nils-herrmann/clean_scirepeval huggingface dataset:
Recovering Surface Details under General Unknown Illumination Using Shading and Coarse Multi-view Stereo

Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision and high quality reconstruction is usually challenging especially when high detail is needed. This paper presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are our two important observations: (1) the illumination over the surface of an object tends to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing geometry, which were previously overlooked. Thus we introduce TV to regularize the lighting and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TV minimization problem that treats the shape and lighting as unknowns simultaneously. An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem. As a result, our approach is robust, stable and is able to efficiently recover high quality of surface details even starting with a coarse MVS. These advantages are demonstrated by the experiments with synthetic and real world examples.
Locating Facial Landmarks Using Probabilistic Random Forest

Random forest is a useful tool for face alignment/tracking. The method of regressing local binary features learned from random forest has achieved state-of-the-art performance both in fitting accuracy and speed. Despite the great success of this method, it has certain weaknesses: the number of available local binary features is rather limited and is not optimal for face alignment; the binary features inevitably lead to serious jitter when tracking a video sequence. To address these problems, we propose learning probability features from probabilistic random forest (PRF). The proposed PRF is the same as standard random forest except that it models the probability of a sample belonging to the nodes of a tree. By using the probability features, our method significantly outperforms the state-of-the-art in terms of accuracy. It also achieves about 60 fps for locating a few facial landmarks. In addition, our method shows excellent stability in face tracking.
Euclidean and Hamming Embedding for Image Patch Description with Convolutional Networks

Local feature descriptors represent image patches as floating-point or binary arrays for computer vision tasks. In this paper, we propose to train Euclidean and Hamming embedding for image patch description with triplet convolutional networks. Thanks to the learning ability of deep ConvNets, the trained local feature generation method, which is called Deeply Learned Feature Transform (DELFT), has good distinctiveness and robustness. Evaluated on the UBC benchmark, we get the state-of-the-art results using floating-point and binary features. Also, the learned features can cooperate with existing nearest neighbor search algorithms in Euclidean and Hamming space. In addition, a new benchmark is constructed to facilitate future related research, which contains 40 million image patches, corresponding to 6.7 million 3D points, being 25 times larger than existing dataset. The distinctiveness and robustness of the proposed method are demonstrated in the experimental results.
Tianlai: A 21cm intensity mapping experiment proceedings of the XXXIst URSI general assembly and scientific symposium to be held in Beijing, China (CIE), August 17–23, 2014

The Tianlai project is a radio astronomical experiment dedicated to the study of the nature of dark energy. By observing the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum through 21cm intensity mapping, i.e. observe the intensity variation of the 21cm line emission of the neutral hydrogen at redshifts 0<;z<;3 without resolving the individual galaxies which host these neutral hydrogen atoms. The BAO features in the power spectrum can be used as standard ruler for distance measurement, by measuring it at different redshifts, the dark energy equation of state can be determined. In order to achieve high precision, it is necessary to observe large sky area with high precision, and the design of the Tianlai experiment is optimized for this. The full scale experiment requires a compact array with a geometric dimension of (100m)2, and filled with receiver elements. The Tianlai pathfinder, which is being built in a radio quiet site in Xinjiang, China, consists of 3 cylinders of 15m wide and 40 m long. The project is an international effort with participants from China, France, the USA and Canada.
Analytical model for camera distance related 3D virtual view distortion estimation

We propose an analytical model to estimate the depth-error-induced synthesis distortion in 3D video, taking into account the configuration of the cameras. In particular, the model mathematically relates the Distance between camera positions (reference view and virtual view) to the Virtual View Distortion (VVD), thus it is denoted as DVVD model. Specifically, the DVVD model accounts for two modules: distribution of disparity errors and shift-induced distortion. The former one is derived under a Laplacian distribution assumption of depth errors, and the latter one is estimated under a Quadratic model. We further propose a linear Steady-State model by performing Taylor series approximation of the DVVD model over a region of practical interest. Experiment results demonstrate that both the DVVD and Steady-State models are capable of estimating the relationship between VVD and the distance between virtual/reference view. Therefore, our model can effectively inform camera setup for capturing, in particular, the setup of the cameras in situation where depth information will be compressed subsequently.
[Demo paper] exploring attractive faces: General versus personal preferences

