nils-herrmann/clean_scirepeval huggingface dataset:
Latest arrival time leaky bucket for HRD constrained video coding

Hypothetical reference decoder (HRD) is a mathematical model of a decoder and its input buffer. It represents a set of normative requirements on bitstream for the purpose of avoiding buffer overflow and underflow. In other words, any coded video bitstream shall meet constraints imposed by HRD model. Two HRD models, i.e. the earliest arrival time leaky bucket (EAT- LB) and the constrained arrival time leaky bucket (CAT-LB), have been proposed for the JVT standard jointly developed by ISO/IEC and ITU-T. EAT-LB has the lower initial delay, whereas the CAT-LB model has the lower maximum delay. This paper proposes an improved leaky bucket model called latest arrival time leaky bucket (LAT-LB) so as to achieve the advantages of EAT-LB and CAT-LB simultaneously. The proposed LAT-LB first defines the data transmission schedule according to a set of resume points and stop points, and then derives the HRD parameters according to this schedule. Experimental results demonstrate that the proposed LAT-LB outperforms EAT-LB and CAT-LB in terms of initial delay and maximum delay.
Low-complexity and high-efficiency background modeling for surveillance video coding

Recently, background modeling (shortly BgModeling) plays a more and more important role in high-efficiency surveillance video coding. Meanwhile, many practical video coding applications also present some specific requirements for BgModeling, such as the low memory cost and low computational complexity. However, existing BgModeling methods are mostly designed for video content analysis such as object detection. Thus they may be not directly applicable for video coding. In this paper, we firstly present an analysis for the features of BgModeling in surveillance video coding and make a comparison of the performances of existing BgModeling methods. Then we propose a segment-and-weight based running average (SWRA) method for surveillance video coding. SWRA firstly divides pixels at each position in the training frames into several temporal segments, and then calculate their corresponding mean values and weights. After that, a running and weighted average procedure is used to reduce the influence of foreground pixels and finally obtain the modeling results. Experimental results show that, the SWRA-based encoder achieves the best performance over several state-of-the-art methods, with much less cost of memory and modeling time.
Smart query expansion scheme for CDVS based on illumination and key features

Given a query image, retrieving images depicting the same object in a large scale database is becoming an urgent and challenging task. Recently, Compact Description for Visual Search (CDVS) is drafted by the ISO/IEC Moving Pictures Experts Group (MPEG) to support image retrieval applications, and it has been published as an international standard. Unfortunately, with regard to applications with hugely mutative illumination, perspective and noisy background, CDVS suffers from an inevitable performance loss. In this paper, firstly we introduce the query expansion to address performance loss caused by the scene complexity in CDVS. Secondly, a query expansion instance selection method based on illumination is proposed, which achieves better performance. Thirdly, we adopt a key feature matching score based weighted strategy in basic query expansion to improve retrieval performance. We evaluate our proposed methods on the Oxford (5K images) dataset and a reality traffic vehicle dataset (12K images), and the result shows that the proposed methods boost mean average precision (MAP) by 7% ∼ 10% in Oxford dataset and 7% ∼17% in vehicle dataset.
Sports video analysis

Techniques of computer and Internet are developing very fast, and users want to access their interested multimedia information from anywhere at anytime by using their most convenient digital equipments. Sports video always appeals to large audiences, and it becomes an important problem to automatically extracting useful semantic information from sports video to facilitate user's accessing requirements. Therefore, sports video analysis has emerged as a hot research point in multimedia area recently due to its high commercial potentials. This talk gives an introduction on technologies and prototypes of sports video analysis and enhancement that include object detection and tracking, scene analysis, event detection and highlight summarization, as well as 3D virtual sports event reconstruction. In the processing of sports video, playfield, player, ball, overlay text and jersey number are detected in the object level. Video scenes with the same semantic meaning are categorized together by using supervised classification and unsupervised clustering algorithms. Solutions of event detection and highlight ranking by using multi-model information are employed in the highlight extraction level, which is one of the most important goals in sports video analysis. 3D virtual cartoon from broadcast soccer video are generated by applying both computer vision and computer graphics techniques. These results can be used to intelligently adapt, enhance and personalize the multimedia content to meet users' preferences and network/device environments.
Adaptive generic learning for face recognition from a single sample per person

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.
Method of deformable optimum threshold for lipreading

