nils-herrmann/clean_scirepeval huggingface dataset:
Eliminating structure misalignments using robust matching and image editing based on seam carving

In this paper, we propose an algorithm that generates a natural composite from the misaligned images. The image stitching for this purpose is usually performed in two steps: correspondence matching of salient features followed by appropriated warping or editing. The proposed correspondence matching problem is formulated as the one-dimensional registration along the stitching boundary, where an appropriate energy function is proposed. The designed energy function consists of three complementary terms that encode appearance, smoothness and ordering of points, whereas the existing method considers the edge strength and ordering. Then we develop an algorithm that makes several salient points move to desired positions by using seam carving/inserting, which produces visually pleasing results compared to the conventional warping methods with less computations. The experimental results show that the proposed method efficiently and robustly generates natural composite images.
Aging Management Using a Reconfigurable Switch Network for Arrays of Nonideal Power Cells

Power cells, such as battery cells, solar cells, and fuel cells, are often connected in series and parallel to satisfy a desired amount of power and energy density as well as the terminal voltage. However, it is allowed to compose an array of power cells only when the individual cell has the identical characteristics among each other. However, even originally identical cells eventually have different characteristics due to uneven aging as well as manufacturing process variations. Mixture use of nonidentical cells in the same array results in: 1) significant inefficiency of the whole array; 2) accelerated aging of the weak cells; and 3) degrading healthy cells due to overloading. This paper is first to introduce a system-level solution for cell aging management. The proposed method is dynamic reconfiguration to modify cell grouping and array structures to enable independent control of aged cells from healthy cells. The reconfiguration switch network regroups cells into healthy, aged, and residual array groups, regardless of the physical locations of the aged cells. The proposed network is highly scalable in that the number of switches grows linearly as the number of cells increases. We show practicality of the proposed reconfiguration switch network with a case study of a solid oxide fuel cell (SOFC) system. However, the proposed reconfiguration switch network can be applied to any power cell arrays with an aging effect. We demonstrate significant improvement on the operating hours of an SOFC array. Such improvement is comparable with cell-level independent aging control where every SOFC stack has its own dc–ac inverter.
Regional deep feature aggregation for image retrieval

This paper presents a method to aggregate deep features for an object-based image retrieval system. Several recent works have demonstrated that it is quite important to selectively aggregate features with a weighting scheme and extract features from the limited regions likely to contain specific objects. Hence, the proposed method is to find possible candidate regions in an image, extract region descriptors from each region, and match images in a region-by-region manner. To adhere to using a pre-trained network without retraining or spatial verification, several candidate regions are found in an image and a more sophisticated pooling scheme is used for better performance. Specifically, salient points with active responses are detected in the image and clustered to form the candidate regions. In each region, we aggregate activations of a convolutional layer with the emphasis on more active spatial positions, and generate region descriptors effective for the object-based image retrieval. Our experiments show that the proposed method performs well on several public datasets, especially for the images showing the varied shapes or positions of an object.
Adaptive IIR filtering employing subband decomposition

In this paper, a novel adaptive IIR filter employing subband decomposition is presented, To derive the subband IIR adaptive filter, the subband ARMA model is introduced. Then the subband IIR adaptive filter is proposed. The proposed subband IIR filter employs reduced order adaptive IIR filter in each subband, which can approximate high order adaptive ARMA system. The computer simulations reveal that the convergence performance of the proposed subband IIR filter outperforms the conventional parallel form adaptive IIR filter.
IKONOS image fusion by minimisation of spectral distortion using MAP estimator

Though intensity-hue-saturation (IHS) approaches for image fusion are very fast and easy to implement, they have the intrinsic problem of spectral distortion. To reduce colour distortion in IHS methods, a maximum a posteriori estimator is proposed that minimises colour distance between estimated intensity image and panchromatic image, which preserves spectral information and sharpens the reconstructed images. The simulation demonstrates that the proposed method reconstructs clearer colour images and yields good results, both subjectively and quantitatively.
A scalable coding based on structure-preserving filter and saliency-ordered transmission

