In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

personal photos on the smart phones is a common yet uneasy task for users due to the large volume of photos taken in daily life. Inspired by the human memory and its natural recall characteristics, we build a personal photo re-visitation tool, PhotoPrev, to facilitate users to re-find previous photos through associated memory cues. To mimic users' episodic memory recall, we present a way to automatically generate an abundance of related contextual metadata (e.g. weather, temperature, etc) and organize them as context lattices for each photo in a life cycle. Meanwhile, photo content (e.g. object,text) is extracted and managed in a weighted term list, which corresponds to semantic memory. A TA-based top-k photo re-visitation algorithm for context-and content-based keyword search on a personal photo collection, together with a user feedback mechanism, are also given. We evaluate the scalability on a large synthetic data, and a 12-week user study demonstrates the feasibility and effectiveness of our photo re-visitation strategies.

Video cutout refers to extracting moving objects from videos, which is an important step in many video editing tasks. Recent algorithms have limitations in terms of efficiency, interaction style and robustness. This paper presents a novel method for progressive video cutout with less user interaction and fast feedback. By exploring local and compact features, an optimization is constructed based on a graph model which establishes spatial and temporal relationship of neighboring patches in video frames. This optimization enables an efficient solution for progressive video cutout using graph cuts. Furthermore, a sampling-based method for temporally coherent matting is proposed to further refine video cutout results. Experiments demonstrate that our video cutout by paint selection is more intuitive and efficient for users than previous stroke-based methods, thus could be put into practical use.

This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colorization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing K nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.

From a visual point of view, the shape of an image is mainly determined by the edges. Conventional polynomial interpolation of image enlarging methods would produce blurred edges, while edge-directed interpolation based methods would cause detail distortion in the non-edge areas. A new method for image enlarging is presented. The image is enlarged in two steps. In the first step, a fitting surface is constructed to interpolate the image data. To remove the zigzagging artifact, for each pixel, a fitting patch is constructed using edge information as constraints. The combination of all the patches forms the fitting surface which has the shape suggested by image data. Each point on the fitting surface can be regarded as a sampling point taken from a unit square domain, which means that when the fitting surface is used to enlarge the image, each sampling domain of the enlarged pixels is also a unit square, causing the enlarged image to lose some details. To make the enlarged image keep the details as much as possible, the sampling domain of the enlarged pixels should be less than a unit square. Then, in the second step, using the points taken from the fitting surface, new pixels are computed using constrained optimization technique to form the enlarged image, the size of the sampling domain of the enlarged pixels is inversely proportional to the size of the enlarged image. The enlarged image has a quadratic polynomial precision. Comparison results of the new method with other methods are included.

Similarity learning has always been a popular topic in computer vision research. Among this, facial similarity is especially important and difficult due to its wide applications and the nonrigid nature of human faces. The large gap between feature representations and human perceptual descriptions makes the problem even harder. In this paper, we learn facial similarity through human-computer interactions. To learn perceptual similarities of faces in a gallery set, we ask users to label some candidate images with their similarities to a probe image. Based on users' responses, a sampling algorithm actively generates a probe image and a set of candidates for the next query. Assisted with human efforts, the algorithm embeds all the images in a space where the distance between two subjects conforms to their dissimilarity in human perception. We apply the learned embedding to face retrieval and compare our method to some feature-based methods on a dataset we collect from social network sites (SNS). Experimental results demonstrate that incorporating human efforts can ensure retrieval accuracy. At the same time, the active sampling algorithm reduces human efforts.

Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time interval, and use oriented bounding boxes to speedup the process. However, these discrete methods cannot guarantee no penetration occurs when the components moving. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent process. To solve these problems, we propose an approach for real-time collision detection by utilizing the computational power of modern GPUs. First we present a novel GPU-based collision handling framework for virtual disassembly. Next we use a collision-streams based continuous collision detection to guarantee no collision missed. Finally we introduce a triangle intersection detection algorithm to solve the problem that collision cannot be detected when the components are embedded into each other at the initial configuration. The experiment results show that our method can improve the overall performance of collision detection, and achieve real-time simulation.

