment and quantitative evaluation.</p>

Sign in / Sign up

Export Citation Format

Share Document