This paper presents a new scalable coding (SC) algorithm considering the properties of human visual system (HVS), especially the importance of structures and saliencies for visual perception. The proposed method first finds the salient regions in an image and then applies structure preserving filter to the regions excluding the saliencies. This image is encoded with a pre-specified quantization parameter (QP), which is the base layer in our coding system. Unlike the base layer image in the conventional methods, our base layer keeps the important regions (usually foreground) and also structures in the background while suppressing noise and fine textures. In addition, the enhancement layer is sent in the order of saliencies, i.e., the residual signals in the salient regions are given the priority in the transmission of enhancement layer. The algorithm is implemented based on the HEVC intra coding, and the experimental results show that the proposed method yields higher PSNR in salient regions than the conventional SHVC (scalable HEVC). Also, the overall perceptual quality is improved because the quality in the salient region is improved and important structures in the background region are kept, while the smeared noise and fine texture in the background region do not much affect the perception.
Fixed-point error analysis of CORDIC processor based on the variance propagation formula

This paper presents a fixed-point mean-square error (MSE) analysis of coordinate rotation digital computer (CORDIC) processors based on the variance propagation method, whereas the conventional approaches provide only the error bound which results in large discrepancy between the analysis and actual implementation. The MSE analysis is aimed at obtaining a more accurate analysis of digital signal processing systems with CORDIC processor, especially when the design specification is given by the signal-to-noise ratio or MSE. For the MSE analysis, the error source and models are first defined and the output error is derived in terms of MSE in the rotation mode of the conventional CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of practical application, a fast Fourier transform processor using the CORDIC processor is presented in this paper, and its output error variance is analyzed with respect to the wordlength of CORDIC. The results show a close match between the analysis and simulation.
Low bit rates video coding using hybrid frame resolutions

This paper proposes a video coding scheme for low bit rates environments such as video streaming in internet or mobile networks. Since the annoying artifacts at low bit rates result mostly from the coarse quantization of residual signals, we try to reduce this problem by introducing the hybrid resolution scheme and allocating more bits to the transform coefficients. The intra frames are encoded in the original resolution for their importance, and the inter frames are downsampled and encoded by H.264 with finer quantization steps. The images of original resolution are reconstructed by using the decoded images of reduced resolution and previously reconstructed frames. Each block of original resolution is reconstructed by one of four modes, according to the combination of interpolation of the decoded block of reduced resolution, fetching of the previously reconstructed block of original resolution block, and use of the residual images of original resolution. And, the selection of mode is decided by the R-D optimization. The final bit stream consists of side information, bit stream of the H.264 for the reduced frames, and the residual signals of original resolution. Experimental results show that the proposed method yields better performance than the usual H.264/AVC up to some critical bit rates and the PSNR difference increases up to 3.5 dB at low bit rates. In addition, since the motion estimation is performed with the downsampled frames, the computational load for the overall encoding is much reduced. The proposed video coding system is especially efficient at low bit rates environment, thus it is suitable for video streaming in internet and mobile networks.
Classification of video resolution for the enhanced display of images on HDTV

Although video sources for HDTV broadcasting are mainly in HD resolution, some of them are still from low resolution video sources such as videos that are taken long time ago, internet-streamed or cellphone videos. When these sources are displayed on HDTV, they usually appear to be blurry and their color is not vivid. To alleviate these problems, a proper video enhancement for each source is necessary to display them on HDTV with satisfaction. However, since the decoded images on HDTV do not contain the information on the origin of sources in many cases, it needs to be classified whether their origins out from HD source or not. For this, we propose an HD/non-HD classifier based on the Support Vector Machine (SVM) with the frequency information of the selected region in the decoded image. To evaluate the performance of the proposed HD/non-HD classifier, we use a test database of 6252 HD and 6934 SD still images captured from various TV genres. The experimental results show that the proposed classifier yields high accuracy rate of 99.61%.
A lossless color image compression method based on a new reversible color transform

