nils-herrmann/clean_scirepeval huggingface dataset:
HNIP: Compact Deep Invariant Representations for Video Matching, Localization, and Retrieval

With emerging demand for large-scale video analysis, MPEG initiated the compact descriptor for video analysis (CDVA) standardization in 2014. Beyond handcrafted descriptors adopted by the current MPEG-CDVA reference model, we study the problem of deep learned global descriptors for video matching, localization, and retrieval. First, inspired by a recent invariance theory, we propose a nested invariance pooling (NIP) method to derive compact deep global descriptors from convolutional neural networks (CNNs), by progressively encoding translation, scale, and rotation invariances into the pooled descriptors. Second, our empirical studies have shown that a sequence of well designed pooling moments (e.g., max or average) may drastically impact video matching performance, which motivates us to design hybrid pooling operations via NIP (HNIP). HNIP has further improved the discriminability of deep global descriptors. Third, the technical merits and performance improvements by combining deep and handcrafted descriptors are provided to better investigate the complementary effects. We evaluate the effectiveness of HNIP within the well-established MPEG-CDVA evaluation framework. The extensive experiments have demonstrated that HNIP outperforms the state-of-the-art deep and canonical handcrafted descriptors with significant mAP gains of 5.5% and 4.7%, respectively. In particular the combination of HNIP incorporated CNN descriptors and handcrafted global descriptors has significantly boosted the performance of CDVA core techniques with comparable descriptor size.
An implemented architecture of deblocking filter for H.264/AVC

H.264/AVC is a new international standard for the compression of natural video images, in which a deblocking filter has been adopted to remove blocking artifacts. We propose an efficient processing order for the deblocking filter, and present the VLSI architecture according to the order. Making good use of data dependence between neighboring 4/spl times/4 blocks, our design reduces the requirement of on-chip SRAM bandwidth and increases the throughput of the filter processing. The architecture has been described in Verilog HDL, simulated with VCS and synthesized using 0.25 /spl mu/m CMOS cells library by Synopsys Design Compiler. The circuit costs about 24k logic gates (not including a 32/spl times/64 SRAM and two 32/spl times/96 SRAMs) when the working frequency is set to 100 MHz. This design can support real-time deblocking of HDTV (1280/spl times/720, 60 fps) H.264/AVC video. This architecture is valuable for the hardware design of an H.264/AVC codec.
Adaptive discriminant analysis for face recognition from single sample per person

Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem.
A new controller design for a synchronous generator-based variable-speed wind turbine

This paper presents a control scheme for full-scale back to back converter used for connecting the synchronous generator-based variable speed wind turbine to grid. The new control concept is based on the q-d reference modulation indices of the Discontinuous Pulse Width Modulation (DPWM) technique. The converter consists of a six-diode rectifier and a six-IGBT voltage source inverter with a dc-link in between. Modeling of the whole system is presented in PSCAD/EMTDC software and steady-state and fault conditions of the system are investigated. Results show that the controller can appropriately control the converter of the wind turbine system in steady-state and fault conditions.
Background Subtraction via generalized fused lasso foreground modeling

Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts.
A control and readout circuit with capacitive mismatch auto-compensation for MEMS vibratory gyroscope

In this paper, a control and readout circuit for MEMS vibratory gyroscope is described, including closed- loop driving axis and open-loop sensing axis. Capacitive mismatch auto-compensation has been implemented in this system to suppress the influence to the output due to the mismatch of gyroscope capacitors. The ASIC is fabricated in a 0.35um CMOS process. The test of the ASIC is performed with a MEMS vibratory gyroscope. The test result shows that the non-linearity is less than 0.1% within angular velocity range of -300°/s to 300°/s.
Agent-based distributed simulation

