nils-herrmann/clean_scirepeval huggingface dataset:
Fixed point error analysis of CORDIC processor based on the variance propagation

The effects of angle approximation and rounding in the CORDIC processor have been intensively studied for the determination of design parameters. However, the conventional analyses provide only the error bound which results in large discrepancy between the analysis and the actual implementation. Moreover, some of the signal processing architectures require the specification in terms of the mean squared error (MSE) as in the design specification of FFT processor for OFDM. This paper proposes a fixed point MSE analysis based on the variance propagation for more accurate error expression of the CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of application, an FFT processor for OFDM using the CORDIC processor is presented. The results show close match between the analysis and simulation.
Fast text line extraction in document images

This paper proposes an algorithm for fast text line extraction in document image. Instead of binarization or multi-oriented Gaussian blurring of an image as in the conventional methods, we use integral image and design filters that are proper to detect text regions on the integral image. After the filtering, the center points in the regions are discovered by cascade text region verification followed by non-maximum suppression. Finally, text lines are extracted by grouping the points on the same line. The proposed method is tested with document images taken in various environments, and it is shown to be faster than the conventional ones while its performance is comparable.
Color filter array demosaicking using optimized edge direction map

This paper proposes a new color filter array de-mosaicking method with emphasis on the edge estimation. In many existing approaches, the demosaicking is considered a directional interpolation problem, and thus finding the correct edge direction is a very important factor. However, these methods sometimes fail to determine an accurate interpolating direction because they use local information from neighboring pixels. For the estimation of edge direction using global information, we employ an MRF framework where the energy function is formulated by defining new notions of interpolation risk and pixel connectivity. Minimizing this function gives the edge directions, and the green channel is interpolated along the edges. Then we iterate the luminance update and color correction using the high frequencies from green channel. The algorithm is tested with the commonly used images, and it is shown to yield higher CPSNR than the state-of-the-art methods in many images, up to 2.7dB at maximum and 0.4dB on average. Subjective comparison also shows that the proposed method produces less artifacts on complex structures.
Autofocus method using similarity measure based on inter-color channel correlation

Recently, an autofocus method using dual aperture and color filters has been shown to be fast and cost-effective, and thus suitable for compact digital cameras. The dual aperture separates the incoming light rays and the color filters on the apertures transmit only a single color component. Hence, each light ray passing through an aperture can be separately captured by the corresponding color sensors. The captured monochromatic images of two different color channels are spatially shifted with respect to each other in propotion to the defocus. The disparity between the different color channel can be converted to the distance for the focusing lens to move. However, since the disparity between two different color images cannot well be measured by the conventional methods, we propose a new similarity measure between the dichromatic channel images (e.g. R and B color channels). Experimental results show that the proposed measure is more effective than the existing measures especially when the region of focus measuring is small. This advantage enables to focus the small object and reduce the processing time.
Consumer video summarization based on image quality and representativeness measure

This paper presents an algorithm for the static summarization of consumer videos. For the fast summary of long and unedited consumer videos, we propose a two-step approach where the first step is for pruning the video and the second for content-aware clustering with keyframe selection. More specifically, the first step employs the conventional spectral clustering method with simple color histogram features for removing most redundant frames. This step yields a condensed video that is shorter and has clearer temporal boundaries than the original video, so that temporal segmentation can be easily performed. In the second step, refined clustering is performed for each of the temporal segments by the image classification method based on the sparse coding of SIFT features. The representativeness of each frame is defined from these coding results, which is one of the measures for the keyframe selection. We also formulate the quality measures of each frame such as contrast, blur, and image skew. The main problem is finding the frames that have both representativeness and high quality, which is formulated as an optimization problem. Experimental results on videos with various lengths show that our video summaries closely follow the important contents of videos.
Fixed point error analysis of CORDIC processor based on the variance propagation

