Field‐aligned Quadrangulation for Image Vectorization

2019 ◽  
Vol 38 (7) ◽  
pp. 171-180 ◽  
Author(s):  
Guangshun Wei ◽  
Yuanfeng Zhou ◽  
Xifeng Gao ◽  
Qian Ma ◽  
Shiqing Xin ◽  
...  
Keyword(s):  
Image Vectorization

Patch-based image vectorization with automatic curvilinear feature alignment

2009 ◽  
Vol 28 (5) ◽  
pp. 1-10 ◽  
Author(s):  
Tian Xia ◽  
Binbin Liao ◽  
Yizhou Yu
Keyword(s):  
Image Vectorization ◽  
Feature Alignment

Simplicity Driven Edge Refinement and Color Reconstruction in Image Vectorization

2021 ◽  
pp. 452-463
Author(s):  
Zheng Zhang ◽  
Junhao Zhao ◽  
Shiqing Xin ◽  
Shuangmin Chen ◽  
Yuanfeng Zhou ◽  
...  
Keyword(s):  
Image Vectorization

DBSC-Based Grayscale Line Image Vectorization

2006 ◽  
Vol 21 (2) ◽  
pp. 244-248 ◽  
Author(s):  
Konstantin Melikhov ◽  
Feng Tian ◽  
Jie Qiu ◽  
Quan Chen ◽  
Hock Soon Seah
Keyword(s):  
Line Image ◽  
Image Vectorization

Research and Implementation of Remote Sensing Image Vectorization

2010 ◽  
Vol 439-440 ◽  
pp. 220-224
Author(s):  
Hai Yan Wu
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Collection Efficiency ◽  
Remote Sensing Image ◽  
Tracking Algorithm ◽  
Edge Enhancement ◽  
Boundary Tracking ◽  
Edge Tracking ◽  
Open Operation ◽  
Image Vectorization

Remote sensing image vectorization; edge enhancement; boundary tracking; Abstract. This paper researched and implemented remote sensing image vectorization process using semi-automated way. Remote sensing image can be vectored as the following five steps:vertical and horizontal edge enhancement, open operation, close operation, binarization, and finally the boundary tracking. The paper presented an improved tracking algorithm to avoid the shortcomings of traditional edge tracking algorithm. In our tracking algorithm, for image maps, we first use open operation and then use close operation to remove noise.This vectorization method changed the traditional data collection mode and improved data collection efficiency.

Research on Image Vectorization Based on Dual-Thumb Pneumatic Flexible Robot Hand

2014 ◽  
Vol 989-994 ◽  
pp. 3181-3184
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Qun Song Zhu ◽  
Cong Yan
Keyword(s):  
Feature Fusion ◽  
Nonlinear Problems ◽  
Middle Finger ◽  
Artificial Muscles ◽  
Robot Hand ◽  
Flexible Robot ◽  
Inference Mechanism ◽  
Flexible Joint ◽  
Image Vectorization ◽  
Parallel Arrangement

Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted. Fuzzy rules and its inference mechanism are the assurance of achieving feature fusion. This paper has a design and analysis on the dual thumb pneumatic flexible robot hand base on the active flexible bending joints we have developed. The flexible joint is composed of four elongation artificial muscles with parallel arrangement. The robot hand has five multi-actuated fingers each with three flexible joints. To improve the flexibility and stability of grasp, the other four fingers are orthogonal and symmetrical except the middle finger, which forms the dual thumb oriented grasp model. Furthermore, we study on the position and posture of fingers applying the parameterized homogeneous transformation to the large deformation and nonlinear problems of flexible joint.

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