nils-herrmann/clean_scirepeval huggingface dataset:
Solving the out-of-gamut problem in image composition

Existing digital image composition algorithms neglect the out-of-gamut problem, i.e. some pixel values in a composited image exceed the displayable or printable range. The commonly used solutions, including hard clipping or linear scaling, result in either detail loss or global contrast reduction. Directly applying the existing high dynamic range (HDR) compression algorithms cannot achieve pleasant visual quality either. In our previous work, we proposed a gamut fitting algorithm by formulating gamut fitting as an energy-minimization problem and used the cubic polynomial in the Bernstein-Bézier form to compute the optimal mapping curve. Despite the good performance achieved, the problem of the previous algorithm lies in the necessity of fine tuning the weighting parameter in the proposed energy function. In this paper, we further improve our previous method by using piecewise mapping curves with multiple Bernstein polynomials to find the optimal mapping curve. The proposed approach can be regarded as a post-process to enhance the visual quality of the resulting images from image composition applications. The performance of the proposed method is compared with our previous method and the state-of-the-art HDR compression algorithms.
Diagnostic strategy and modeling of PMSM stator winding fault in electric vehicles

Permanent Magnet Synchronous Motor (PMSM) as the key component of the Electric Vehicles (EVs), has numerous advantages over other synchronous motors. Fault diagnosis is essential to guarantee the safety and continuity of the operation for EVs. An undetected fault in the PMS M system may lead to high repair cost, or even catastrophic failure. Among lots of faults in the PMSM, the most serious are short circuit faults, such as inter-turn fault, phase-to-ground fault. If it is not detected in time, the inter-turn fault may lead to phase-to-ground fault. For the PMSM, the early detection of motor failure has a very important significance. In this paper, the mathematic model and the phase-to-ground fault model of PMSM in abc coordinates is proposed, and the corresponding model is established based on MATLAB /Simulink. The zero-sequence voltage component (ZSVC) is analyzed in the inter-turn fault, which provides the basis for the fault diagnosis of PMSM.
Adaptive patch size determination for patch-based image completion

Patch-based image completion proceeds by iteratively filling the target (unknown) region by the best matching patches in the source image. Inmost existing such algorithms, the size of the patches is either fixed and specified by a default number or simply chosen to be inversely proportional to the spatial frequency. However, it is noted that the patch size affects how well the filled patch captures the local characteristics of the source image and thus the final completion accuracy. Thus in this paper we propose a new method to compute appropriate patch sizes for image completion to improve its performance. In particular, we formulate the patch size determination as an optimization problem that minimizes an objective function involving image gradients and distinct and homogenous features. Experimental results show that our method can provide a significant enhancement to patch-based image completion algorithms.
Globally Consistent Wrinkle-Aware Shading of Line Drawings

Shading is a tedious process for artists involved in 2D cartoon and manga production given the volume of contents that the artists have to prepare regularly over tight schedule. While we can automate shading production with the presence of geometry, it is impractical for artists to model the geometry for every single drawing. In this work, we aim to automate shading generation by analyzing the local shapes, connections, and spatial arrangement of wrinkle strokes in a clean line drawing. By this, artists can focus more on the design rather than the tedious manual editing work, and experiment with different shading effects under different conditions. To achieve this, we have made three key technical contributions. First, we model five perceptual cues by exploring relevant psychological principles to estimate the local depth profile around strokes. Second, we formulate stroke interpretation as a global optimization model that simultaneously balances different interpretations suggested by the perceptual cues and minimizes the interpretation discrepancy. Lastly, we develop a wrinkle-aware inflation method to generate a height field for the surface to support the shading region computation. In particular, we enable the generation of two commonly-used shading styles: 3D-like soft shading and manga-style flat shading.
CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images.