The effects of angle approximation and founding in the CORDIC processor have been intensively studied for the determination of design parameters. However, the conventional analyses provide only the error bound which results in large discrepancy between the analysis and the actual implementation. Moreover, some of the signal processing architectures require the specification in terms of the mean squared error (MSE) as in the design specification of FFT processor for OFDM. This paper proposes a fixed point MSE analysis based on the variance propagation for more accurate error expression of CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of application, an FFT processor for OFDM using the CORDIC processor is presented. The results show close match between the analysis and simulation.
Noise Eigenvalue Modification Methods for Spatial Subspace Based Multi-Channel Speech Enhancement

In this paper, frequency domain multi-channel filtering schemes are proposed for speech enhancement, based on the subspace decomposition of spatial spectral matrices. For better estimation of noise statistics, which is important for most speech enhancers, we propose noise eigenvalue modification methods for the correction of noise spatial spectral matrix. These methods are based on the rank-1 property of the speech spatial spectral matrix for single desired speech source. Simulation results show that the proposed methods yield better performance compared to the conventional multi-channel Wiener filtering.
Design of signed powers-of-two coefficient perfect reconstruction QMF Bank using CORDIC algorithms

Lattice structures have several advantages over the tapped delay line form, especially for the hardware implementation of general digital filters. It is also efficient for the implementation of quadrature mirror filter (QMF), because the perfect reconstruction is preserved under the coefficient quantization. Moreover, if lattice coefficients are implemented in signed powers-of-two (SPT), the hardware complexity can also be reduced. But the discrete coefficient space with the SPT representation is sparse when the number of nonzero bits is small. This paper proposes a structure of orthogonal QMF lattice with SPT coefficients, which has much denser discrete coefficient space than the conventional structure. While the conventional approaches directly quantize the lattice coefficients into SPT form, the proposed algorithm considers the quantization in the SPT angle space. For this, each lattice stage is implemented by the cascade of several variants of COordinate Rotation DIgital Computer. The resulting angle space and corresponding discrete coefficient space is much denser than the one generated by the conventional direct quantization approach. An efficient coefficient search algorithm for this structure is also proposed. Since the proposed architecture provides denser coefficient space, it shows less coefficient quantization error than the conventional QMF lattice
Hybrid Resolution Switching Method for Low Bit Rate Video Coding

This paper proposes a video coding method using hybrid resolution switching for low bit rate environments. The proposed method encodes I pictures in high resolution and B and P pictures in low resolution. The decimated inter-frame pictures are encoded in the usual H.264/AVC framework, and they are reconstructed to high resolution ones using motion information, interpolation filter, and some residual signals in high resolution. The proposed video coding scheme shows better compression performances in low bit rate environments than the traditional algorithms based on H.264/AVC. The resolution switching method increases the coding efficiency in low bit rates, mainly because the side information of motion estimation is reduced. It is expected that the proposed method can also improve higher bit rate cases, if some parameters optimization and deblocking filter are appropriately applied.
Panorama Mosaic Optimization for Mobile Camera Systems

In order to enable the real-time image mosaic in mobile devices that have very limited computing power and memory, we propose an efficient image mosaic algorithm with integer arithmetic. The proposed algorithm is focused not only on the computational efficiency, but also on the performance improvement of image mosaic. For the efficient projection without transform estimation, we obtain successive images through the semitransparent view finder which shows the fixed amount of right part of previously captured image. We project each images onto cylindrical surface by the proposed projection equation and integer arithmetic. We align the projected images by hierarchical hexagon search and a matching measure that is robust to illumination changes caused by different exposures and vignette distortion. Finally, the images are naturally blended using color compensation and dynamic programming based stitching. The proposed mosaic algorithm is embedded and tested in mobile phone systems. According to various tests and experiments, the proposed algorithm shows good panorama composition in real-time compared with the other PC-based methods.
Improved H.264/AVC lossless intra compression using multiple partition prediction for 4×4 intra block

