nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Fine-Granular Scalable Coding of 3D Mesh Sequences

An efficient fine-granular scalable coding algorithm of 3-D mesh sequences for low-latency streaming applications is proposed in this work. First, we decompose a mesh sequence into spatial and temporal layers to support scalable decoding. To support the finest-granular spatial scalability, we decimate only a single vertex at each layer to obtain the next layer. Then, we predict the coordinates of decimated vertices spatially and temporally based on a hierarchical prediction structure. Last, we quantize and transmit the spatio-temporal prediction residuals using an arithmetic coder. We propose an efficient context model for the arithmetic coding. Experiment results show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting finer-granular spatial scalability.
An efficient inter-frame coding with intra skip decision in H.264/AVC

In this paper, we propose a simple but effective intra-mode skip algorithm to reduce the computational cost for inter-frame coding. It makes use of motion, temporal, and spatial homogeneity characteristics of video sequences. Specifically, the motion homogeneity is defined by using the mean deviation of motion vectors (MV) of the 4×4 blocks. The temporal and spatial homogeneity are computed by the sum of absolute difference (SAD) between an original block and its prediction block of the best inter-mode and the intra-mode with large block size, respectively. Based on the three types of homogeneity, the proposed method skip the full intra-mode search for inter-frame coding to reduce the encoding time when a region has the motion homogeneity and temporal homogeneity is stronger than the spatial one. Experimental results demonstrate that the proposed algorithm significantly increases the skip decision accuracy by up to 20% and reduces the total encoding time by about 3~10% as compared to the existing methods with negligible loss in PSNR and small increment of bit rate.
Motion-Compensated Frame Interpolation Based on Multihypothesis Motion Estimation and Texture Optimization

A novel motion-compensated frame interpolation (MCFI) algorithm to increase video temporal resolutions based on multihypothesis motion estimation and texture optimization is proposed in this paper. Initially, we form multiple motion hypotheses for each pixel by employing different motion estimation parameters, i.e., different block sizes and directions. Then, we determine the best motion hypothesis for each pixel by solving a labeling problem and optimizing the parameters. In the labeling problem, the cost function is composed of color, shape, and smoothness terms. Finally, we refine the motion hypothesis field based on the texture optimization technique and blend multiple source pixels to interpolate each pixel in the intermediate frame. Simulation results demonstrate that the proposed algorithm provides significantly better MCFI performance than conventional algorithms.
Gradient domain contrast enhancement with histogram-guided boundary conditions

A novel contrast enhancement algorithm with histogram-guided boundary conditions is proposed in this work. The proposed algorithm enhances details in local regions by boosting gradient components, while improving the overall contrast by imposing boundary conditions based on a global transformation function. Moreover, we develop an efficient masking scheme, called the soft masking, to strike the balance between the global enhancement and the local enhancement. Simulation results demonstrate that the proposed algorithm can yield high quality output images by improving both global and local contrast simultaneously.
Multi-hypothesis error concealment algorithm for H.26L video

In this work, we propose a multi-hypothesis error concealment algorithm, which replaces a lost block with a weighted superposition of more than two reference blocks in previous frames. Three methods are developed to find the set of reference blocks and determine the weighting coefficients. These methods are implemented based on H.26L standard, and their performances are evaluated. It is shown that the proposed multi-hypothesis algorithm provides up to 1.5 dB better performance than the conventional single-hypothesis concealment algorithm.
Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k+l) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k+l, and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.
Robust video stabilization based on mesh grid warping of rolling-free features

A robust video stabilization algorithm, which reduces shaky camera movements and rolling-shutter distortions in video sequences, is proposed in this work. We first extract feature trajectories through a video sequence, and then transform the feature positions into rolling-free smoothed positions. Then, we set a mesh grid on each frame and warp each grid cell by matching the original features to the smoothed ones. For robust warping, we formulate a cost function based on the confidence of each feature and the reliability of each grid cell. The cost function consists of a data term, a structure-preserving term, and a regularization term. By minimizing the cost function, we find the optimal grid positions in the warped frame and transform each grid cell accordingly. Experimental results show that the proposed algorithm stabilizes videos and removes rolling-shutter distortions more efficiently than conventional algorithms.
Primary Object Segmentation in Videos Based on Region Augmentation and Reduction