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data.11.All these coarse-scale and fine-scale photo-realistic face image datasets can be downloaded from https://github.com/Juyong/3DFace. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.
A color-guided, region-adaptive and depth-selective unified framework for Kinect depth recovery

Considering the existing depth recovery approaches that have different limitations when applying to Kinect depth data, in this paper, we propose to integrate their effective features including adaptive support region selection, reliable depth selection and color guidance together under a unified framework for Kinect depth recovery. In particular, we formulate our depth recovery as an energy minimization problem, which solves the depth hole-filling and denoising simultaneously. The energy function consists of a fidelity term and a regularization term. The fidelity term takes into account the characteristics of Kinect data. The regularization term is designed to incorporate the joint bilateral filtering (JBF) kernel and the joint trilateral filtering (JTF) kernel so as to facilitate both depth hole-filling and denoising. Moreover, the JBF kernel is modified to incorporate the structure information. Both simulations on the benchmark Middlebury dataset and experiments on real Kinect data show that our proposed method achieves state-of-the-art performance in terms of recovery accuracy and visual quality.
Automatic Path Planning for Dual-Crane Lifting in Complex Environments Using a Prioritized Multiobjective PGA

Cooperative dual-crane lifting is an important but challenging process involved in heavy and critical lifting tasks. This paper considers the path planning for the cooperative dual-crane lifting. It aims to automatically generate optimal dual-crane lifting paths under multiple constraints, i.e., collision avoidance, coordination between the two cranes, and balance of the lifting target. Previous works often used oversimplified models for the dual-crane lifting system, the lifting environment, and the motion of the lifting target. They were thus limited to simple lifting cases and might even lead to unsafe paths in some cases. We develop a novel path planner for dual-crane lifting that can quickly produce optimized paths in complex 3-D environments. The planner has fully considered the kinematic structure of the lifting system. Therefore, it is able to robustly handle the nonlinear movement of the suspended target during lifting. The effectiveness and efficiency of the planner are enabled by three novel aspects: 1) a comprehensive and computationally efficient mathematical modeling of the lifting system; 2) a new multiobjective parallel genetic algorithm designed to solve the path planning problem; and 3) a new efficient approach to perform continuous collision detection for the dual-crane lifting target. The planner has been tested in complex industrial environments. The results show that the planner can generate dual-crane lifting paths that are easy for conductions and optimized in terms of costs for complex environments. Comparisons with two previous methods demonstrate the advantages of the planner, including safer paths, higher success rates, and the ability to handle general lifting cases.
Photometric stereo using mesh face based optimization

The state-of-the-art photometric stereo (PS) methods typically apply shading cues on each vertex and represent a vertex normal as a non-linear function of its neighboring vertices. Such vertex-based representation leads to huge computational cost and limits it from processing dense meshes. In this paper, we propose a PS based surface reconstruction using mesh face based representation. In particular, we propose to apply the shading cue on each mesh face instead of each vertex and optimize face normals instead of vertex normals. We develop a two-step approach to solve the surface recovery problem, where we first optimize face normals using shading cues and then update vertices by the optimized face normals. Experimental results show that, compared with the state-of-the-art, such a two-step approach is able to reduce the runtime significantly as well as handling much denser meshes.
Salient object cutout using Google images

Given any image input by users, how to automatically cutout the object-of-interest is a challenging problem due to lack of information of the object-of-interest and the background. Saliency detection techniques are able to provide some rough information about object-of-interest since they highlight high-contrast or high attention regions or pixels. However, the generated saliency map is often noisy and directly applying it for segmentation often leads to erroneous results. Motivated by the recent progress on image co-segmentation and internet image retrieval techniques, in this paper, we propose to use the user input image for segmentation as a query image to Google Images and then employ the top returned Google images to build up the knowledge about the object-of-interest in the user input image. Particularly, we develop a lightweight algorithm to learn the knowledge of the object-of-interest in the retrieved images to enhance the saliency map of the input image. Then, the enhanced saliency map is used to initialize the graph-cut to extract the object-of-interest. Experiments with the Mcgill dataset and multiple challenge cases demonstrate the effectiveness of our method in terms of producing a clean cutout.
A benchmark for semantic image segmentation

Though quite a few image segmentation benchmark datasets have been constructed, there is no suitable benchmark for semantic image segmentation. In this paper, we construct a benchmark for such a purpose, where the ground-truths are generated by leveraging the existing fine granular ground-truths in Berkeley Segmentation Dataset (BSD) as well as using an interactive segmentation tool for new images. We also propose a percept-tree-based region merging strategy for dynamically adapting the ground-truth for evaluating test segmentation. Moreover, we propose a new evaluation metric that is easy to understand and compute, and does not require boundary matching. Experimental results show that, compared with BSD, the generated ground-truth dataset is more suitable for evaluating semantic image segmentation, and the conducted user study demonstrates that the proposed evaluation metric matches user ranking very well.
OVIE: Object Oriented and Vector based Image Editing

A powerful image editing system called OVIE is described, which provides fast and accurate creation, composition, rendering and other manipulation of image contents. Flexibility and convenience of the system are achieved by including two modules: image decomposition and image vectorization to understand and represent an image respectively. To understand an image comprehensively, we propose to integrate image segmentation, shape completion and image completion techniques to ensure a seamless image editing. An array of pixels is replaced by vector data with geometric edit ability for image representation since the geometrically-based editing has physical meanings and thus it is more natural or intuitive for users to edit. Compared to the existing works, our system is more convenient and can generate effects with higher quality.
Kinect-based non-intrusive human gait analysis and visualization

Home healthcare becomes more and more important with the increased aging population. The advent of various low-cost sensing devices makes it tempting to develop low-cost, non-intrusive systems to monitor the variations of human being. In this paper, we describe a Kinect-based gait analysis and visualization system as a case study in this direction. The system uses depth images and skeleton captured by the Kinect to generate a BVH file recording the motion information, extracts features of gait for detecting abnormal gait, and customizes the 3D body model for personalized motion visualization. Compared to previous work in this area, the proposed system has advantages since it integrates gait classification and visualization which may bring new possibilities in healthcare. The experiments show that our proposed system achieves accurate gait classification as well as flexible personalized 3D visualization.
Real-Time and Realistic Simulation for Cardiac Intervention with GPU

Cardiovascular disease is the number one cause of death today in developed countries and is projected to remain so. Cardiac intervention is a minimally invasive solution to diagnose and treat the disease. Training of young cardiologists is playing an important role towards quality cardiac healthcare. The advance of interactive and graphics technologies has offered a new avenue to train young cardiologists using virtual patients which can tremendously reduce the cost and risk. This paper presents a novel interactive simulation for cardiac intervention. Two-body collision between the catheter and the heart wall is detected based on a boundary representation. GPU based algorithms are developed to enhance the performance of simulator for real-time and realistic cardiac intervention.
Object-Level Image Segmentation Using Low Level Cues

This paper considers the problem of automatically segmenting an image into a small number of regions that correspond to objects conveying semantics or high-level structure. Although such object-level segmentation usually requires additional high-level knowledge or learning process, we explore what low level cues can produce for this purpose. Our idea is to construct a feature vector for each pixel, which elaborately integrates spectral attributes, color Gaussian mixture models, and geodesic distance, such that it encodes global color and spatial cues as well as global structure information. Then, we formulate the Potts variational model in terms of the feature vectors to provide a variational image segmentation algorithm that is performed in the feature space. We also propose a heuristic approach to automatically select the number of segments. The use of feature attributes enables the Potts model to produce regions that are coherent in color and position, comply with global structures corresponding to objects or parts of objects and meanwhile maintain a smooth and accurate boundary. We demonstrate the effectiveness of our algorithm against the state-of-the-art with the data set from the famous Berkeley benchmark.
Foldover-Free Mesh Warping for Constrained Texture Mapping