This paper presents a new approach to the location and/or extraction of mouths during lipreading preprocessing. In other words, a new approach to making binary lip images with a deformable optimum threshold is presented. Using this approach makes the mouth object follow more closely the true shape of the mouth, and can improve the features from the image data. It is useful for improving the rate of recognition. The deformable optimum threshold technique and its application is introduced in this paper.
Capacitor mismatch auto-compensation for MEMS gyroscope differential capacitive sensing circuit

A capacitor mismatch auto-compensation circuit has been designed and implemented for MEMS gyroscope differential capacitive sensing circuit. An in-chip capacitor array that controlled by the 7-bit SAR is selected to be connected in parallel with one of the gyroscope capacitor, making the two differential capacitors of the gyroscope equal. The compensation progress only takes eight periods of the clock at the start and will be turned off afterward automatically. The chip is fabricated in a 0.35um CMOS process. The test of the chip is performed with a vibratory gyroscope on the condition of a closed-loop control in the drive mode, and the measurement shows that the minimum capacitive compensation is
Evaluation of passenger travel plan based on entropy and gray theory

In order to rank all the alternatives, basing on the systematic analysis of influencing factors of passenger travel plan selection, railway passenger travel plan evaluation system is established, using travel cost consumption, travel time consumption, travel convenience, travel fatigue and travel time satisfaction as evaluation index. In order to avoid the deviation of subjective factors, the weight of each index is determined by using the information entropy. The paper extracts optimal value of different indexes to set an ideal solution, apply the method of TOPSIS and grey relational analysis for proposed schemes and ideal solution to achieve the optimal scheduling of travel plans. Finally, the paper makes use of example of Lanzhou-Beijing travel planning to verify that, although some indexes in transfer train operation plan are better than those in the direct train operation plan, overall, the latter has greater advantage.
Interacting multiple model particle filter to adaptive visual tracking

Visual tracking could be formulated as a state estimation problem of target representation based on observations in image sequences. Approaching visual tracking problem in the Bayesian filter framework, how to sample the state evolution model to generate hypothesis of high confidence level is a critical factor. In this paper, we introduce an interacting multiple model estimation (IMME) framework for adaptive visual tracking. The essence of the IMME framework is that the state is estimated by integrating several different models in parallel and by interacting among those models' estimates probabilistically. Based on the IMME framework, we propose a new variation of particle filter named interacting multiple model particle filter (IMMPF), in which the hypotheses can be sampled from several different state evolution models adaptively. Experiments show that, when compared with the standard particle filter, the IMMPF generates better hypotheses resulting in better tracking results, especially when the target behaves along several motion modes randomly.
Sizing of battery and supercapacitor in a hybrid energy storage system for wind turbines

In this paper, we introduce a new hybrid energy storage system (HESS) design for wind power generation application and corresponding calculation of the proper size of the battery and supercapacitor. The design has an objective to increase the battery life time. The boundary levels of the state of charge of the battery and that of the supercapacitor are used in the control strategy. In the controller, some logic gates are also used to control the operating time durations of the battery. The sizing method is based on the average fluctuation of wind profiles of a specific wind station which consequently provides some fluctuations in the generated wind power. The calculated battery size is dependent on the size of the supercapacitor, state of charge of the supercapacitor and battery wear. To verify the presented method, two different cases are studied in PSCAD and corresponding results are provided.
Image Matching by Normalized Cross-Correlation

Correlation is widely used as an effective similarity measure in matching tasks. However, traditional correlation based matching methods are limited to the short baseline case. In this paper we propose a new correlation based method for matching two images with large camera motion. Our method is based on the rotation and scale invariant normalized cross-correlation. Both the size and the orientation of the correlation windows are determined according to the characteristic scale and the dominant direction of the interest points. Experimental results on real images demonstrate that the new method is effective for matching image pairs with significant rotation and scale changes as well as other common imaging conditions
Surface reflectance classifying under natural illumination

Though a point light source is more suitable to measure the BRDF of the surface, the natural illuminations in the real-world are not point light source and very complex. Fortunately, the complex natural illuminations exhibit some statistical regularity R.O. Dror, et al. (2001). These statistical properties of the natural illuminations lead to predictable image statistics for a surface with given reflectance properties. We develop an algorithm for classifying a surface according to its reflectance from a single photograph under unknown illumination by learning relationships between surface reflectance and certain features computed from the observed image. The statistics of the natural illuminations and the relationships learning are performed in frequency domain because the reflection equation is a rotational convolution and it is convenient to analyze it in space-frequency domain.
Exploring Algorithmic Limits of Matrix Rank Minimization Under Affine Constraints

