Fast image retargeting based on strip dividing and resizing

Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First,we construct the image energy map using gradient magnitude of the pixels and calculate the accumulated energy of each column,dividing the image into several strips by integrating similar energy columns. The reduced amount of dimension is decided in inverse proportion to the average energy for each strip. Then we retarget the image combining scaling with cropping in terms of each strip＇s reduced ratio. Experiment results show that the proposed algorithm is capable of implementing fast image retargeting and preserving both the local structures and the global visual effect of the image.

Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content- aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.

Video retargeting using optimized crop-and-scale

Mobile devices are increasingly powerful in multimedia transmitting and browsing. However, the small screen and different aspect ratios of mobile devices lower visual quality while watching images or videos. Video retargeting is aim at fitting an existing video into arbitrary size and aspect ratio. Previous content-aware retargeting methods are mostly high computational cost, which limits their applications on the portable devices with low computation ability. In this paper, a new crop-and-scale approach is presented to adapt video to better suit the target display. We automatically find the optimal parameters of cropping window using the dynamic programming method,, and then scale it to fit the target display. The cropping window can smoothly shift during a shot to follow the movement＇of important objects. The retargeting results using our approach introduce no deformation and jitter effects over the whole video. Experimental results show the success of our approach on adapting a variety of source videos to small display sizes.

Maya6．0新增功能展示

Maya6．0发布以后，再次向世人展示了其无与伦比的强大功能。作为一个动画工作者在此向maya的开发者们深表感激，是他们的辛勤开发才一次次的代给我们以惊喜与震撼，这次maya6的角色动画方面除了对Trax Edifer做了改进，还给我们增加了两个强大新的功能：Retarget和redirect。

Automatic Target Description File Generation

Agile hardware design is gaining increasing momentum and bringing new chips in larger quantities to the market faster.However,it also takes new challenges for compiler developers to retarget existing compilers to these new chips in shorter time than ever before.Currently,retargeting a compiler backend,e.g.,an LLVM backend to a new target,requires compiler developers to write manually a set of target description files(totalling 10300+lines of code(LOC)for RISC-V in LLVM),which is error-prone and time-consuming.In this paper,we introduce a new approach,Au-tomatic Target Description File Generation(ATG),which accelerates the generation of a compiler backend for a new tar-get by generating its target description files automatically.Given a new target,ATG proceeds in two stages.First,ATG synthesizes a small list of target-specific properties and a list of code-layout templates from the target description files of a set of existing targets with similar instruction set architectures(ISAs).Second,ATG requests compiler developers to fill in the information for each instruction in the new target in tabular form according to the list of target-specific properties syn-thesized and then generates its target description files automatically according to the list of code-layout templates synthe-sized.The first stage can often be reused by different new targets sharing similar ISAs.We evaluate ATG using nine RISC-V instruction sets drawn from a total of 1029 instructions in LLVM 12.0.ATG enables compiler developers to gen-erate compiler backends for these ISAs that emit the same assembly code as the existing compiler backends for RISC-V but with significantly less development effort(by specifying each instruction in terms of up to 61 target-specific properties only).

Color retargeting:Interactive time-varying color image composition from time-lapse sequences

In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.

SVM based layout retargeting for fast and regularized inverse lithography

Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.

A retargeting method for stereoscopic 3D video

We propose a disparity-constrained retargeting method for stereoscopic 3D video, which simultaneously resizes a binocular video to a new aspect ratio and remaps the depth to the perceptual comfort zone. First, we model distortion energies to prevent important video contents from deforming. Then, to maintain depth mapping stability, we model disparity variation energies to constraint the disparity range both in spatial and temporal domains. The last component of our method is a non-uniform, pixel-wise warp to the target resolution based on these energy models. Using this method, we can process the original stereoscopic video to generate new, high-perceptual-quality versions at different display resolutions. For evaluation, we conduct a user study; we also discuss the performance of our method.

Image resizing by reconstruction from deep features

Traditional image resizing methods usually work in pixel space and use various saliency measures.The challenge is to adjust the image shape while trying to preserve important content.In this paper we perform image resizing in feature space using the deep layers of a neural network containing rich important semantic information.We directly adjust the image feature maps,extracted from a pre-trained classification network,and reconstruct the resized image using neuralnetwork based optimization.This novel approach leverages the hierarchical encoding of the network,and in particular,the high-level discriminative power of its deeper layers,that can recognize semantic regions and objects,thereby allowing maintenance of their aspect ratios.Our use of reconstruction from deep features results in less noticeable artifacts than use of imagespace resizing operators.We evaluate our method on benchmarks,compare it to alternative approaches,and demonstrate its strengths on challenging images.

Model Transduction for Triangle Meshes

This paper proposes a novel method, called model transduction, to directly transfer pose between different meshes, without the need of building the skeleton configurations for meshes. Different from previous retargetting methods, such as deformation transfer, model transduction does not require a reference source mesh to obtain the source deformation, thus effectively avoids unsatisfying results when the source and target have different reference poses. Moreover, we show other two applications of the model transduction method： pose correction after various mesh editing operations, and skeleton-free deformation animation based on 3D Mocap （Motion capture） data. Model transduction is based on two ingredients： model deformation and model correspondence. Specifically, based on the mean-value manifold operator, our mesh deformation method produces visually pleasing deformation results under large angle rotations or big-scale translations of handles. Then we propose a novel scheme for shape-preserving correspondence between manifold meshes. Our method fits nicely in a unified framework, where the similar type of operator is applied in all phases. The resulting quadratic formulation can be efficiently minimized by fast solving the sparse linear system. Experimental results show that model transduction can successfully transfer both complex skeletal structures and subtle skin deformations.

A survey on high coherence visual media retargeting： recent advances and applications

The numerous works on media retargeting call for a thorough and comprehensive survey for reviewing and categorizing existing works and providing insights that can help future design of retargeting approaches and its applica- tions. First, we present the basic problem of media retarget- ing and detail state-of-the-art retargeting methods devised to solve it. Second, we review recent works on objective quality assessment of media retargeting, where we find that although these works are designed to make the objective assessment result in accordance with the subjective evaluation, they are only suitable for certain situations. Considering the subjective nature of aesthetics, designing objective assessment metric for media retargeting could be a promising area for investiga- tion. Third, we elaborate on other applications extended from retargeting techniques. We show how to apply the retarget- ing techniques in other fields to solve their challenging prob- lems, and reveal that retargeting technique is not just a simple scaling algorithm, but a thought or concept, which has great flexibility and is quite useful. We believe this review can help researchers and practitioners to solve the existing problems of media retargeting and bring new ideas in their works.

Retargeting——助力企业微营销

Retargeting是一种精准广告投放的尖端技术。企业可以通过这种技术,能将广告精准地投给曾经访问过特定网站或搜索过特定产品的人群,通过反复、多次曝光刺激消费,实现最终销售的目的。这一技术已成为电子商务、网络游戏、旅游、品牌网站的重要营销利器。在微营销大行其道的市场环境下,企业更应借力Retargeting来深度挖掘目标消费者的个性化需求和偏好,不断提升消费者的产品和服务体验。本文重点介绍Retargeting的概念,并分析个性化服务与Retargeting的区别及Retargeting的营销效果。