In many conventional lossless color image compression methods, the pixels or lines from each color component are interleaved, and then they are predicted and coded. Also, it has been reported that the reversible color transform (RCT) followed by a grayscale encoder gives higher coding gain than the independent compression of each channel does. In this paper, we propose a lossless color image compression method that concentrates on the efficient coding of chrominance channels with a new color transform and hierarchical coding of chrominance channel pixels. Specifically, we first transform an input image with R, G, and B color space into Y CuCv color space using the proposed RCT, which shows better decorrelation performance than the existing RCT. After the color transformation, the luminance channel Y is compressed by a conventional lossless image coder, such as JPEG-LS, CALIC, or JPEG2000 lossless. Unlike the luminance channel, the chrominance channels Cu and Cv are relatively smooth and have different statistical characteristic. Therefore, the chrominance channels are differently encoded based on a hierarchical decomposition and directional prediction. Finally, effective context modeling for prediction residuals is adopted. Experimental results show that the proposed method improves the compression performance by 40% over the conventional channel independent compression methods and 5% over the existing methods that exploit the channel correlation.
An unsupervised image segmentation algorithm based on the machine learning of appropriate features

This paper proposes a new approach to the feature based unsupervised image segmentation. The difficulty with the conventional unsupervised segmentation lies in finding appropriate features that discriminate a meaningful region from the others. In this paper, the appropriate features are automatically learnt by machine learning with boosting scheme. At the initial step, the image is split into many small regions (blocks at first) and strong classifiers for every region, which discriminate the region from the others, are found by AdaBoosting. Each strong classifier so obtained is the weighted sum of several popular weak classifiers (features), which best describes the coherence of the region and thus well discriminates the region from the others. The output of this classifier is used in designing the energy function for the labeling, in the form of Conditional Random Fields (CRFs). Minimization of the energy function produces the labeling result which reflects the property learnt by the classifier. For the labeling result, the machine learning is again performed and the process iterates until some conditions are met. Experimental results show that the proposed method provides competitive result compared to the conventional feature based methods.
Energy-efficient digital filtering using ML-based error correction (ML-EC) technique

We present a maximum likelihood-based error correction (ML-EC) technique which achieves significant power savings in digital filtering. Although voltage over-scaling (VOS) can achieve high energy efficiency, it can introduce "soft errors" which severely degrade the performance of the filter. The proposed scheme detects, estimates and corrects these soft errors via an ML-based algorithm that achieves up to 47% power savings without any SNR loss and up to 60% power savings with a 1.5 dB SNR loss for an example case study of a frequency-selective low-pass filter.
Multiobjective data clustering

Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It includes detection of clusters by a set of candidate objective functions as well as their integration into the target partition. A key ingredient of the approach is a cluster goodness junction that evaluates the utility of multiple clusters using re-sampling techniques. Multiobjective data clustering is obtained as a solution to a discrete optimization problem in the space of clusters. At meta-level, our algorithm incorporates conflict resolution techniques along with the natural data constraints. An empirical study on a number of artificial and real-world data sets demonstrates that multiobjective data clustering leads to valid and robust data partitions.
Image denoising based on a statistical model for wavelet coefficients

In this paper, we propose a new statistical model for the relationship of wavelet coefficients and its application to image denoising. The magnitude of a wavelet coefficient usually shows high correlations with the nearby ones. This property has been exploited in many wavelet-based image processing techniques. However, conventional works consider only the local neighborhood of a coefficient when inferring its hidden state. Consequently, the image context is not faithfully reflected and thus there are sometimes visually annoying artifacts. We attempt to alleviate this problem by developing a new statistical model for the random field that is consisted of hidden variables of the overall band and thus includes global relationship of wavelet coefficients. In this model, the image context is encoded by the relations of hidden states, and the state plane is efficiently inferred by the sum-product algorithm. In the experiment, the proposed model is incorporated with the state-of-the-art denoising algorithm, namely BLS- GSM (Bayes least square - Gaussian scale mixture). The results show that the proposed algorithm suppresses many annoying artifacts that exist in the conventional denoising methods, and thus improves the subjective quality.
Digital watermarking of 3D geometry

A non-blind robust watermarking algorithm is proposed for 3D mesh models. The watermark is inserted into the 2D image which is extracted from the target 3D model. Each pixel value of the extracted 2D image represents a distance from the predefined reference points to the face of the given 3D model. This extracted image is defined as a range image in this paper. The watermark is embedded into the range image. Then, a watermarked 3D mesh is obtained by modifying vertices using the watermarked range image. Experimental results show that the proposed algorithm is robust against attacks such as rotation, translation, uniform scaling, mesh simplification, AWGN and quantization of vertex coordinates.
A fast algorithm for 2-D DCT

