nils-herrmann/clean_scirepeval huggingface dataset:
Depth map up-sampling with texture edge feature via sparse representation

Depth information has been an important part in various computer vision applications. However, the low resolution depth map captured by TOF cameras and other depth sensors cannot be directly used in visual depth perception and 3D reconstruction. Thus, this paper proposes an effective depth map up-sampling algorithm which reconstructs a high resolution depth map from a low depth map, by integrating the registered color image. Different from the other depth map up-sampling methods, we establish the correspondence between the high resolution and low resolution image pairs via image sparse representation, then extract the geometric structure component and texture component from high resolution depth map and the relevant color image, respectively. Specially, we employ a guided filter to preserve regions smooth and edges sharp. The experimental results show that the proposed depth map up-sampling method obtains quality improvement on depth map and the synthesized view compared with state-of-the-art methods.
3D parametric design system for heating furnace

Current heating furnace design is mainly carried on two-dimensional CAD platforms. This process exists problem of low efficiency and less intuitive; the previous design experience can also not be reused. In this paper, a three-dimensional parametric design system is proposed for heating furnace based on secondary development method. This design method is supported by ActiveX-Automation technology and VBA technology. Batched modeling becomes possible through mutual visit between application programs. Key dimensions are linked with restrictions, so models are updated quickly by changing few parameters. Designer can be free from repeated drawing work during modification. Solid Edge system is chosen as the modeling server. A software is developed by Visual Basic for three-dimensional heating furnace design. Manual modeling is replaced by program process. In this way, design period is shortened, and design efficiency is improved significantly.
The study of multi-level authentication-based single sign-on system

Single Sign on (SSO) is a method for accessing resources within multiple systems. However, some security vulnerabilities exist in current SSO systems, and user's experience is not convenient. This paper proposes an extension of SSO, named multi-level authentication-based SSO (MLA-SSO). In MLA-SSO, SPs are divided into several levels based on security intensity. This allows the model to benefit from key advantages in security of the scheme. In specific procedures, MLA-SSO adds security strategy consultations. So MLA-SSO can help users to realize unified login and logout in their ways. At last, the design and implementation of a prototype of MLA-SSO, i.e. SHARE, is introduced and discussed.
Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.
Block-Level Entropy-Based Adaptive Sampling Framework for Depth Map

Recently, the three dimensional (3-D) video technology has drawn significant attention among industry and academic researchers. As a special data format in 3-D video, the depth map consists of gray levels, which are nearly the same within an object but change abruptly across the boundaries. In view of the similarity of the gray levels in the most regions, down-sampling can be employed as the pre-processing and up-sampling as post-processing in most applications of the depth map, such as compression and transmission. Differently from the conventional uniform sampling, in this paper a framework of adaptive sampling is proposed based on block-level entropy according to the context of each block in the depth map. If more complicated context or more boundaries exist in the block, higher sampling rate should be set up while lower sampling rate should be used for smooth regions. Since the block-level entropy can represent the content complexity of each block in the depth map, it can be calculated to determine the adaptive sampling rate. In the experiments, different up-sampling methods are employed in our framework to test and verify the results. Experimental results show that compared with the uniform sampling, the proposed framework has higher objective and subjective quality at the same sampling rate both for the depth map and for the synthesized virtual viewpoint.
A new virtual relay protection simulation system of substation based on IEC61850 standard

The intelligent substation uses the communication network instead of conventional secondary circuit, therefore virtual digital signal in the network replaces the physical electrical signal. Thus there is not a one-to-one correspondence between function information signal and the communication network wiring. In intelligent substation, IED is used as common operational platform, and the functional configuration condition can't be reflected in devices. Traditional protection simulation system is based on hardware and it's difficult to show the internal mechanism of the protections. This paper proposes a new virtual relay protection simulation system of intelligent substation based on IEC61850 standard, which displays the virtual circuit and the protection signals in a visual way. It can simulate a full section of fault condition by real-time closed-loop testing, show the dynamic logical processes of protection principle and secondary circuits, which can be helpful for protection training.
Visual saliency-based vehicle manufacturer recognition using autoencoder pre-training deep neural networks