In this paper, we propose a novel Personality&Generality Support Vector Regression (PG-SVR) model to train the personality and generality regression attractiveness models from training facial images and their corresponding attractive scores simultaneously, which is completely different from existing method which returns only one general regression model. The trained PG-SVR serves for facial attractiveness enhancement, constructing low-dimensional reasonable solution space, which reflects the “Generality” and “Personality” attractiveness standard respectively. Experiments demonstrate that our PG-SVR enhanced face image space contains satisfactory results for different users and can be explored in real time.
Making 3D Eyeglasses Try-on practical

In this paper, we propose a virtual 3D Eyeglasses Try-on (3DET) system, with efficient, realistic and real-time augmented performance. The 3DET system captures user's performance with the help of depth camera and renders the glasses properly on the video stream immediately after user's selection. The virtual eye-glasses follow with the motion (movement or rotation) of user's head simultaneously. The high efficiency of our 3DET system is achieved by simplifying the eyeglasses matching procedure, making use of the active appearance model (AAM) based face tracking algorithm. This is completely different from existing methods, which usually relies on eye detection. In addition, due to the exploiting of a generic 3D face model during tracking and displaying, the 3DET system can handle occlusion problem easily and render realistic glasses in videos effectively. Experimental results demonstrate that the proposed 3DET system is able to produce superior natural and smooth visual results with virtual glasses fitted on the users face at 30 fps with a high level of accuracy on common hardware.
Static/Dynamic Filtering for Mesh Geometry

The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering.
Design of a wideband feed for the Tianlai project proceedings of the XXXIst URSI general assembly and scientific symposium to be held in Beijing, China (CIE), August 17–23, 2014

This paper describes the design of a wideband feed for on-axis parabolic cylinder antenna. This assembly, comprising four 4-square elements and reflective cavity, supports dual polarization. The feed is fed by a standard quarter wave balun which can provide a DC short. The current design is intended to work with the on-axis parabolic cylinder antenna from 700MHz to 1200MHz. The simulated results show that the VSWR from 700MHz to 1200MHz, stays below 2.3:1. A prototype of the proposed feed is built to validate the design experimentally, and a bandwidth of 700MHz-1200MHz is achieved.
3D Face Reconstruction With Geometry Details From a Single Image

3D face reconstruction from a single image is a classical and challenging problem with wide applications in many areas. Inspired by recent works in face animation from RGB-D or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterward, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms the state-of-the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real-world models and publicly available data sets.
Pixel-level guided face editing with fully convolution networks

Face editing has a variety of applications, especially with the increasing popularity of photography using mobile devices. In this work, we argue that the performance of face image editing can be further improved by using semantic segmentation which marks each pixel with a label that indicates its corresponding facial part. To this end, we propose a deep learning based method for automatic pixel-level labeling on face images. Our approach achieves state-of-the-art labeling accuracy on publicly available datasets, at a significantly higher speed than existing labeling methods. Then we show how the label information can be applied to various face image editing applications, such as face smoothing, face cloning and face blending. Extensive experimental results demonstrate the effectiveness of our method in editing face images with convincing visual quality.
Variational Mesh Denoising Using Total Variation and Piecewise Constant Function Space.

Mesh surface denoising is a fundamental problem in geometry processing. The main challenge is to remove noise while preserving sharp features (such as edges and corners) and preventing generating false edges. We propose in this paper to combine total variation (TV) and piecewise constant function space for variational mesh denoising. We first give definitions of piecewise constant function spaces and associated operators. A variational mesh denoising method will then be presented by combining TV and piecewise constant function space. It is proved that, the solution of the variational problem (the key part of the method) is in some sense continuously dependent on its parameter, indicating that the solution is robust to small perturbations of this parameter. To solve the variational problem, we propose an efficient iterative algorithm (with an additional algorithmic parameter) based on variable splitting and augmented Lagrangian method, each step of which has closed form solution. Our denoising method is discussed and compared to several typical existing methods in various aspects. Experimental results show that our method outperforms all the compared methods for both CAD and non-CAD meshes at reasonable costs. It can preserve different levels of features well, and prevent generating false edges in most cases, even with the parameters evaluated by our estimation formulae.
Computationally controllable integer, half, and quarter-pel motion estimator for MPEG-4 Advanced Simple Profile