A novel algorithm to segment a primary object in a video sequence is proposed in this work. First, we generate candidate regions for the primary object using both color and motion edges. Second, we estimate initial primary object regions, by exploiting the recurrence property of the primary object. Third, we augment the initial regions with missing parts or reducing them by excluding noisy parts repeatedly. This augmentation and reduction process (ARP) identifies the primary object region in each frame. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art conventional algorithms on recent benchmark datasets.
Multiscale Feature Extractors for Stereo Matching Cost Computation

We propose four efficient feature extractors based on convolutional neural networks for stereo matching cost computation. Two of them generate multiscale features with diverse receptive field sizes. These multiscale features are used to compute the corresponding multiscale matching costs. We then determine an optimal cost by combining the multiscale costs using edge information. On the other hand, the other two feature extractors produce uni-scale features by combining multiscale features directly through fully connected layers. Finally, after obtaining matching costs using one of the four extractors, we determine optimal disparities based on the cross-based cost aggregation and the semiglobal matching. Extensive experiments on the Middlebury stereo data sets demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, the proposed algorithm provides competitive matching performance with the state of the arts, while demanding lower computational complexity.
An object inpainting algorithm for multi-view video sequences

An inpainting algorithm for multi-view video sequences is proposed in this work. First, we set a mask region in a view with manual interactions, and find the corresponding masks in the other views using the mask cloning method. Then, we overlay grid points on the mask region in the target view frame. For each grid rectangle, we find the matching patch in the source view frame. Finally, we transform and paste the patches to the target frame. Simulation results illustrate that the proposed algorithm removes objects in the multi-view sequences effectively by exploiting inter-view redundancies.
Temporally x real-time video dehazing

A real-time video dehazing algorithm, which reduces flickering artifacts and yields high quality output videos, is proposed in this work. Assuming that a scene point yields highly correlated transmission values between adjacent image frames, we develop the temporal coherence cost. Then, we add the temporal coherence cost to the contrast cost and the truncation loss cost to define the overall cost function. By minimizing the overall cost function, we obtain the optimal transmission. Moreover, to reduce the computational complexity and facilitate real-time applications, we approximate the conventional edge preserving filter by the overlapped block filter. Experimental results demonstrate that the proposed algorithm is sufficiently fast for real-time applications and effectively removes haze and flickering artifacts.
Video Fingerprinting Based on Frame Skipping

A video fingerprinting algorithm based on frame skipping is proposed in this work. At the encoder side, we skip video frames according to a pattern, specified by a fingerprint. The skipping pattern is designed such that the degradation of video quality is negligible. At the decoder side, the fingerprinted sequence is compared with the original sequence and the skipping pattern is reconstructed. Then, the watermark codeword is decoded from the skipping pattern. Since the selection of skipped frames is a combinatorial problem, the proposed algorithm can provide a large capacity for embedded information. Also, the proposed algorithm is inherently robust against spatial attacks, such as geometrical attacks and spatial filtering attacks. To improve the robustness against temporal attacks, such as frame rate reduction, we introduce the notion of error correction coding into the watermarking embedding. Experimental results show that the proposed algorithm is very robust against various attacks, while satisfying the transparency of embedded watermarks.
Histogram-Based stereo matching under varying illumination conditions

A histogram-based matching algorithm for stereo images captured under different illumination conditions is proposed in this work. The cumulative histogram of an image represents the ranks of relative pixel brightness, which are robust to illumination changes. Therefore, we design the matching cost based on the similarity of the cumulative histograms of stereo images. As an optional mode, the proposed algorithm can evaluate the histograms for foreground objects and the background separately to alleviate occlusion artifacts. To determine the disparity of each pixel, the proposed algorithm adaptively aggregates matching costs based on the color similarity and the geometric proximity of neighboring pixels. Then, it refines false disparities at occluded pixels using more reliable disparities of non-occluded pixels. Experimental results demonstrate that the proposed algorithm provides higher quality disparity maps than the conventional methods under varying illumination conditions.
An efficient coding algorithm for color and normal data of three-dimensional mesh models