Vehicle manufacturer recognition (VMR), consisting of vehicle logo detection (VLD) and vehicle logo recognition (VLR), is now a crucial part of intelligent transportation system (ITS). A novel VMR method combining visual saliency detection and autoencoder pre-training deep neural network (AP-DNN) is proposed in this paper. An automatic VLD method based on visual saliency detection is used to build a vehicle logo dataset. This dataset contains 10000 training samples and 1500 testing samples for ten types of vehicle manufacturers. In the experiment stage, using AP-DNN, a VLR rate of 99.20% with a training time of 40 min is obtained, which shows higher accuracy than scale-invariant feature transform (SIFT) or AdaBoost-based methods and less training time than methods using a convolutional neural network (CNN). Further, with 2000 vehicle images for ten different types of manufacturers, a VMR rate of 97.95% is obtained automatically and demonstrates the robustness and efficiency of our method.
Forensics of blurred images based on no-reference image quality assessment

The inexpensive hardware and sophisticated image editing software tools have been widely used, which makes it easy to create and manipulate digital images. The detection of forgery images has attracted academic researches in recent years. In this paper, we proposed a forensic method to detect globally or locally blurred images using no-reference image quality assessment. The features are extracted from mean subtracted contrast normalized (MSCN) coefficients and fed to SVM, which can distinguish the tampered regions from the original ones and can quantify the tampered regions. Experimental results show that this method can detect the edges of tampered regions efficiently.
Corrections to "A 0.02 mm2 59.2 dB SFDR 4th-Order SC LPF With 0.5-to-10 MHz Bandwidth Scalability Exploiting a Recycling SC-Buffer Biquad"

Presents corrections to the paper, “A 0.02 mm 59.2 dB SFDR 4th-order SC with 0.5-to-10 MHz bandwidth scalability exploiting a recycling SC-buffer biquad,” (Zhao, Y., et al),  Solid-State Circuits, vol. 50, no. 9, pp. 1988–2001, Sep. 2015.
A multi-channel medium access control protocol for multicast in mobile ad-hoc network

In mobile ad-hoc network, some multi-channel MAC protocols utilize multiple channels to reduce the collision of wireless transmission and thus get high throughput. These multi-channel MAC protocols aim at improving the performance of unicast communication, and multicast data are generally transmitted as broadcast. So multicast cannot benefit from the multi-channel technique. This paper proposes a multi-channel media access control protocol for multicast (MCMAC) which uses multi-channel techniques to improve multicast performance. To improve the reliability of MAC layer, we extended a reliable multi-channel MAC protocol for multicast (RMCMAC) based on MCMAC. Taking ODMRP [S.-J. Lee et al., 2000] as an example of multicast routing protocols, we evaluate the multicast performance of MCMAC and RMCMAC for ad hoc networks via detailed simulation.
Knowledge Transferring for Image Classification

Traditional image classification approaches focused on utilizing a host of target data to learn an efficient classification model. However, these methods were generally based on the target data without considering auxiliary data. If the knowledge from auxiliary data could be successfully transferred to the target data, the performance of the model would be improved. In recent years, transfer learning has emerged to address this problem. Based on transfer learning, we present a knowledge transferring method to enhance the image classification performance. Since the target data are merely limited on images, we employ an auxiliary dataset to construct the pseudo text for each target image. By exploiting the semantic structure of the pseudo text data, the visual features are mapped to the semantic space which respects the text structure. Experiments show that the proposed approach in this paper is feasible.
Cross-Modal Retrieval With CNN Visual Features: A New Baseline.

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.
Stereo video coding based on H.264 with adaptive prediction mode

H.264 is an advanced video compression standard and absorbs the advantages of the previous standard. In this paper, we propose a scheme which compresses the stereo video based on H.264 with adaptive prediction mode. In the proposed scheme, left sequence and right sequence are incorporated into one sequence firstly. Then the incorporated sequence is compressed with adaptive prediction mode. There is a processor in this scheme. When the current frame is compressed, the processor chooses a frame which is the most interrelated with the current frame from the prior frames as reference frame. Experimental results successfully demonstrate this method is effective.
Two-Description Image Coding With Steganography

For two-description image coding, a conventional scheme is to partition an image into two parts and then to produce each description by alternatively concatenating a finely coded bitstream of one part and a coarsely coded bitstream of the other part. This letter presents a new two-description image coding approach using steganography. Specifically, we propose forming each description by embedding (hiding) the coarsely coded part into the finely coded part based on a least-significant bit (LSB) steganographic method. In this way, the bit budget for the coarsely coded part in each description can be saved with little reconstruction degradation for the finely coded part if the embedding process is well designed. The experimental results substantiate the effectiveness of the proposed method.
Flyability Failures Due to Siloxanes at the Head-Disk Interface Revisited