Photon mapping is a global illumination algorithm which is composed of two steps: photon tracing and photon searching. During photon searching step, each shading point needs to search the photon-tree to find k-neighbouring photons for reflected radiance estimation. As the number of shading points and the size of photon-tree are dramatically large, the photon searching step is time consuming. We proposed a parallel photon searching algorithm by using radiance estimation approach for coherent shading points on the Intel Many^® Integrated Core (MIC) Architecture. In order to efficiently use single instruction multiple data (SIMD) units, shading points are clustered by similarity first (every cluster contains 16 shading-points), and an initial neighbouring scope is searched from the photon-tree for each cluster. Then we use 16-wide SIMD units by performing K-NN searching from the initial neighbouring scope for those 16 shading-points in a cluster in parallel. We use the method to simulate some global illumination scenes on Intel^® Xeon^® Processors and Intel^® Xeon^® Phi^TM Coprocessors. The comparison results with existing photon mapping techniques indicate that our method gives significant improvement in speed with the same accuracy.

We present a prototype to generate a garment-shape sequence guided by a monocular video sequence. It is a combination of a physically-based simulation and a boundary-based modification. Given a garment in the video worn on a mannequin, the simulation generates a garment initial shape by exploiting the mannequin shapes estimated from the video. The modification then deforms the simulated 3D shape into such a shape that matches the garment 2D boundary extracted from the video. According to the matching correspondences between the vertices on the shape and points on the boundary, the modification is implemented by attracting the matched vertices and their neighboring vertices. For best-matching correspondences and efficient performance, three criterions are introduced to select the candidate vertices for matching. Since modifying each garment shape independently may cause inter-frame oscillations, changes by the modification are also propagated from one frame to the next frame. As a result, the generated garment 3D shape sequence is stable and similar to the garment video sequence. We demonstrated the effectiveness of our prototype with a number of examples.

This paper presents a spectral approach to compress dynamic animation consisting of a sequence of homeo-morphic manifold meshes. Our new approach directly compresses the field of deformation gradient defined on the surface mesh, by decomposing it into rigid-body motion (rotation) and non-rigid-body deformation (stretching/shearing) through polar decomposition. It is known that the Rotation Group has the algebraic topology of 3-D ring, which is different from other operations like stretching and shearing. Thus we compress these two groups separately, by using Manifold Harmonics Transform to drop out their high-frequency details. Our experimental result shows that our method achieves a good balance between the reconstruction quality and compression ratio. We compare our results quantitatively with other existing approaches on animation compression, using standard measurement criteria.

Not many methods for parameterization guarantee bijectivity or local injectivity, which is essential for foldover-free mappings. Stretch-minimizing parameterization which is widely used for surface parameterization, provides foldover-free mappings and is capable of trading off between angle and area distortions. We extend its usage to volumetric parameterization in this paper by deriving a 3D version of stretch-distortion energy and incorporating fixed boundary conditions. Our energy definition includes a natural barrier term which effectively prevents elements from collapsing and folding over. It saves the effort in other methods of formulating additional energy or constrains to ensure free of foldover.
We propose to minimize the overall energy integrated over the whole mesh with a relaxation-enhanced solver, which optimizes the parameterization globally. This is different from the conventional approach of surface parameterization where mesh nodes are optimized individually. Comparing to other volumetric parameterization, our approach bears the advantages of stretch-minimizing method, being foldover-free and offering a good trade-off between angle and volume distortions.

In this article, we investigate the use of joint α-entropy for 3D ear matching by incorporating local shape feature of 3D ears into the joint α-entropy. First, we extract a sufficient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the Minimum Spanning Tree (MST) on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint α-entropy—the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate and high robustness. Furthermore, we demonstrate, for the first time in computer graphics, the classical information theory of joint α-entropy is used to deal with 3D ear shape recognition.

