Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Fuzhang Wu is active.

Publication


Featured researches published by Fuzhang Wu.


international conference on computer graphics and interactive techniques | 2014

Inverse procedural modeling of facade layouts

Fuzhang Wu; Dong-Ming Yan; Weiming Dong; Xiaopeng Zhang; Peter Wonka

In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework. Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful.


Computer Graphics Forum | 2013

Content-Based Colour Transfer

Fuzhang Wu; Weiming Dong; Yan Kong; Xing Mei; Jean-Claude Paul; Xiaopeng Zhang

This paper presents a novel content‐based method for transferring the colour patterns between images. Unlike previous methods that rely on image colour statistics, our method puts an emphasis on high‐level scene content analysis. We first automatically extract the foreground subject areas and background scene layout from the scene. The semantic correspondences of the regions between source and target images are established. In the second step, the source image is re‐coloured in a novel optimization framework, which incorporates the extracted content information and the spatial distributions of the target colour styles. A new progressive transfer scheme is proposed to integrate the advantages of both global and local transfer algorithms, as well as avoid the over‐segmentation artefact in the result. Experiments show that with a better understanding of the scene contents, our method well preserves the spatial layout, the colour distribution and the visual coherence in the transfer process. As an interesting extension, our method can also be used to re‐colour video clips with spatially‐varied colour effects.


IEEE Transactions on Visualization and Computer Graphics | 2014

Summarization-Based Image Resizing by Intelligent Object Carving

Weiming Dong; Ning Zhou; Tong-Yee Lee; Fuzhang Wu; Yan Kong; Xiaopeng Zhang

Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images. are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.


IEEE Transactions on Visualization and Computer Graphics | 2016

Image Retargeting by Texture-Aware Synthesis

Weiming Dong; Fuzhang Wu; Yan Kong; Xing Mei; Tong-Yee Lee; Xiaopeng Zhang

Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on exampled-based texture synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategies since they have different natures. We propose to retarget the textural regions by example-based synthesis and non-textural regions by fast multi-operator. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image retargeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.


international conference on computer graphics and interactive techniques | 2011

Distribution-aware image color transfer

Fuzhang Wu; Weiming Dong; Xing Mei; Xiaopeng Zhang; Xiaohong Jia; Jean-Claude Paul

Color transfer is a practical image editing technology which is useful in various applications. An ideal color transfer algorithm should keep the scene in the source image and apply the color styles of the reference image. All the dominant color styles of the reference image should be presented in the result especially when there are similar contents in the source and reference images.


The Visual Computer | 2016

Feature-aware natural texture synthesis

Fuzhang Wu; Weiming Dong; Yan Kong; Xing Mei; Dong-Ming Yan; Xiaopeng Zhang; Jean-Claude Paul

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies.


Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing | 2016

Symmetrization of facade layouts

Haiyong Jiang; Dong-Ming Yan; Weiming Dong; Fuzhang Wu; Liangliang Nan; Xiaopeng Zhang

We present an automatic approach for symmetrizing urban facade layouts. Our method can generate a symmetric layout through minimally modifying the original input layout. Based on the principles of symmetry in urban design, we formulate facade layout symmetrization as an optimization problem. Our method further enhances the regularity of the final layout by redistributing and aligning elements in the layout. We demonstrate that the proposed solution can effectively generate symmetric facade layouts.


Applied Physics A | 2007

Structural and electrical properties of 0.7Pb(Mg1/3Nb2/3)O3–0.3PbTiO3 thin films grown on Ir/MgO buffered Si(100) substrates

Fuzhang Wu; X.M. Li; W.D. Yu; Xue-Wang Gao; X. Zhang


arXiv: Graphics | 2014

Image Retargeting by Content-Aware Synthesis.

Weiming Dong; Fuzhang Wu; Yan Kong; Xing Mei; Tong-Yee Lee; Xiaopeng Zhang


arXiv: Computer Vision and Pattern Recognition | 2018

Image Retargetability.

Fan Tang; Weiming Dong; Yiping Meng; Chongyang Ma; Fuzhang Wu; Xinrui Li; Tong-Yee Lee

Collaboration


Dive into the Fuzhang Wu's collaboration.

Top Co-Authors

Avatar

Weiming Dong

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar

Xiaopeng Zhang

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar

Xing Mei

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar

Yan Kong

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Tong-Yee Lee

National Cheng Kung University

View shared research outputs
Top Co-Authors

Avatar

Dong-Ming Yan

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Xiaohong Jia

Chinese Academy of Sciences

View shared research outputs
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge