nils-herrmann/clean_scirepeval huggingface dataset:
An adaptive quantization algorithm for video coding

This paper proposes adaptive quantization algorithms for video coding using the information obtained from the previously encoded image. Before quantizing the DCT coefficients, the properties of reconstruction error of each macro block(MB) is estimated from the previous frame. For the estimation of the error of the current MB, a block with the size of MB in the previous frame is chosen. Since the original and reconstructed images of the previous frame are available in the encoder, we can evaluate the tendency of reconstruction error of this block in advance. Then, this error is considered as the expected error of the current MB. Comparing the error of the MB with the average of overall MBs, if it is larger than the average, a small step size is given for this MB, and vice versa. As a result, the error distribution of the MB is more concentrated to the average, yielding low variance and improved image quality. For reducing the blocking artifact, the error measure for the blocking artifact is also considered as well as SAD(sum of absolute difference) and PSNR. Especially for low bit application, the proposed algorithm yields much smaller error variance and higher PSNR compared to the conventional TM5. We also propose a modified algorithm for efficient hardware implementation.
Reduction of blocking artifacts by a modeled lowpass filter output

This paper proposes an algorithm for the reduction of blocking artifacts in the transform coded images and videos. The algorithm is based on the filtering of block boundaries. But, the proposed technique is not the actual filtering in the sense that the output is not obtained by multiplying the pixel values with the filter coefficients. Instead of filtering the pixel values through the filter coefficients, the output of the filter is modeled as a typical step response function of the lowpass filter or a functions with similar shapes. Then the blocky signal is just replaced by the modeled output signal which is generated by the library function such as exp or sin. The algorithm can be easily implemented by a lookup-table and very few multiplications, while the objective and subjective performance is comparable to those of other algorithms based on the conventional filtering and POCS (projection onto convex sets).
QRD-based optimal filtering for multichannel speech enhancement using time varying smoothing factor

In this paper, we introduce a time varying smoothing factor for the noise update in the optimal filtering based on QR decomposition (QRD). Conventionally, the noise statistics are updated during noise-only periods and kept fixed during speech present periods in QRD-based multichannel noise reduction [G. Rombouts and M. Moonen, Sept 2003]. Instead of using this conventional scheme that needs an explicit voice activity detector (VAD), we adopt a time varying smoothing factor which is parameterized from the normalized cross correlation (NCC). In a multi-microphone based system, the NCCs between two microphones can be used for finding the direction of signals [M. Omologo and P. Svaizer, 1994]. Provided that we know the direction of the speech signal and the time delays between microphones are compensated, the NCCs can be considered as a measure for the speech presence probability. The NCCs are estimated for a given time interval, and they are averaged over the microphones and smoothed in time. Then the averaged value is mapped to a smoothing factor through a nonlinear function. The simulation results show that the proposed algorithm yields better performance compared to the conventional QRD-based noise reduction.
Rectification of figures and photos in document images using bounding box interface

This paper proposes an algorithm for the segmentation and rectification of figures and photos in document images. The algorithm requires just a rough user-provided bounding box for the objects in a single-view image. On receiving the user's bounding box, it takes about 1–2 seconds to segment and rectify mega-pixel sized figures. The main feature of the algorithm is a novel segmentation method that exploits the properties of printed figures. Specifically, a set of boundary candidates is generated using the properties, and the optimal boundary in the set is found by using an alternating optimization scheme. This segmentation result is further refined so that it is well localized to the true boundary. In addition to our segmentation method, we also propose a new boundary interpolation method for the rectification of segmented figures. The method improves the quality of output by largely removing perspective distortions compared to conventional boundary interpolation methods. Experimental results on a variety of images show that the method is efficient, robust, and easy to use.
A relevance feedback algorithm based on the clustering and Parzen window

A relevance feedback algorithm based on the nonparametric approach is proposed. In the feature space, the algorithm generates multiple hyper-spheres around the regions where the images relevant with the query are densely populated, whereas the conventional algorithm searches the images in a single hyper-ellipsoid region. Then the Parzen window approach is applied to estimate the probability of relevance of each image in these multiple clusters (hyper-spheres). As a result, the relevance region in the feature space expands rapidly and covers arbitrarily shaped spaces with a small number of parameters. Also, since the user needs to determine only the positive images not the ambiguous negative ones, it is more convenient to use compared to some of the existing algorithms requiring negative feedback.
Design of 2K/4K/8K-point FFT processor based on CORDIC algorithm in OFDM receiver

The architecture and the implementation of a 2K/4K/8K-point complex fast Fourier transform (FFT) processor for an OFDM system are presented. The processor can perform 8K-point FFT every 273 /spl mu/s, and 2K-point every 68.26 /spl mu/s at 30 MHz which is enough for the OFDM symbol rate. The architecture is based on the Cooley-Tukey (1965) algorithm for decomposing the long DFT into short length multi-dimensional DFTs. The transposition and shuffle memories are used for the implementation of multi-dimensional transforms. The CORDIC processor is employed for the twiddle factor multiplications in each dimension. A new twiddle factor generation method is also proposed for saving the size of ROM required for storing the twiddle factors.
Fast algorithm and implementation of 2-D discrete cosine transform

An algorithm for the fast computation of a 2-D discrete cosine transform (DCT) is presented. It is shown that the N*N DCT, where N=2/sup m/, can be computed using only N 1-D DCTs and additions, instead of using 2N 1-D DCTs as in the conventional row-column approach. Hence the total number of multiplications for the proposed algorithm is only half of that required for the row-column approach and is also less than that of most of other fast algorithms, whereas the number of additions is almost comparable to that of others. It is also shown that only N/2 1-D DCT module are required for hardware parallel implementation of the proposed algorithm. Thus the number of actual multipliers being used is only a quarter of that required for the conventional approach.
Non-rigid image registration based on the globally optimized correspondences

In this paper, we propose a new approach to the non-rigid image registration. This problem can be easily attacked if we can find regularly distributed correspondence points over the whole image or over the objects of interest. Dense and stable image registration can be achieved by using some natural mapping (e.g., thin plate spline) of these correspondences. However, the problems with conventional correspondence matching methods are that the features can rarely be found at the textureless regions and the matching accuracy is degraded at the parts with non-rigid motions. In order to find the regularly spaced correspondences and their accurate matching even under the non-rigid motion, we place mesh nodes over the image and develop a new cost function that considers three complementary terms: similarity, smoothness and some topological constraint that prevents unlikely mappings. Experimental results demonstrate that the proposed method can find correct correspondences in the presence of non-rigid motions, multi-layers (motion discontinuity) and even in the textureless regions. Experimental results also show that the proposed method can be applied to old film restoration as well as image registration.
On the tracking properties of the ALNF (adaptive lattice notch filter)

The tracking properties of the ALNF (adaptive lattice notch filter) for linearly and randomly varying frequencies are investigated. Expressions for the asymptotic tracking error and MSE (mean square error) of the frequency estimate are derived. The results show that the forgetting factor plays an important role in controlling the tracking error, MSE, and output SNR. The optimal forgetting factor, which minimizes the MSE of the frequency estimate, is also derived from the expression for the MSE. Simulations are conducted to verify the theoretical expressions, and it is shown that the simulation results and theory are in close agreement.<<ETX>>
Luminance adapted skin color modeling for the robust detection of skin areas

Statistical models of color channels have been used for the detection of skin areas. However, since the distribution of colors also change as the luminance varies, color distribution models without considering the luminance variation do not work well for the images taken under various illumination conditions. Hence we propose a new skin detection algorithm that considers the luminance value in modeling the color distribution. For implementing this idea, we need a sample of skin color in the image, which can be obtained from the face. It is noted that the faces can be detected without color feature, but by using only structural features such as eyes, mouth, etc. In our algorithm, the eye detector and elliptical boundary are used to detect the face. From the face region, joint Gaussian distributions of color components with respect to the luminance values are obtained. Then, each pixel in the image is classified into skin or non-skin using the statistics obtained from the face region. Experimental results show that the proposed skin color model outperforms the conventional methods in terms of the detection accuracy and F-score.
Tracking analysis of an adaptive lattice notch filter

In this paper, tracking properties of the adaptive lattice notch filter (ALNF) for input sinusoids with linearly and randomly varying frequencies are investigated. Expressions for the asymptotic tracking error and the mean square error (MSE) of the frequency estimate are derived. The expressions show that the forgetting factor, or the step-size constant of the algorithm, plays an important role in controlling the tracking error, the MSE of the frequency estimate, and the output SNR. It is shown that the tracking error decreases as the forgetting factor increases. However, the MSE of the frequency estimate becomes large either for large and small forgetting factor. The output SNR also becomes small either for too large and small forgetting factors, Hence, the optimal forgetting factors, which minimize the MSE and maximize the output SNR for given frequency variation models, are also derived from the expressions for MSE. Intensive computer simulations are performed to augment the theoretical analysis, and the results are discussed.
Feature Based Binarization of Document Images Degraded by Uneven Light Condition

This paper proposes a document image binarization method, which is especially robust to the images degraded by uneven light condition, such as the camera captured document images. A descriptor that captures the regional properties around a given pixel is first defined for this purpose. For each pixel, the descriptor is defined as a vector composed of filter responses with varying length. This descriptor is shown to give highly discriminating pattern with respect to the background region, text region, and near text region. Of course there are misclassified pixels, which are then relabeled using an energy optimization method, specifically by using the graph cut method. For this, we devise an appropriate energy function that leads to clear and correct binarization. The proposed descriptor is also used for the skew detection, and thus correcting the skewed documents.
LPI adaptive descreening method with Hadamard domain analysis

This paper presents a new line-per-inch (LPI) adaptive descreening algorithm for restoring a high-quality image when scanning a printed matter. The LPI of scanned image is estimated and image region is classified into flat or edge region by analyzing the distribution in Hadamard transform coefficients. Then, an LPI adaptive edge preserving filtering is applied to reduce the halftone patterns not only in flat region but also in edge region. Proposed method has hardware-friendly structure without iteration and cascades of filters, and also it needs only integer adder and shift operations. Experimental result shows that the proposed method performs adaptively for a variety of LPI contents.
Fast lane detection & tracking based on Hough transform with reduced memory requirement

In this paper, we present a computationally efficient and robust lane detection algorithm based on Hough transform. The proposed method first extracts lane markings by applying 1D ridge detector to each row of an image. Given the extracted ridge points, the lane is then detected by applying Hough transform. Unlike the conventional methods, we consider only small set of line candidates which pass through a circle centered at previously detected vanishing point. The set is quite small and it is represented by a small region on a parametric domain. Hence, the proposed method needs much smaller accumulation array than conventional one and reduces the required memory. In addition, we propose modified parametric domain which encodes approximated lateral positions and the current lateral positions are searched on the modified parametric domain within certain region centered at previously detected positions. This proposed tracking scheme on the parametric domain considers temporal coherency and enables robust detection even when actual lane is weakly represented on the parametric domain. Experimental results show that the proposed method robustly detects the lane with reduced memory requirement.
Robust Document Image Dewarping Method Using Text-Lines and Line Segments

Conventional text-line based document dewarping methods have problems when handling complex layout and/or very few text-lines. When there are few aligned text-lines in the image, this usually means that photos, graphics and/or tables take large portion of the input instead. Hence, for the robust document dewarping, we propose to use line segments in the image in addition to the aligned text-lines. Based on the assumption and observation that many of the line segments in the image are horizontally or vertically aligned in the well-rectified images, we encode this property into the cost function in addition to the text-line alignment cost. By minimizing the function, we can obtain transformation parameters for camera pose, page curve, etc., which are used for document rectification. Considering that there are many outliers in line segment directions and missed text-lines in some cases, the overall algorithm is designed in an iterative manner. At each step, we remove text components and line segments that are not well aligned, and then minimize the cost function with the updated information. Experimental results show that the proposed method is robust to the variety of page layouts.
Open-Contour Tracking Using a New State-Space Model and Nonrigid Motion Training

Object tracking in a video sequence is usually achieved by tracking the bounding box over the object or the object’s boundary, each of which has somewhat different applications. In this paper, we present a new open-contour tracking algorithm based on a Bayesian framework in which the contour is a part of the object’s boundary. We first propose a new state-space model for the representation of contours, which can handle the rigid and nonrigid motions of contours independently. This model enables us to focus on the nonrigid motions during the training, and the model works for challenging rigid motion scenarios. In addition, for the robust tracking of contours, we propose a measurement function that considers the contrast on object boundaries, target appearance, and temporal coherence. We applied the proposed method to two kinds of open-contours targets, and the experimental results show that the proposed method achieves superior performance to the conventional contour tracking methods. The proposed method is also compared with recent bounding box tracking methods for the object tracking purposes, and the comparison shows that the proposed method works robustly to fast motions and yields a more accurate estimate of an object’s location than the conventional bounding box tracking methods.
Video Transcoding for Packet Loss Resilience Based on the Multiple Descriptions

This paper proposes error resilient video transcoding structures based on the multiple description (MD) scheme. Two structures are proposed for different use, namely low complexity simple MD transcoding structure (simple MD) and adaptive MD transcoding structure (adaptive MD). Simple MD structure always converts and splits the given bit stream into two descriptions regardless of channel condition. On the other hand, the adaptive MD structure considers the channel condition, and it switches between the MD and single description (SD) mode depending on the packet loss rate (PLR). In the switching process, the best mode which produces less end-to-end cost is chosen between SD and MD under given channel condition. The expected end-to-end cost is optimally estimated based on the rate-distortion theory at the time of encoding. The simulation results show that the simple MD structure provides efficient performance than the conventional error resilient transcoding structures based on the spatial and temporal error localization. And the adaptive MD structure shows higher PSNR than the conventional methods under the comparable complexity
An improved soft feedback V-Blast detection technique for TURBO-MIMO systems

In this paper, an improved minimum mean square error (MMSE) soft feedback detector, called the soft input, soft output, and soft feedback (SIOF) symbol detector, is proposed for turbo multi-input multi-output (turbo-MIMO) systems. The SIOF symbol detector is derived by minimizing the power of interference plus noise, given a priori probabilities of yet undetected layers and a posteriori probabilities of detected layers. As a result, soft feedback interference cancellation based on a posteriori information is derived, yielding symbol detection robust to error propagation effects. Furthermore, a low complexity implementation using approximate detection ordering and linear filtering is introduced. Simulations performed for block fading channels show that the SIOF symbol detector exhibits performance gains over the existing turbo-blast algorithm.
On the regular structure for the fast 2-D DCT algorithm

N. I. Cho and S. U. Lee (1991) proposed a fast algorithm for 2-D N*N DCT, where N=2/sup m/. It requires only half the number of multiplications of the conventional row-column approach. However, the signal flow graph for the postaddition stage seems very complicated and the order of the output index is seemingly irregular, because the postaddition stage was not based on the mathematical expressions. Consequently, derivation of the signal flow graph becomes complicated as the transform size increases. Systematic expressions for the postaddition stage of the algorithm that enable any N*N DCT to be implemented in a straightforward manner are provided here. The results show that the signal flow graph from input to output has a recursive structure in which the structure for smaller N reappears for larger N. However, the number of additions increases in the new signal flow graph at the expense of improving the regularity in the structure.
On the performance analysis of the subband adaptive digital filter

A performance analysis of the subband adaptive digital filter (SBADF) is presented, and some examples are given which show that SBADF yields better convergence properties than the full band adaptive digital filter (FBADF). The optimum filter in the subband and the relationship between the transfer function of the SBADF and that of the FBADF after convergence are derived. The error surface of the SBADF is derived and compared with that of the FBADF. The autocorrelation functions of signals in the subband are derived, from which the convergence behavior of the filter coefficients and mean square error can be examined. Computer simulation results show that the analysis presented is in close agreement with the experiment. It is shown that the SBADF provides better convergence properties than the FBADF in the case of adaptive line enhancer (ALE) and adaptive channel equalizer for severely distorted channels.<<ETX>>
Stereo Matching Using Multi-directional Dynamic Programming

In this paper, we propose a new stereo matching technique which employs an adaptive multi-path dynamic programming (DP) scheme. DP has been a classical and popular optimization method for various computer vision problems including stereo matching. However, the performance of conventional DP has not been satisfactory when applied to the stereo matching problems since the vertical correlation between scanlines has not been properly considered. This paper estimates disparity maps by using an optimal multi-directional DP scheme. We define a new energy function which considers the discontinuity of disparity and occlusions based on the edge information. The experimental results applied to the Middlebury stereo images demonstrate that our proposed algorithm shows better performances in stereo matching than the previous DP based approaches
Image co-saliency detection based on clustering and diffusion process

This paper presents a co-saliency detection algorithm based on clustering and diffusion process. For each image in a set, intra saliency maps are constructed from the measure of boundary and contrast priors. Then, segmented regions of all images are clustered based on features of colors, intra saliency and coherence of saliency. Co-saliency of each cluster is computed from combination of foreground probability and coherence of the cluster. The co-saliency of cluster is propagated over the segmented regions according to affinity between the cluster and segments. In addition, we adopt an intra image diffusion process from a graph with learned fully affinity in order to improve spatial consistency of co-saliency maps. Experimental results show that our algorithm yields better results compared to the state-of-the-art methods in terms of precision-recall curve, visual plausibility and computational cost.
Low-Power Adaptive FIR Equalizer Via Soft Error Cancellation

In this paper, we present an adaptive FIR equalizer which reduces power dissipation by employing a new algorithmic error correction technique. Building on the voltage overscaling (VOS) technique, we formulate the statistical estimation of timing errors that may be caused by VOS, called soft errors to detect and cancel them at a system level. We derive a minimum variance unbiased estimator, and develop an adaptive and power-optimized algorithm for an adaptive equalizer. Up to 30% power savings are demonstrated with negligible performance loss for an example, 16-tap minimum mean square error (MMSE) FIR equalizer