基于格式塔的建筑物图画抽象

格式塔(Gestalt)理论总结了人类认知规则、模式和语义的若干规律,已有大量的研究针对独立的格式塔规则建立计算模型,但建立多个格式塔规则相互作用的耦合模型仍然是一个挑战.基于格式塔理论的若干主要规则建立了一个针对多个格式塔规则耦合的计算模型,并将该模型用于建筑物线画图的抽象;通过减少图画中重复结构元素的数目,或者将复杂的结构用简单结构替换的方式对建筑物结构进行概括和抽象.实验结果表明,该方法可以应用于风格各异的建筑物线画图,并可以扩展到其他类型的图画以及三维模型.

Deep residual learning for denoising Monte Carlo renderings

Learning-based techniques have recently been shown to be effective for denoising Monte Carlo rendering methods. However, there remains a quality gap to state-of-the-art handcrafted denoisers. In this paper, we propose a deep residual learning based method that outperforms both state-of-the-art handcrafted denoisers and learning-based denoisers.Unlike the indirect nature of existing learning-based methods(which e.g., estimate the parameters and kernel weights of an explicit feature based filter), we directly map the noisy input pixels to the smoothed output. Using this direct mapping formulation, we demonstrate that even a simple-and-standard ResNet and three common auxiliary features(depth, normal,and albedo) are sufficient to achieve high-quality denoising. This minimal requirement on auxiliary data simplifies both training and integration of our method into most production rendering pipelines. We have evaluated our method on unseen images created by a different renderer. Consistently superior quality denoising is obtained in all cases.

Boundary-aware texture region segmentation from manga

Due to the lack of color in manga(Japanese comics), black-and-white textures are often used to enrich visual experience. With the rising need to digitize manga, segmenting texture regions from manga has become an indispensable basis for almost all manga processing, from vectorization to colorization. Unfortunately, such texture segmentation is not easy since textures in manga are composed of lines and exhibit similar features to structural lines(contour lines). So currently, texture segmentation is still manually performed, which is labor-intensive and time-consuming. To extract a texture region, various texture features have been proposed for measuring texture similarity, but precise boundaries cannot be achieved since boundary pixels exhibit different features from inner pixels. In this paper, we propose a novel method which also adopts texture features to estimate texture regions. Unlike existing methods, the estimated texture region is only regarded an initial, imprecise texture region. We expand the initial texture region to the precise boundary based on local smoothness via a graph-cut formulation. This allows our method to extract texture regions with precise boundaries. We have applied our method to various manga images and satisfactory results were achieved in all cases.

An improved parallel contrast-aware halftoning

Digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time cost remains a challenging problem. This paper presents a highly parallel algorithm to boost real-time application of serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, but it offers only a limited opportunity for graphics processing unit(GPU) acceleration. Our method integrates contrast-aware halftoning into a new parallelizable error-diffusion halftoning framework. To eliminate visually disturbing artifacts resulting from parallelization, we propose a novel multiple quantization model and space-filling curve to maintain tone consistency, blue-noise property, and structure consistency. Our GPU implementation on a commodity personal computer achieves a real-time performance for a moderately sized image. We demonstrate the high quality and performance of the proposed approach with a variety of examples, and provide comparisons with state-of-the-art methods.

TransHist:Occlusion-robust shape detection in cluttered images

Shape matching plays an important role in various computer vision and graphics applications such as shape retrieval, object detection, image editing,image retrieval, etc. However, detecting shapes in cluttered images is still quite challenging due to the incomplete edges and changing perspective. In this paper, we propose a novel approach that can efficiently identify a queried shape in a cluttered image. The core idea is to acquire the transformation from the queried shape to the cluttered image by summarising all pointto-point transformations between the queried shape and the image. To do so, we adopt a point-based shape descriptor, the pyramid of arc-length descriptor(PAD),to identify point pairs between the queried shape and the image having similar local shapes. We further calculate the transformations between the identified point pairs based on PAD. Finally, we summarise all transformations in a 4 D transformation histogram and search for the main cluster. Our method can handle both closed shapes and open curves, and is resistant to partial occlusions. Experiments show that our method can robustly detect shapes in images in the presence of partial occlusions, fragile edges, and cluttered backgrounds.

Region-based structure line detection for cartoons

Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays,with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation,colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images.Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors,which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region.These measure darkness, local contrast, and shape.Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.

Flow-aware synthesis: A generic motion model for video frame interpolation

A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.