In an earlier report, flyability failures due to siloxanes were investigated comparing disks with either Z-Dol or X-1P lubricant. On the Z-Dol disk, the head-disk interface failed at 47 h (h) and the slider accumulated silicon oxide smears, whereas the X-1P disk did not fail (truncated at 300 h) and the slider remained clean. The contrasting performance was rationalized by an assumed mechanism in which degraded lubricant was essential. In this paper, we demonstrate that siloxanes can be tribochemically converted into silicon oxides without any lubricant on the disk or slider. Lubricant degradation is, therefore, not needed for the formation of silicon oxides from siloxanes. We find that siloxane oligomers on Z-Dol disks form micrometer-sized droplets 100 nm in height, whereas on X-1P disks, siloxanes spread out as a very thin film. The siloxane droplets on Z-Dol disks may lead to flyability failure because they exceed the fly height. The presence or absence of siloxane droplets on disk surfaces may be explained in terms of surface wetting properties characterized by contact angle measurements
Real-time video streaming exploiting the late-arrival packets

For real-time video applications, such as video telephony service, the allowed maximum end-to-end transmission delay is usually fixed. The packets arriving at the destination out of the maximum end-to-end delay are treated as late-arrival packets, and these packets are discarded in traditional video transmission systems. In this paper, in order to improve the system performance, we propose to exploit these packets to update the decoder reference buffer. Two schemes are proposed to exploit the late-arrival packets, one scheme is to use sliding-window updating, where the updating window is moving; another scheme is to use fixed-window update together with systematic Reed-Solomon code. The effectiveness of the two schemes are validated by simulation results without adding extra delay. It is found that in both schemes, the updating window size plays an important role on the system performance.
Multiple Description Video Coding using Adaptive Temporal Sub-Sampling

Multiple description coding (MDC) is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. Especially, MDC has emerged as an attractive approach for video applications where retransmission is unacceptable or infeasible. In this paper, an effective MD video codec is designed based on pre-and post-processing of video sequences, without any modification to the source or channel codec. Considering different motion information of inter-frame, adaptive temporal sub-sampling is employed to the original video data. As a result, adaptive redundancy can make a better tradeoff between the reconstruction quality and compression efficiency. The experimental results exhibit better performance of the proposed scheme than other schemes.
Warping-driven mode selection for depth error concealment

This paper deals with the recovery of corrupted depth maps in loss-prone networks. Different from color maps, depth maps are not directly used for display but served for view synthesis process. Therefore, the conventional concealment methods which focus on reducing the distortion of reconstructed color maps, are not suitable for the corrupted depth maps. In this paper, a novel mode selection method for depth error concealment is proposed to efficiently reconstruct the lost depth blocks. To this end, three modes, i.e., INTRA, INTER, and MV sharing, are applied as candidate modes to achieve the concealed depth blocks. In order to provide acceptable synthesized views, the optimal mode selection is based on a new distortion function, in which warping process is taken into account. Experimental results show that the proposed method outperforms the conventional schemes up to 0.65 dB on the synthesized views, at block loss rate up to 20%. Most important of all, the subjective quality of synthesized views are much better due to the consideration of warping process in the mode selection.
Landsat time series clustering under modified Dynamic Time Warping

Compared with the single remote sensing image, the time series images provide more information of ground objects, which can greatly improve the clustering accuracy. But time series clustering also has many difficulties, such as the impacts of cloud and other sharp noise. The sharp noise impacts time series clustering by affecting the calculation of the similarity measure of sequences. Therefore, this paper proposed a CD-DTW method (Canberra Distance-Dynamic Time Warping) based on the classic dynamic time warping distance and canberradistance. The CD-DTW method helps to construct a more reliable distance measure for remote sensing time series clustering. The CD-DTW is applied to Landsat time series clustering, and the overall performance of CD-DTW distance (overall accuracy 91.52%; kappa coefficient 0.89) was considerably better than that of classic DTW distance.
Control-Point Representation and Differential Coding Affine-Motion Compensation