Simulating a large-scale power system with detailed modeling of the components creates a heavy computational burden. One possible way to relieve the computational burden is to decouple the network into subsystems and solve the subsystem independently, and then combine the results of the subsystems to get the solution. The way to decouple network and represent the missing parts will affect the result greatly. The equivalent for other networks needs to be constructed to allow power system analysis. In addition with more intelligent devices being introduced, simulation of a power system with intelligent control is even more difficult. In this paper, an agent based distributed simulation is proposed to handle the issues above. An agent based distributed simulation achieves an overall system solution through separately and concurrently computing subsystems with the agents' communications over the network. A history of distributed simulation and agent techniques is briefly introduced. A generalized three-phase coupling method dealing with natural coupling is proposed. Both natural coupling and signal level coupling agent-based models are developed and demonstrated in virtual test bed (VTB). The models are tested with a simple case and the test results are presented and analyzed
Granularity-tunable gradients partition (GGP) descriptors for human detection

This paper proposes a novel descriptor, granularity-tunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular uncertainty of the line segments in the Hough space. Then this uncertainty is backprojected into the image space by orientation-space partitioning to achieve efficient implementation. By changing the granularity parameter, the level of uncertainty can be controlled quantitatively. Therefore a family of descriptors with versatile representation property can be generated. Specifically, the finely granular GGP descriptors can represent the specific geometry information of the object (the same as Edgelet); while the coarsely granular GGP descriptors can provide the statistical representation of the object (the same as histograms of oriented gradients, HOG). Moreover, the position, orientation, strength and distribution of the gradients are embedded into a unified descriptor to further improve the GGP's representation power. A cascade structured classifier is built by boosting the linear regression functions. Experimental results on INRIA dataset show that the proposed method achieves comparable results to those of the state-of-the-art methods.
Learning multiple codebooks for low bit rate mobile visual search

Compressing a query image's signature via vocabulary coding is an effective approach to low bit rate mobile visual search. State-of-the-art methods concentrate on offline learning a codebook from an initial large vocabulary. Over a large heterogeneous reference database, learning a single codebook may not suffice for maximally removing redundant codewords for vocabulary based compact descriptor. In this paper, we propose to learn multiple codebooks (m-Codebooks) for extremely compressing image signatures. A query-specific codebook (q-Codebook) is online generated at both client and server sides by adaptively weighting the off-line learned multiple codebooks. The q-Codebook is subsequently employed to quantize the query image for producing compact, discriminative, and scalable descriptors. As q-Codebook may be simultaneously generated at both sides, without transmitting the entire vocabulary, only small overhead (e.g. codebook ID and codeword 0/1 index) is incurred to reconstruct the query signature at the server end. To fulfill m-Codebooks and q-Codebook, we adopt a Bi-layer Sparse Coding method to learn the sparse relationships of codewords vs. codebooks as well as codebooks vs. query images via l1 regularization. Experiments on benchmarking datasets have demonstrated the extremely small descriptor's supervior performance in image retrieval.
A robust text detection algorithm in images and video frames

In this paper, an algorithm for detecting text in images and video frames is proposed. The algorithm contains two steps: initial detection and verification. In the first step, edge feature and morphology operation are employed to locate edge-dense image blocks. Empirically rules are applied on these blocks to get candidate text. In the second step, wavelet-based features are employed to represent the texture property of text. A SVM classifier is used to identify text from the candidate ones. Experiments show that this algorithm has 93.9% detection rate for English text and a 92.4% detection rate for Chinese text. The algorithm is robust to language, font-color and size.
Matching images more efficiently with local descriptors

Image matching is a fundamental task for many applications of computer vision. Today it is very popular to represent two matched images as two bags of local descriptors, and the classic RANSAC based matching procedure is always exploited in the task. In this paper, we present a much efficient image matching approach based on sets of any local descriptors. A block-to-block strategy is devised to speed up the establishment of local correspondences. Additionally, the weighted RANSAC (w-RANSAC) technique is proposed to make the search of optimal global models converge faster. Comparative experiments with the RANSAC based paradigm show our approach can not only generate more accurate correspondences, but also double the matching speed.
Non-Local Structure-Based Filter for Video Coding

The deblocking filtering (DF) in HEVC is only applied to the boundaries between the coding units, prediction units, or transform units, which actually exists two issues. On one hand, the simple DF in HEVC does not fully exploit structure information in video. On the other hand, DF in HEVC does not consider the inside areas, which often suffers from quantization distortion. To alleviate the above issues, in this paper, a non-local structure-based filter (NLSF) is proposed by simultaneously enforcing the intrinsic local sparsity and the non-local self-similarity of each frame in video. NLSF not only deals with the boundaries, but also deals with the inside areas, which is able to effectively reduce the artifacts while enhance the quality of the deblocking frames. Experimental results demonstrate that, compared with the original HEVC reference encoder implementation in AI configuration, the proposed NLSF can achieve up to 7.3% BD-rate saving by substituting for DF in HEVC.
Merge Mode for Deformable Block Motion Information Derivation