The sample by sample DPCM (SbS DPCM) is an important prediction technique for the H.264/AVC lossless intra compression. In this paper, we propose a new prediction method that is more efficient than the conventional SbS DPCM, thereby improving the overall compression performance. The proposed method prepares 5 partition patterns for each 4×4 block such as 4×4 (no partition), 4×2, 2×4, 2×2 and 1×1. The pixels in each partition is intra predicted by SbS DPCM and the best partition which produces minimum bit is selected as the partition pattern for the 4×4 block. Also, the number of available intra prediction directions is determined according to the partition pattern to avoid too much side-information transmission. The experimental results show that the proposed method gives 3.62 % point bit rate saving on average and 4.74 % point bit rate saving at maximum compared to the conventional SbS DPCM.
Block convolution with arbitrary delays using fast Fourier transform

In digital signal processing applications, the convolution with a very long finite impulse response (FIR) filters is often required. In order to perform a fast high order convolution, the block convolution using fast Fourier transform (FFT) can be considered, which entails the time delays proportional to the block length. But since the delays due to the block convolution is generally set to 2/sup n/, we do not have much choice in selecting the delays. In this paper, we propose a block convolution with an arbitrary delay, which can be implemented by overlap-save (OLS) or overlap-add (OLA) algorithms. Experimental results show that even though the OLS-based algorithm creates some redundancies in output data due to more overlapped samples, the cost is nearly the same as the previous works. And the OLA-based algorithm has exactly the same complexity at the expense of an additional buffer.
Age and gender estimation using deep residual learning network

In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database . However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.
Blocking artifact reduction method based on non-iterative POCS in the DCT domain

The post-processing method based on projections onto convex sets (POCS) has demonstrated good performance for blocking artifact reduction. However the iterative procedure in POCS requires a lot of computations, and would be infeasible for practical post-processing applications with real-time constraints. In this paper, we propose non-iterative post-processing method based on POCS in DCT domain for complexity reduction. In DCT domain POCS (D-POCS), the low-pass filtering (LPF) is performed in the DCT domain to remove the inverse DCT and forward DCT modules from POCS. Through the investigation of LPF in iterative POCS, we define kth order LPF which is equivalent to the LPF with k iterations. By combining DCT domain filtering and the kth order LPF concept, we define kth order DCT domain LPF. Simulation results show that the proposed D-POCS without iteration gives very close PSNR and subjective quality performance compared to the conventional POCS with iterations, while it requires much less computational complexity. If we take into account typical sparseness in DCT coefficients, the D-POCS method gives tremendous complexity reduction. Hence the proposed D-POCS would be an attractive method for practical real-time post-processing applications.
Level set tracking using shape prior and global-local color model

This paper presents a new level set tracking method for the tracking of non-rigid objects in videos. The main idea is to introduce a new probabilistic model to classify the background and object more robustly, which is then encoded as a new term for the energy function for solving the level set problem. Also, we propose to use bounding box of the contour as a shape prior, which simplifies the contour tracking process. Another contribution is to design temporal smoothness term. Experimental results shows that our method performs well on several challenging sequences in comparison with conventional algorithms.
Word Segmentation Method for Handwritten Documents based on Structured Learning

Segmentation of handwritten document images into text-lines and words is an essential task for optical character recognition. However, since the features of handwritten document are irregular and diverse depending on the person, it is considered a challenging problem. In order to address the problem, we formulate the word segmentation problem as a binary quadratic assignment problem that considers pairwise correlations between the gaps as well as the likelihoods of individual gaps. Even though many parameters are involved in our formulation, we estimate all parameters based on the Structured SVM framework so that the proposed method works well regardless of writing styles and written languages without user-defined parameters. Experimental results on ICDAR 2009/2013 handwriting segmentation databases show that proposed method achieves the state-of-the-art performance on Latin-based and Indian languages.
Segmentation based disparity estimation using color and depth information

The well-known cooperative stereo uses two dimensional rectangular window for a local block matching, and three dimensional box-shaped volume for a global optimization procedure. In many cases, appropriate selections of these matching regions can provide satisfactory matching results. This paper presents a new method for iteratively modifying sizes and shapes of matching regions based on color and depth information. This algorithm computes the aggregated matching costs with two ideas. The first idea is to select matching regions based on object boundaries to avoid projective distortion. This provides the reliable matching scores as well as the prevention of the foreground fattening phenomenon. The second idea is to iteratively modify the segmentation map by merging the regions where the disparities are likely to be the same. Experimental results show that the proposed algorithm provides more accurate disparity map than other algorithms. Especially, the computed disparity map shows the advantage of our algorithm in disparity discontinuity regions.
Single image dehazing based on reliability map of dark channel prior