A cost-effective hardware architecture of integer, half, and quarter-pel motion estimation for MPEG-4 Advanced Simple Profile is proposed in this paper. Three-step hierarchy scheme is employed to cope with different pixel accuracy. For integer-pel estimation, the proposed computation-controllable algorithm makes it easy to be integrated into the coding system according to the power, quality, and timing conditions. For half and quarter-pel motion estimation, hardware-oriented algorithm and related architecture are proposed for cost reduction and provide 1.63 to 4.81 dB improvement in PSNR quality. The implementation takes 15K gates at 54MHz for sequences of CIF format at 30 fps.
720 × 480 30fps Efficient Prediction Core Chip for Stereo Video Hybrid Coding System

The chip design of prediction core in the stereo video hybrid coding system is implemented with 0.18 mum 1P6M technology by TSMC. The die size is 4.53 mm. This IC can achieve real-time requirement under the operating frequency of 81 MHz for 30 D1 frames per second (fps) in the left and the right channel simultaneously, with ME/DE search range of [-64,  in horizontal direction and [-32, +31]/[-16, +15] in vertical direction. Compared with the hardware requirement for implementation of full search block matching algorithm (FSBMA), only 11.5% on-chip SRAM and 1/30 amount of PEs are needed. It shows that the hardware cost is quite small
Vector quantization using tree-structured self-organizing feature maps

In this paper, we propose a binary-tree structure neural network model suitable for structured clustering. During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space. This model is used to design tree search vector quantization codebooks for image coding. Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images.
Architecture design of full HD JPEG XR encoder for digital photography applications

To satisfy the high quality image compression requirement, the new JPEG XR compression standard is introduced. The analysis and architecture design with VLSI architecture of JPEG XR encoder are proposed in this paper which can encode 4:4:4 1920 times 1080 high definition photo in smooth. According to the simulation results, the throughput of the proposed design can encode 44.2 M samples/sec. This design can be used for digital photography applications to achieve low computation, low storage, and high dynamical range features.
Cache-based integer motion/disparity estimation for quad-HD H.264/AVC and HD multiview video coding

To provide more vivid perception, more and more advanced features, like the 4k×2k resolution and the multiview functionality, are emerging for TV. For a multiview video coding (MVC) encoder, motion and disparity estimation (ME/DE) take at least half the hardware requirement. To solve these challenges, a cache-based integer ME/DE algorithm is proposed. With a cache memory as the search window buffer, a predictor-centered ME/DE algorithm is presented. The search range can be reduced to ±16 pixels with less than 0.1dB quality drop compared with full search algorithm. Based on this algorithm, an integer ME/DE chip design is realized. It can reduce 82% on-chip SRAM and 39% system bandwidth. Moreover, the search candidate requirement is also reduced by 79%. As the result, an ME/DE chip design for 4k×2k quad-HD H.264 and HDTV MVC is implemented.
A 52 mW Full HD 160-Degree Object Viewpoint Recognition SoC With Visual Vocabulary Processor for Wearable Vision Applications