Motion estimation (ME) plays an important role in most video coding standards since it can improve the coding performance significantly. Block matching ME (BMME) based on translation motion model is employed in both H.264/Advanced Video Coding and High Efficiency Video Coding (HEVC), which assumes that all the pixels in a block undergo the same motion. However, BMME cannot represent the complex motion accurately, such as rotation, zoom, and deformation motion. To resolve the problem, a merge mode for deformable block motion information derivation is proposed, which considers bilinear interpolation model, six-parameter affine model, and four-parameter affine model simultaneously. The sum of absolute transformed differences criterion is used to select the best model for a block. In the proposed merge mode, the motion information of the current block is derived from the motion information of its neighbors. It does not need to perform ME and transmit motion parameters. The proposed merge mode is integrated into HEVC test model (HM) 14.0. The simulation results show that compared with HM14.0, the proposed merge mode can bring 1.3%, 1.7%, and 2.7% Bjøntegaard delta (BD) bitrate savings on average for test sequences of HEVC, and 11.0%, 15.2%, and 17.4% BD bitrate savings for deformation sequences in random access main, low-delay B main, and low-delay P main test conditions, respectively, while the increased encoding and decoding complexities are moderate.
An approach to robust speaker recognition using stochastic matching

This paper describes an approach to robust speaker recognition using stochastic matching for depressing channel mismatch in telephone networks. For the two recognition algorithms such as GMM (Gaussian mixture models) for text-independent speaker identification and HMM for text prompted speaker verification, the corresponding stochastic matching approaches are presented in detail. Experimental results have shown that the stochastic matching approach for channel compensation based on the compensation term from GMM is better than that of the compensation term from a single Gaussian model.
Nonparametric Background Generation

A novel background generation method based on non-parametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. Based on ECD, an effective background generation method, most reliable background mode (MRBM), is developed. The basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. The advantages of this method are three-fold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. Extensive experimental results illustrate its good performance
A proposed AVS decoder configuration in the reconfigurable video coding framework

This demonstration shows an AVS intra decoder configuration in the RVC framework. It explains how to use the dataflow mechanism offered by the RVC framework to support AVS decoder configuration. It also shows the flexibility and convenience to reconfigure decoders in the RVC framework. In this work, the AVS VTL is established containing FUs from AVS. The proposed AVS decoder configuration is implemented by connecting some FUs from AVS VTL and reusing some FUs from MPEG VTL. The demonstration shows that the decoder can decode AVS conformance bitstreams correctly in RVC simulator.
Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets

This study presents mixed integer non-linear programming (MINLP) approach for determining optimal location and number of distributed generators in hybrid electricity market. For optimal location of distributed generation (DG), first the most appropriate zone has been identified based on real power nodal price and real power loss sensitivity index as an economic and operational criterion. After identifying the suitable zone, mixed integer non-linear programming approach has been applied to locate optimal place and number of distributed generators in the obtained zone. The non-linear optimisation approach consists of minimisation of total fuel cost of conventional and DG sources as well as minimisation of line losses in the network. The pattern of nodal real and reactive power prices, line loss reduction and fuel cost saving has been obtained. The results have also been obtained for pool electricity market model for comparison. The impact of demand variation on the results has also been obtained for both the market models. The proposed MINLP-based optimisation approach has been applied for IEEE 24 bus reliability test system.
A new control strategy for stand-alone fuel cell-battery hybrid power supply system