Three-dimensional (3D) mesh models have attribute data, such as colors, normal vectors, and texture coordinates to render or shade the surface of the mesh. Although several coding schemes have been developed to represent the topology and geometry information of the 3D mesh, coding of the attribute data has received less attention. In this paper, we propose a new predictive coding scheme for colors and normal vectors of the 3D mesh model, where we predict colors and normals based on several ancestors along the vertex ordering. In order to encode the color information, we define a mapping table that specifies how colors are mapped into other vertices. The mapping table can represent frequently occurring color patterns efficiently. For normal vectors, we also propose an average predictor and the 6-4 subdivision quantizer in the spherical coordinate system. The proposed scheme has demonstrated good coding efficiency for various VRML test data.
Rate estimation for H.264/AVC spatial resolution reduction

The emergence of wireless networks has posed new challenges for multimedia content providers. In particular, many existing video contents have bit-rates and resolutions far exceeding the capability of wireless networks and mobile devices and they need to be adapted before transmission. In this paper, we propose a technique using /spl rho/-domain information to estimate the bit rate of down-sampled video in the H.264/AVC format. The bit rate is estimated in two stages. In the initialization stage, we use a low-complexity algorithm to estimate the /spl rho/-domain parameters for half and quarter resolutions, respectively. In the refinement stage, the estimation error is feed-forwarded to improve the estimation accuracy. Experimental results show that the proposed technique can estimate the bit rate accurately at a wide range of down-sampling ratios.
RGB-D image segmentation based on multiple random walkers

A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve image segmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then, we segment an input RGB-D image by employing MRW simulation. Specifically, we decide the initial probability distribution of agents so that they are far from each other. We then execute the MRW process with the repulsive restarting rule, which makes the agents repel one another and occupy their own exclusive regions. Experimental results show that the proposed MRW image segmentation algorithm provides competitive segmentation performances, as compared with the conventional state-of-the-art algorithms.
A Learning Ontology Method for RESTful Semantic Web Services

The growing number of Restful web services available on the web raises a challenging search problem as to how the desired web services should be located. Traditional keyword searching is inaccurate, and its limitations have been noted for several years. We propose a combination method of WADL and a learning ontology mechanism to enable Restful semantic web services. These syntactic and semantic descriptions allow search engines to support a similarity search for Restful web services. We describe an experimental study on a collection of 168 Restful web services. The experimental results show that our method has higher performance in terms both of the rate of recall and precision compared to existing methods.
Contour-Constrained Superpixels for Image and Video Processing

A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour constraint. Furthermore, we extend the CCS algorithm to generate temporal superpixels for video processing. We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours. Experimental results demonstrate that the proposed algorithm provides better performance than the state-of-the-art superpixel methods.
A novel NMOS transistor for high performance ESD protection devices in 0.18 /spl mu/m CMOS technology utilizing salicide process

The electrostatic discharge (ESD) threshold of fully salicided grounded-gate NMOS transistors (ggNMOSTs) and partially salicided ggNMOSTs consisting of dummy-gate and N-well resistor was studied by transmission line pulse (TLP) I-V curves, and HBM and machine model (MM) robustness. The state-of-the-art 0.18 /spl mu/m cobalt salicide CMOS process is used, and the thickness of the gate dielectric material is 35 /spl Aring/. Fully salicided ggNMOSTs have much lower values of second breakdown current (It2) than partially salicided ggNMOSTs, and with multi-finger structures, only partially salicided ggNMOSTs turn on uniformly. Using these partially salicided NMOSTs as protection devices, we acquired ESD immunity of >2 kV (HBM) and >200 V (MM).
Online Video Object Segmentation via Convolutional Trident Network

A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, is proposed in this work. We propagate the segmentation labels at the previous frame to the current frame using optical flow vectors. However, the propagation is error-prone. Therefore, we develop the convolutional trident network (CTN), which has three decoding branches: separative, definite foreground, and definite background decoders. Then, we perform Markov random field optimization based on outputs of the three decoders. We sequentially carry out these processes from the second to the last frames to extract a segment track of the target object. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art conventional algorithms on the DAVIS benchmark dataset.
Fabrication of Au-decorated 3D ZnO nanostructures as recyclable SERS substrates