Mapping texture onto 3D meshes with positional constraints is a popular technique that can effectively enhance the visual realism of geometric models. Such a process usually requires constructing a valid mesh embedding satisfying a set of positional constraints, which is known to be a challenging problem. This paper presents a novel algorithm for computing a foldover-free piecewise linear mapping with exact positional constraints. The algorithm begins with an unconstrained planar embedding, followed by iterative constrained mesh transformations. At the heart of the algorithm are radial basis function (RBF)-based warping and the longest edge bisection (LEB)-based refinement. A delicate integration of the RBF-based warping and the LEB-based refinement provides a provably-foldover-free, smooth constrained mesh warping, which can handle a large number of constraints and output a visually pleasing mapping result without extra smoothing optimization. The experiments demonstrate the effectiveness of the proposed algorithm.
TV-L1 Optimization for B-Spline Surface Reconstruction with Sharp Features

The placement of knot vector and the determination of control points are two fundamental issues in B-spline surface reconstruction. This paper presents a variational approach to construct B-spline surfaces from a set of data points. The approach finds the optimal placement of knots and control points simultaneously while most previous methods determine the knots heuristically or in a separate step. Moreover, different from most previous methods using least squares metric, our approach adapts L_1-norm with total variation (TV) as regularization in the fitting procedure, which enables the approach to handle both Gaussian noise and outliers in the same manner and is able to automatically optimize the placement of knot vector to faithfully reconstruct the sharp features. A numerical solver based on the augmented Lagrangian method is also proposed in the paper to efficiently solve the TV-L_1 optimization. Experimental results demonstrate the effectiveness and efficiency of the proposed variational B-spline surface reconstruction.
Gamut fitting for image composition applications

Existing digital image composition algorithms neglect the out-of-gamut problem, i.e. some pixel values in a composited image exceed the displayable or printable range. In this paper, we show that the commonly used solution, i.e. hard clipping or linear scaling, results in either detail loss or global contrast reduction. Directly applying the existing high dynamic range (HDR) compression algorithms cannot achieve pleasant visual quality either. Thus, we propose a gamut fitting method to solve this out-of-gamut problem in image composition. In particular, we formulate gamut fitting as a multi-criteria optimization problem. Polynomials in the Bernstein-Bézier form are used to compute the optimal gamut mapping curve. The proposed approach can be regarded as a post-processing procedure to enhance the visual quality of the resulting images from image composition applications.
Inversion Free and Topology Compatible Tetrahedral Mesh Warping Driven by Boundary Surface Deformation

Warping a tetrahedral mesh driven by boundary surface deformation is useful in many applications. Although some methods have been developed to transform the mesh to conform to the deformed boundary surface, it is still a challenging problem to construct an inversion free warped mesh maintaining a compatible topology. In this paper, these two problems are solved by a novel method that combines radial basis function (RBF)-based warping and adaptive mesh refinement. We iteratively transform the mesh using RBF-based warping with a safe step size to ensure that no element is inverted. The use of the RBF-based warping ensures a smooth warping and thus generates a high-quality warped volumetric mesh. To avoid too small step sizes, we refine the elements that are potentially inverted. The refinement is performed on the original and the warped meshes in the same way so as to maintain compatible topology between them. The results of our method can be used in many areas such as finite element simulation and shape interpolation. We demonstrate the effectiveness of our method with a set of examples.
Representing Images Using Curvilinear Feature Driven Subdivision Surfaces

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.
Design for an embedded SCADA system

An embedded SCADA system has been studied and fulfilled in order to operate and control industry objects directly at the terminal of production line. A solution is suggested for an embedded SCADA system based on the development of embedded computer and network communication. The design and implementation methods are introduced for technics structure, hardware and software of this system. The first 10 units have been brought into use at power plants and power monitor centers since 2010. The result of site acceptance indicates that measuring precision and reliability follow correlative standards.