Online social networks (OSNs) have revolutionarily changed the way people connect with each other. One of the main factors that help achieve this success is reputation systems that enable OSN users to mutually establish trust relationships based on their past experience. Current approaches for the reputation management cannot achieve the fine granularity and verifiability for each individual user, in the sense that the reputation values on such OSNs are coarse and lack of credibility. In this paper, we propose a fine granularity attribute-based reputation system which enables users to rate each other's attributes instead of identities. Our scheme first verifies each OSN user's attributes, and further allows OSN users to vote on the posted attribute-associated messages to derive the reputation value. The attribute verification process provides the authenticity of the reputation value without revealing the actual value to entities who do not have the vote privilege. To predict a stranger's behavior, we propose a reputation retrieval protocol for querying the reputation value on a specific attribute. To the best of our knowledge, we are the first to define a fine-grained reputation value based on users' verified attributes in OSNs with privacy preservation. We provide the security analysis along with the simulation results to verify the privacy preservation and feasibility. The implementation of the proposed scheme on current OSNs is also discussed.

Ricci flow deforms the Riemannian metric proportional to the curvature, such that thecurvature evolves according to a nonlinear heat diffusion process, and becomes constant eventually. Ricci flow is a powerful computational tool to design Riemannian metrics by prescribed curvatures.Surface Ricci flow has been generalized to the discrete setting. This work surveys the theory of discrete surface Ricci flow, its computational algorithms, and applications for surface registration and shape analysis.

As technology advances, there is a large gap between traditional congestion model used in global routing and routing resource consumption in detailed routing. The new factors contributing to congestion include local pin access paths, vias, and various design rules. In this paper, we propose a practical congestion model with measurement of the impact of design rules, and resources consumed by vias and local pin access paths. This congestion model is compatible with path search algorithms used in global routing. Validated by experiments using full-flow routing, this congestion model correlates better with real resource consumption situation in detailed routing. Compared with previous work, it leads to much better solution quality and shorter runtime of detailed routing when it is used in layer assignment phase of global routing stage.

Inverse lithography technology (ILT) is one of the promising resolution enhancement techniques (RETs), as the advanced integrated circuits (IC) technology nodes still use the 193nm light source. Among all the algorithms for ILT, the level-set-based ILT (LSB-ILT) is a feasible choice with good production result in practice. However, existing ILT algorithms optimize mask at nominal process condition without giving sufficient attention to the process variations, and thus the optimized masks show poor performance with focus and dose variations. In this paper, we put forward a new LSB-ILT algorithm for process robustness improvement with fast convergence. In order to account for the process variations in the optimization, we adopt a new form of the cost function by adding the objective function of process variation band (PV band) to the nominal cost. We also adopt the hybrid conjugate gradient (CG) method to reduce the runtime of the algorithm. We perform experiments on ICCAD 2013 benchmarks and the results show that our algorithm outperforms the top two winners of the ICCAD 2013 contest by 6.5%. We also adopt the attenuated Phase Shift Mask (att-PSM) in the experiment with test cases from industry. The results show that our new algorithm has fast convergence speed and reduces the process manufacturability index (PMI) by 38.77% compared with the LSB-ILT algorithm without PV band.

Cloud computing, after its success as a commercial infrastructure, is now emerging as a private infrastructure. The software platforms available to build private cloud computing infrastructure vary in their performance for management of cloud resources as well as in utilization of local physical resources. Organizations and individuals looking forward to reaping the benefits of private cloud computing need to understand which software platform would provide the efficient services and optimum utilization of cloud resources for their target applications. In this paper, we present our initial study on performance evaluation and comparison of three cloud computing software platforms from the perspective of common cloud users who intend to build their private clouds. We compare the performance of the selected software platforms from several respects describing their suitability for applications from different domains. Our results highlight the critical parameters for performance evaluation of a software platform and the best software platform for different application domains.