Highly roughened Au-decorated 3D ZnO nanostructures were prepared using a combination of prism holographic lithography and atomic layer deposition techniques. The optimized SERS intensity from the prepared Au-coated 3D ZnO inverse structures was twenty times the intensity obtained from an Au-coated flat glass control substrate. The surfaces could be reused after the photocatalytic degradation and removal of adsorbates in the presence of ZnO. The Au-coated 3D ZnO structures described here offer an alternative to traditional single-use SERS substrates.
Lossless compression of 3-D point data in QSplat representation

We propose a lossless compression algorithm for three-dimensional point data in graphics applications. In typical point representation, each point is treated as a sphere and its geometrical and normal data are stored in the hierarchical structure of bounding spheres. The proposed algorithm sorts child spheres according to their positions to achieve a higher coding gain for geometrical data. Also, the proposed algorithm compactly encodes normal data by exploiting high correlation between parent and child normals. Simulation results show that the proposed algorithm saves up to 60% of storage space.
Construction of regular 3D point clouds using octree partitioning and resampling

We propose a construction algorithm of regular 3D point clouds from irregular ones. An irregular point cloud is partitioned hierarchically using an octree, and the points in each octree node are projected onto a square face, called a base plane, of the cubic node. The geometry of the point cloud is then represented by the height fields on the base planes. The height fields are interpolated and then resampled uniformly on the base plane. Consequently, the original geometry is represented by the scalar height fields, each of which is defined at uniform grid points on a square region. Therefore, the resulting geometry can be easily processed by conventional 2D image processing techniques.
Progressive compression of 3D dynamic mesh sequences

An algorithm to compress 3D mesh sequences for dynamic objects is proposed in this work. Given an irregular mesh sequence, we construct a semi-regular mesh structure for the first frame and then map it to the subsequent frames bused on the hierarchical motion estimation. The regular structure of the resulting mesh sequence enables us to employ the zero-tree coding scheme to compress the motion compensation residuals efficiently. Simulation results demonstrate that the proposed algorithm provides significantly better compression performance than the static coder that encodes each frame independently.
Adaptive image and video retargeting technique based on Fourier analysis

An adaptive image and video retargeting algorithm based on Fourier analysis is proposed in this work. We first divide an input image into several strips using the gradient information so that each strip consists of textures of similar complexities. Then, we scale each strip adaptively according to its importance measure. More specifically, the distortions, generated by the scaling procedure, are formulated in the frequency domain using the Fourier transform. Then, the objective is to determine the sizes of scaled strips to minimize the sum of distortions, subject to the constraint that the sum of their sizes should equal the size of the target output image. We solve this constrained optimization problem using the Lagrangian multiplier technique. Moreover, we extend the approach to the retargeting of video sequences. Simulation results demonstrate that the proposed algorithm provides reliable retargeting performance efficiently.
Fully spray-coated inverted organic solar cells

A spray deposition method was adapted to fabricate inverted organic solar cells (IOSCs) for a low cost production. The spray-coating process was used to deposit a highly transparent sol-gel solution derived ZnO layer, P3HT:PCBM photoactive layer, and PEDOT:PSS layer. The IOSCs fabricated by fully spray-coating method shows similar performances with that of spin-coating method. In the spray coating process, the power conversion efficiency (PCE) was obtained 2.95 % at an effective photocurrent generated area of 0.38 cm2 under AM.1.5 simulated illumination. Comparing to spin-coating process, spray-coating method can be a promising substitution for the next-generation of OSCs.
Air-stable inverted organic solar cells using TiO2 layer deposited by atomic layer deposition

We investigated the air-stable and highly efficient inverted organic solar cells (IOSCs) using TiO2 as electron selective layer prepared by an atomic layer deposition (ALD) method. Various thicknesses of TiO2 layer from 10 to 50 nm were adapted to confirm the thickness effect on device performance and stability. The IOSC with 20-nm-thick TiO2 layer showed the PCE of 3.01 % under AM 1.5 simulated illumination. Furthermore, the IOSCs showed better stability retaining over 80 % of the maximum PCE after 30 days. This suggests that IOSCs with TiO2 layer deposited by ALD gives a promising way for fabrication of air-stable organic solar cells.