Many applications require recovering a matrix of minimal rank within an affine constraint set, with matrix completion a notable special case. Because the problem is NP-hard in general, it is common to replace the matrix rank with the nuclear norm, which acts as a convenient convex surrogate. While elegant theoretical conditions elucidate when this replacement is likely to be successful, they are highly restrictive and convex algorithms fail when the ambient rank is too high or when the constraint set is poorly structured. Nonconvex alternatives fare somewhat better when carefully tuned; however, convergence to locally optimal solutions remains a continuing source of failure. Against this backdrop, we derive a deceptively simple and parameter-free probabilistic PCA-like algorithm that is capable, over a wide battery of empirical tests, of successful recovery even at the theoretical limit where the number of measurements equals the degrees of freedom in the unknown low-rank matrix. Somewhat surprisingly, this is possible even when the affine constraint set is highly ill-conditioned. While proving general recovery guarantees remains evasive for nonconvex algorithms, Bayesian-inspired or otherwise, we nonetheless show conditions whereby the underlying cost function has a unique stationary point located at the global optimum; no existing cost function we are aware of satisfies this property. The algorithm has also been successfully deployed on a computer vision application involving image rectification and a standard collaborative filtering benchmark.
Hybrid Algorithm with Adaptive Complexity for Integer Pel Motion Estimation of H. 264

Owing to the great computation complexity of the ME (motion estimation) in video coding, a lot of fast ME algorithms have been proposed in literature. Most of them are designed based on a given hypothesis about the character of the video’s motion field. But due to the inherent variety of the real-world video, these algorithms are not generally efficient, especially for the variable block size ME in H. 264, only using one single ME strategy is difficult to get satisfactory result. This paper firstly will give two different fast ME algorithms, which aim at video sequences with different character. An adaptive strategy is proposed to combine the two algorithms together for H. 264. Experimental result shows that this strategy can keep coding efficiency in all kinds of video sequences, meanwhile the computation complexity is reduced adaptively according to the video’s content.
An epipolar resticted inter-mode selection for stereoscopic video encoding

Fast stereoscopic video encoding becomes a highly desired technique because the stereoscopic video has been realizable for applications like TV broadcasting and consumer electronics. The stereoscopic video has high inter-view dependency subject to epipolar restriction, which can be used to reduce the encoding complexity. In this paper, we propose a fast inter-prediction mode selection algorithm for stereoscopic video encoding. Different from methods using disparity estimation, candidate modes are generated by sliding a window along the macro-block line restricted by the epipolar. Then the motion information is utilized to rectify the candidate modes. A selection failure handling algorithm is also proposed to preserve coding quality. The proposed algorithm is evaluated using independent H.264/AVC encoders for left and right views and can be extended to MVC. Experimental results show that encoding times of one view are reduced by 41.4% and 24.4% for HD and VGA videos respectively with little quality loss.
Demo abstract: WearableHUB: An open pervasive wearable data fusion platform for personal health management

The past years has seen the emergence and fast growing of various wearable devices, such as smart watches, wrist bands, rings, glasses, jewelry, garments, etc. Although wearable devices have a wide usage scope, healthcare is one of the most promising field which is being exploited by a number of big enterprises and research institutes. However, existing healthcare related wearable products usually work individually. There is almost no data transmission or information exchange between different kinds of devices, which prevents data fusion of multiple devices and development of upper layer healthcare applications. In this demo, we present WearableHUB, a mobile phone based open pervasive wearable data fusion platform for personal health management. WearableHUB can transform multimodal data (motion data, physiological indices, etc.) into unified format according to predefined standard. Other than traditional wearable computing system, WearableHUB not only can get preloaded function results from different devices based on module library according to data type and device position, but also can trigger and get dynamic online downloaded APP function base on fusing multimodal data and extracting higher level information.
A lowbit rate vocabulary coding scheme for mobile landmark search