The affine-motion model is able to capture rotation, zooming, and the deformation of moving objects, thereby providing a better motion-compensated prediction. However, it is not widely used due to difficulty in both estimation and efficient coding of its motion parameters. To alleviate this problem, a new control-point representation that favors differential coding is proposed for efficient compression of affine parameters. By exploiting the spatial correlation between adjacent coding blocks, motion vectors at control points can be predicted and thus efficiently coded, leading to overall improved performance. To evaluate the proposed method, four new affine prediction modes are designed and embedded into the high-efficiency video coding test model HM1.0. The encoder adaptively chooses whether to use the new affine mode in an operational rate-distortion optimization. Bitrate savings up to 33.82% in low-delay and 23.90% in random-access test conditions are obtained for low-complexity encoder settings. For high-efficiency settings, bitrate savings up to 14.26% and 4.89% for these two modes are observed.
A new affine transformation: its theory and application to image coding

The fractal image coding technique has attracted a degree of interest for its low bit rate. But the reconstructed image is of medium quality. This problem has prevented the fractal technique from being used practically. In order to improve the compression fidelity, a new affine transformation is proposed. Meanwhile, its contractivity requirement is analyzed, and the optimal parameters are derived using the least square method. The new affine transformation has been practically used in image coding. Experiments show that the PSNR can reach 28.7 dB at a compression ratio (CR) of 16.4 for the 256/spl times/256/spl times/8 "Lena" image. Comparison with other fractal coding schemes shows that the new affine transformation can improve the reconstructed image quality efficiently.
Improving the Linearity and Power Efficiency of Active Switched-Capacitor Filters in a Compact Die Area

The die size of multistandard wireless transceivers in ultrascaled CMOS is dominated by the baseband low-pass filters (LPFs), which typically count on passive-RC components to define the time constant. To break this area constraint, this paper revisits the active switched-capacitor (SC) LPF for its united benefits of clock-rate-defined bandwidth, accurate cutoff frequency, and small die size due to capacitor-ratio-based sizing and no spare elements. The key challenges of active-SC LPFs are the speed- and linearity-to-power tradeoffs, which are addressed by two circuit techniques: 1) switched-current assisting (SCA) and 2) precharging (PC). The SCA accelerates the charging speed of the integration capacitor, while the PC improves the linearity when charging the load capacitor. Three prototypes (first order, biquad, and fifth-order Butterworth) fabricated in a 65-nm CMOS process validate the feasibility of the proposed SCA and PC techniques.
Global robust finite time control for a class of uncertain second-order nonlinear systems

For the tracking control of a class of uncertain second order nonlinear systems, this paper proposes a robust global finite time control strategy. This control strategy consists of two parts: firstly, a time-varying sliding mode controller is designed to make the tracking errors to zero at a desired finite time t r ; then, in the succeeding time t > t r , another nonsingular terminal sliding mode controller is used to make the tracking errors stay at zero. The algorithm has the following advantages: 1) the tracking error convergence time can be set in advance; 2) error convergence rate can be adjusted by changing the value of a parameter; 3) global robustness is guaranteed for parameter uncertainty and external disturbance. Numerical results demonstrate the usefulness of the proposed control strategy.
Joint iterative guidance filtering for compressed depth images

In the general 3D scene, the correlation of depth image and corresponding color image exists, so many filtering methods have been proposed to improve the quality of depth images according to this correlation. Unlike the conventional methods, in this paper both depth and color information can be jointly employed to improve the quality of compressed depth image by the way of iterative guidance. Firstly, due to noises and blurring in the compressed image, a depth pre-filtering method is essential to remove artifact noises. Considering that the received geometry structure in the distorted depth image is more reliable than its color image, the color information is merged with depth image to get depth-merged color image. Then the depth image and its corresponding depth-merged color image can be used to refine the quality of the distorted depth image using joint iterative guidance filtering method. Therefore, the efficient depth structural information included in the distorted depth images are preserved relying on depth itself, while the corresponding color structural information are employed to improve the quality of depth image. We demonstrate the efficiency of the proposed filtering method by comparing objective and visual quality of the synthesized image with many existing depth filtering methods.
Meaningful regions segmentation in CBIR

In this paper, a new approach to fully automatic image segmentation is proposed to get the meaningful regions of general-purpose image. In order to avoid image over segmenting, the original input image is first smoothed by Gaussian filters with different scales. Then an improved ISODATA clustering algorithm with parameters selecting dynamically is proposed to cluster the image pixels into different regions. To eliminate those fragmentary regions, a region merging strategy is also presented. The final experimental results show that the proposed approach can effectively separate the objects from background of general-purpose image.