Brushables: Example-based Edge-aware Directional Texture Painting

2015 ◽  
Vol 34 (7) ◽  
pp. 257-267 ◽  
Author(s):  
M. Lukáč ◽  
J. Fišer ◽  
P. Asente ◽  
J. Lu ◽  
E. Shechtman ◽  
...  
Keyword(s):  
Directional Texture

scholarly journals A New Error Concealment Algorithm Based on Directional Texture Synthesis

2012 ◽  
Vol 6-7 ◽  
pp. 309-314
Author(s):  
Jiang Xin Zhang ◽  
Jin Xie
Keyword(s):  
Image Reconstruction ◽  
Confidence Level ◽  
Error Concealment ◽  
Texture Synthesis ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Structure Information ◽  
Video Images ◽  
Directional Texture

In this paper, we propose a novel directional texture synthesis based error concealment algorithm to recover damaged video images. It uses the confidence level and structure information to calculate the priority of patch, which contributes to improve the ability to select the best matching block when the damaged area is very large. The JM86 model of H.264 standard is used to evaluate the algorithm. And experimental results show that our algorithm achieved a better image reconstruction results than the improved Multi-directional texture interpolation algorithm, with 1.2 to 1.4dB gain in PSNR and 0.5 percent to 1 percent gain in SSIM.

Directional texture transfer for video

2013 ◽  
Vol 74 (1) ◽  
pp. 245-258 ◽  
Author(s):  
Dongwann Kang ◽  
Phutphalla Kong ◽  
KyungHyun Yoon ◽  
SangHyun Seo
Keyword(s):  
Texture Transfer ◽  
Directional Texture

scholarly journals Efficient example-based painting and synthesis of 2D directional texture

2004 ◽  
Vol 10 (3) ◽  
pp. 266-277 ◽  
Author(s):  
Bin Wang ◽  
Wenping Wang ◽  
Huaiping Yang ◽  
Jiaguang Sun
Keyword(s):  
Directional Texture

scholarly journals Compact Local Directional Texture Pattern for Local Image Description

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hui Zeng ◽  
Rui Zhang ◽  
Mingming Huang ◽  
Xiuqing Wang
Keyword(s):  
Image Matching ◽  
Local Region ◽  
Image Feature ◽  
Image Description ◽  
Texture Pattern ◽  
Gradient Magnitude ◽  
Gradient Orientation ◽  
Contrast Information ◽  
Directional Texture ◽  
Local Image

This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

Research on Halftone Technique of Digital Images

2014 ◽  
Vol 989-994 ◽  
pp. 4082-4085
Author(s):  
Wen Bo Dai ◽  
Liang Chen ◽  
Xin Wei Zhang ◽  
Le Wang ◽  
Shi Yue Nie ◽  
...  
Keyword(s):  
Binary Data ◽  
Digital Images ◽  
Error Diffusion ◽  
Correction Factors ◽  
Feedback Type ◽  
Black And White ◽  
Feedback Error ◽  
Simulation Results ◽  
Directional Texture ◽  
Ordered Dithering

Halftone is the key technique for low-resolution equipment like fax machines and printers to reconstruct grayscale images with black and white binary data. Images produced by classic error diffusion algorithm have more abundant color than those made by ordered dithering method. But the problems still exits that there can distortions and false contour phenomenon and fuzzy edges. This paper puts forward an advanced error diffusion algorithm based on bidirectional feedback and improves its feedback type through adding correction factors. The simulation results show that the bidirectional feedback error diffusion algorithm with correction factors better inhibited the phenomenon of distortions and false contour. Directional texture is also improved greatly.

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