We present a low bit rate vocabulary coding scheme in the context of mobile landmark search. Our scheme exploits location cues to boost a compact subset of visual vocabulary, which is discriminative for visual search and incurs low bit rate query for efficient upstream wireless transmission. To validate the coding scheme, we have developed mobile landmark search prototype systems within typical areas including Beijing, New York City, Lhasa, Singapore, and Florence. Our system maintains a single vocabulary in a mobile device, which can be efficiently adapted with the location information of city-scale mobile users. Thus multiple downloading of large vocabulary is completely avoided for normal city tourists. In landmark search domain, we have reported superior performance over the state-of-the art works in compact image descriptors or signatures [2][4][5].
Signal Dependent Transform Based on SVD for HEVC Intracoding

Transform is used to compact the energy of the blocks into a small number of coefficients and is widely used in recent image/video coding standards. In the latest video coding standard high efficiency video coding (HEVC), a combination of discrete cosine transform (DCT) and discrete sine transform (DST) is adopted to transform the residuals from intra prediction. Since the DCT and DST are the fixed transforms that are derived from the Gauss–Markov model, some of residual blocks may not be compacted well by the DCT/DST. In this paper, we propose a signal dependent transform based on singular value decomposition (SVD) for HEVC intracoding. The proposed transform (SDT-SVD) is derived by performing SVD on the synthetic block and applied to the residual block considering the structural similarity between them. Furthermore, we extend SDT-SVD to template matching prediction (TMP) to further improve the intracoding performance. Experimental results show that the proposed transform on angular intra prediction (AIP) outperforms the latest HEVC reference software with a bit rate reduction of 1.0% on average and it can be up to 2.1%. When the proposed transform is extended to TMP-based intracoding, the overall bit rate reduction is 2.7% on average and can be up to 5.8%.
A visual search system based on multi-scale population cell coding

Gaze movement plays an important role in human visual search system. How to simulate such a system to efficiently encode and decode gaze movement for target searching is a meaningful issue. There are two key points that should be addressed for this issue. First, eye movement is affected by the visual context that includes more than one object in images. It is important to study how to encode the spatial relationship between the target and the other objects, and decode the spatial relationship to drive the gaze moving to the target. Second, the human retina has a non-uniform distribution of light sensing neurons, which can be viewed as a composition of neuron arrays in different spatial resolutions. When a system searches a target, how many scales of visual fields should be involved to sense the image stimulus? In this paper, we propose a visual search system using the population cell coding mechanism and the multi-scale visual field as sensing input. As an example, the system is applied to human eye center searching. An experimental comparison of a Full-scale visual field coding system and a Gradual-scale coding system is carried out. The experiment results show the Gradual-scale coding system performs better than Full-scale coding system for target searching.
Guided Image Contrast Enhancement Based on Retrieved Images in Cloud

We propose a guided image contrast enhancement framework based on cloud images, in which the context- sensitive and context-free contrast is jointly improved via solving a multi-criteria optimization problem. In particular, the context-sensitive contrast is improved by performing advanced unsharp masking on the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. To automatically determine the contrast enhancement level, the parameters in the optimization process are estimated by taking advantages of the retrieved images with similar content. For the purpose of automatically avoiding the involvement of low-quality retrieved images as the guidance, a recently developed no-reference image quality metric is adopted to rank the retrieved images from the cloud. The image complexity from the free-energy-based brain theory and the surface quality statistics in salient regions are collaboratively optimized to infer the parameters. Experimental results confirm that the proposed technique can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
Image interpolation via graph-based Bayesian label propagation.

In this paper, we propose a novel image interpolation algorithm via graph-based Bayesian label propagation. The basic idea is to first create a graph with known and unknown pixels as vertices and with edge weights encoding the similarity between vertices, then the problem of interpolation converts to how to effectively propagate the label information from known points to unknown ones. This process can be posed as a Bayesian inference, in which we try to combine the principles of local adaptation and global consistency to obtain accurate and robust estimation. Specially, our algorithm first constructs a set of local interpolation models, which predict the intensity labels of all image samples, and a loss term will be minimized to keep the predicted labels of the available low-resolution (LR) samples sufficiently close to the original ones. Then, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on all samples. Moreover, a graph-Laplacian-based manifold regularization term is incorporated to penalize the global smoothness of intensity labels, such smoothing can alleviate the insufficient training of the local models and make them more robust. Finally, we construct a unified objective function to combine together the global loss of the locally linear regression, square error of prediction bias on the available LR samples, and the manifold regularization term. It can be solved with a closed-form solution as a convex optimization problem. Experimental results demonstrate that the proposed method achieves competitive performance with the state-of-the-art image interpolation algorithms.