A 1920 × 1080 160° object viewpoint recognition system-on-chip (SoC) is presented in this paper. The SoC design is dedicated to wearable vision applications, and we address several crucial issues including the low recognition accuracy due to the use of low resolution images and dramatic changes in object viewpoints, and the high power consumption caused by the complex computations in existing computer vision object recognition systems. The human-centered design (HCD) mechanism is proposed in order to maintain a high recognition rate in difficult situations. To overcome the degradation of accuracy when dramatic changes to the object viewpoint occur, the object viewpoint prediction (OVP) engine in the HCD provides 160° object viewpoint in- variance by synthesizing various object poses from predicted object viewpoints. To achieve low power consumption, the visual vocabulary processor (VVP), which is based on bag-of-words (BoW) matching algorithm, is used to advance the matching stage from the feature-level to the object-level and results in a 97% reduction in the required memory bandwidth compared to previous recognition systems. Moreover, the matching efficiency of the VVP enables the system to support real-time full HD (1920 × 1080) processing, thereby improving the recognition rate for detecting a traffic light at a distance of 50 m to 95% compared to the 29% recognition rate for VGA (640 × 480) processing. The real-time 1920 × 1080 visual recognition chip is realized on a 6.38 mm2 die with 65 nm CMOS technology. It achieves an average recognition rate of 94%, a power efficiency of 1.18 TOPS/W, and an area efficiency of 25.9 GOPS/mm2 while only dissipating 52 mW at 1.0 V.
An 8.6mW 12.5Mvertices/s 800MOPS 8.91mm2 Stream Processor Core for Mobile Graphics and Video Applications

An 8.6 mW stream processor core for mobile applications is implemented with 8.91 mm2 area in 0.18 mum CMOS technology at 50 MHz. The adaptive multi-thread architecture with configurable memory array and geometry-content-aware technique are proposed to reduce power consumption while achieving 12.5 Mvertices/s for 3D graphics and motion estimation with search range {H[-24,24),V[-16,16]} for CIF (352times288) 30 fps video encoding.
Performance analysis and architecture evaluation of MPEG-4 video codec system

This paper presents various analyses of computational behavior. Namely, the number of datapath operations and memory access on the core profile level 2 (CPL2) of MPEG-4 video standard. These analyzed data exploit the load distribution and mode selection of the video system. The exploration of data-flow behavior and its derived computation of MPEG-4 video processing algorithms will then drive through an efficient architecture design.
Four field variable block size motion compensated adaptive de-interlacing

A four field variable block size motion compensated adaptive de-interlacing method is proposed to improve the accuracy of the motion vectors and lower the occlusions of motion compensated de-interlacing. The proposed de-interlacing method consists of variable block size motion estimation/compensation with four field SAD, interlaced block mode decision, and new block modes. The variable block size motion estimation and compensation improve the accuracy of the motion vectors, especially for spatially-periodic patterns. The new block modes and the interlaced block mode decision make block decisions more precisely, and special patterns that motion compensation cannot be compensated are correctly de-interlaced by these two methods. The subjective view shows an improvement of the accuracy of the motion vectors and the correctness of the mode decision.
A low complexity design of psycho-acoustic model for MPEG-2/4 advanced audio coding

The paper presents a new low complexity design of psycho-acoustic model (PAM), which is the key technology for MPEG-2/4 advanced audio coding (AAC) encoding. The real-time constraint of MPEG AAC leads to a heavy computational bottleneck on today's portable devices. To overcome this problem, design analysis and optimization of PAM are addressed at the algorithmic level. First, the dominant calculation of spreading function is replaced with small look-up tables. Second, modified-discrete-cosine-transform-based (MDCT-based) PAM with block type decision in frequency domain is proposed to reduce the overall AAC encoder complexity and maintain the quality. This technique is different from the traditional methods. The computational complexity of PAM is reduced by 82% in total. The proposed design could be implemented in a real-time MPEG-2/4 AAC stereo encoder at low complexity profile and bit rate 128 kb/s with computational complexity of less than 20 MOPS while maintaining CD quality.
Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme

Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy, etc.) poses a fundamental difficulty in seizure prediction problem. The traditional off-line training method trains the model using a fixed training set, and expects the performance of the model to remain stable even after a long period of time, and thus suffers from variation issue. In this paper, we propose an on-line retraining method to leverage the recent input data by gradually enlarging the training set and retraining the model. Also, a simple post-processing scheme is incorporated to reduce false alarms. We develop our method based on the state of the art machine learning based classification of bivariate patterns method. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The proposed method achieves 74.2% sensitivity on ECoG database and 52.2% sensitivity on scalp EEG database, while improving the sensitivity of off-line training method by 29.0% and 17.4% in ECoG database and EEG database respectively. The experimental result suggests that on-line retraining can greatly improve the reliability and is promising for future seizure prediction method development.