The dark channel prior is generally a powerful prior for single image dehazing, but it is invalid when there are objects which have similar colors to the atmospheric light. It is difficult to find the accurate transmission factor in this case, and thus the results show some color distortion. This paper proposes a new algorithm for the estimation of correct transmission map regardless of object color. The proposed method defines a reliability map that depicts how much the objects or areas meet the dark channel prior assumption, and then estimate the transmission map using the reliable pixels only. The transmission factors at the unreliable pixels are estimated by linear fitting using the reliable neighboring pixels. Experiments show that the dehazed images by the proposed method have less color distortion than the results by the conventional algorithm.
A gradient guided deinterlacing algorithm

This paper proposes new intra and inter-deinterlacing algorithm based on the gradient domain image/video processing approach. From the interlaced (field) images, gradient field images are generated and then the gradients of missing lines are estimated to generate gradient images which correspond to progressive frames. The proposed intra-deinterlacing is basically an edge-oriented interpolation, which interpolates the gradients of missing pixels along the optimal spatial orientation. Finding the optimal orientation among all possible ones is formulated as a labeling problem with Markov random field (MRF) framework. For obtaining better results for fast moving video sequences, this method is extended to inter-deinterlacing, which considers the temporal orientations as well as the spatial ones. With the synthesized gradient frame images and the original pixel values of the field images, we then formulate a linear equation that generates the final progressive frame images. Like other gradient domain image processing applications, the integrity of edges is the main advantage of the proposed method.
A low-complexity near-ML decoding technique via reduced dimension list stack algorithm

In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.
Simultaneous object extraction and disparity estimation using stochastic diffusion

This paper proposes a Bayesian maximum a posteriori (MAP) method to estimate disparity field and to extract objects in the stereoscopic images. The disparity and segmentation fields are modelled as the Markov random fields (MRFs), and are estimated by a stochastic approach called stochastic diffusion. The stochastic diffusion is a new energy minimization method to search for the solution fields in the MAP estimation. A clustering method utilizes the disparity field and color information to classify the regions into foreground objects or background. The line field is also included to improve the detection of the object boundaries. According to the some experiments, the proposed approach shows good performances in the foreground objects extraction even in the complex background.
Automatic Defect Classification Using Frequency and Spatial Features in a Boosting Scheme

An automatic defect classification algorithm is proposed in a boosting manner. The proposed method exploits the histogram of spatial orientation and frequency features. Specifically, the spatial gradient orientations of defect image are accumulated to be a histogram, and they are trained by SVM to construct a classifier. The frequency features are the projection of 2D Haar patterns on the frequency responses. The classifiers using these spatial and frequency features are combined in a boosting manner to improve the classification performance. According to the experiments with 100 training and testing sets, the proposed boosting method improves the classification performance compared with the previous works using optical features such as colors, shapes, and sizes of defects.
Extending the lifetime of media recorders constrained by battery and flash memory size

The lifetime of a stand-alone media recorder is a function of both the battery size and flash memory size. In this paper, we present a power management framework for media recorders that significantly enhances their lifetime while minimizing the flash memory usage and maintaining the same level of recording quality. This is achieved by implementing a mixture of encoding algorithms of different complexities that generate data with different compression ratios, and in turn balancing the energy consumption and the flash memory usage. The proposed method can be effectively employed on a direct battery drive system which does not use a DC-DC converter. The gradual drop of the battery voltage of such system is compensated by operating algorithms of lower complexity more and more. For a speech encoding application where a mixture of ADPCM (low complexity) and MP3 (high complexity) is used, the proposed algorithm achieves 70% more lifetime than a DC-DC converter with a highest clock frequency, and 20% more lifetime than even a DC-DC converter with the optimal clock frequency.