In this paper, a new control strategy is proposed for a stand-alone fuel cell-battery hybrid power supply system. This new control strategy can both meet the high peak power load demand and preserve a high energy density and thus ensures a high quality power supply. The fuel cell output current is adjusted according to state of charge and voltage of the battery. The battery is charged in a proper way when state of charge is below a chosen limit. Furthermore the new strategy can mitigate the load transient to ensure a long life time for fuel cell. The control strategy is implemented in Visual Simulation (Vissim™), together with a dynamic proton exchange membrane fuel cell (PEMFC) model and a lead acid battery model, and tested under different conditions. The simulation results demonstrate that the control strategy is valid and can meet the load demands.
An Empirical Research on Comprehensive Assessment of Goverment Websites

The aim of this paper is to develop comprehensive evaluation criteria for government websites and to apply it to a sample. Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and ignorance of the relationship among evaluating objects, a new weight evaluation process using an analytic hierarchy process (AHP) approach was introduced. This improved method for determination of weight of the evaluating indicators was applied in three websites assessment of China government entities. Results showed that it is important for websites to provide orientation information, and that website style be properly adapted to the government web environment and facilities be made available to users, and that website security and privacy concerns be addressed. Compared with the original evaluation method, the method was favorable for fuzzy synthetic evaluation when there were more than one evaluating objects and the evaluation results were more reasonable. The findings provide useful information to website assessment for improving e-government.
Multi-stage motion vector prediction schedule strategy for AVS HD encoder

Motion vector prediction (MVP) is a high-efficiency technology for motion estimation in video compressing. In this paper, a novel MVP schedule strategy is proposed for AVS HD real-time encoder. A multi-stage MVP architecture is designed in this strategy for the macroblock (MB)-based pipelining. The tradeoff between complexity and performance is achieved for the MVP architecture which breaks the data dependencies in the spatial MVP which is harmful for normal pipeline rhythm. The experimental results show that the proposed MVP schedule strategy meets the real-time requirement of AVS HD encoder with an acceptable coding performance loss.
Just Noticeable Difference Estimation for Screen Content Images

We propose a novel just noticeable difference (JND) model for a screen content image (SCI). The distinct properties of the SCI result in different behaviors of the human visual system when viewing the textual content, which motivate us to employ a local parametric edge model with an adaptive representation of the edge profile in JND modeling. In particular, we decompose each edge profile into its luminance, contrast, and structure, and then evaluate the visibility threshold in different ways. The edge luminance adaptation, contrast masking, and structural distortion sensitivity are studied in subjective experiments, and the final JND model is established based on the edge profile reconstruction with tolerable variations. Extensive experiments are conducted to verify the proposed JND model, which confirm that it is accurate in predicting the JND profile, and outperforms the state-of-the-art schemes in terms of the distortion masking ability. Furthermore, we explore the applicability of the proposed JND model in the scenario of perceptually lossless SCI compression, and experimental results show that the proposed scheme can outperform the conventional JND guided compression schemes by providing better visual quality at the same coding bits.
Complexity-Distortion Optimized Motion Estimation Algorithm with Fine-Granular Scalable Complexity

Video encoding now is being implemented in various computing platforms with different computing capability, the requirement on the encoding complexity is also different according to different applications. As the most computation-intensive part of video encoding, the ME (motion estimation) should have a scalable complexity. This paper proposes a ME algorithm with fine-granular scalable complexity, a more important feature of the proposed algorithm is that it seeks for the complexity-distortion optimization. The given computation budget will be allocated to each MB (macroblock) in one frame. Each MB will consume its allocated computation by a hybrid search pattern. Experimental results show that the proposed algorithm can get a better computation-distortion performance than the existing ME algorithms
Multiple kernel active learning for image classification

Recently, multiple kernel learning (MKL) methods have shown promising performance in image classification. As a sort of supervised learning, training MKL-based classifiers relies on selecting and annotating extensive dataset. In general, we have to manually label large amount of samples to achieve desirable MKL-based classifiers. Moreover, MKL also suffers a great computational cost on kernel computation and parameter optimization. In this paper, we propose a local adaptive active learning (LA-AL) method to reduce the labeling and computational cost by selecting the most informative training samples. LA-AL adopts a top-down (or global-local) strategy for locating and searching informative samples. Uncertain samples are first clustered into groups, and then informative samples are consequently selected via inter-group and intra-group competitions. Experiments over COREL-5K show that the proposed LA-AL method can significantly reduce the demand of sample labeling and have achieved the state-of-the-art performance.