Recently, a novel fast algorithm for 2-D N*N DCT (discrete cosine transform), where N=2/sup m/, was proposed. Only half the number of multiplications required for the conventional row-column approach are needed. However, the relationship between the input-output indices for the postaddition stage in the algorithm is seemingly very irregular. In the present work, the authors derive general and systematic expressions for the relation of the postaddition stage in the 2-D DCT algorithm by representing it in matrix form and developing a method for partitioning the matrices. The results show that the signal flow graph from input to output has a recursive structure where the structure for smaller N appears recursively for larger N. Hence, one can obtain an organized and regular structure for the input-output relation in the postaddition stage.<<ETX>>
MAP-Based Object Extraction from Uncalibrated Image Pair

In this paper, we propose a new MAP algorithm for foreground objects extraction from uncalibrated image pairs. The segmentation is performed in the MAP framework with MRF (Markov random field) modeling of images. The proposed algorithm estimates several spatial transformations between two images by corresponding SIFT (scale invariant feature transform) points and sequential RANSAC (random sample consensus) algorithm. The area-ratio criterion is applied to the each transformation so that we select the transformation of foreground object. Using these transformations, we compute the likelihood of color-segmented subregions. We model the prior information which is based on smoothness condition. Finally, the object extraction is performed by the Bayesian belief propagation. Experiments on various image pairs and video sequences show promising results in extracting the foreground objects from the backgrounds.
Optimal design of finite precision FIR filters using linear programming with reduced constraints

An algorithm for the design of optimal one-dimensional (1-D) and two-dimensional (2-D) FIR filters over a discrete coefficient space is proposed. The algorithm is based on the observation that the equiripple frequencies of a subproblem (SP) in the branch and bound (BaB) algorithm are closely related to those of neighboring SPs. By using the relationship among the SPs, the proposed algorithm reduces the number of constraints required for solving each SP. Thus, the overall computational load for the design of FIR filters with discrete coefficients is significantly alleviated, compared with the conventional BaB algorithm.
Design of FIR filter over a discrete coefficient space with applications to HDTV signal processing

A new algorithm for the optimal design of finite impulse response (FIR) filters over a discrete coefficient space is presented. The algorithm is based on the branch and bound algorithm, where each of the subproblems are solved with a reduced number of constraints by exploiting the dependencies between the subproblems. As a result, the computation time is significantly reduced as compared to the conventional branch and bound algorithm. Some technical aspects in applying the filter with CSD (canonic signed digit) coefficient for image interpolation are presented. It is shown that minimizing the error at the critical frequencies is more important than minimizing the maximum ripple in the case of image interpolation. The filter designed by rounding one of the continuous coefficients to the nearest CSD value provides better results than the filter designed by the optimal algorithm.<<ETX>>
Human segmentation algorithm for real-time video-call applications

This paper presents a human region segmentation algorithm for real-time video-call applications. Unlike conventional methods, the segmentation process is automatically initialized and the motion of cameras is not restricted. To be precise, our method is initialized by face detection results and human/background regions are modeled with spatial color Gaussian mixture models (SCGMMs). Based on the SCGMMs, we build a cost function considering spatial and color distributions of pixels, region smoothness, and temporal coherence. Here, the temporal coherence term allows us to have stable segmentation results. The cost function is minimized by the well-known graphcut algorithm and we update our SCGMM models with the segmentation results. Experimental results have shown that our method yields stable segmentation results with a small amount of computation load.
Fast and simple text replacement algorithm for text-based augmented reality

In this paper, we present a novel text-based augmented reality system that performs optical character recognition on natural images and replaces the recognized texts with other informative contents. For the goal, we implement text detection and recognition functions, and develop an image augmentation algorithm for the realistic contents replacement. To be precise, we reconstruct background with a linear interpolation method and insert new contents to the reconstructed backgrounds. Finally, we get natural results by applying proper geometric distortions to them. In order to reduce visual artifacts caused by noisy boundaries, we also develop an optimal path selection method based on dynamic programming. Experimental results show that our method provides very natural results and runs in real-time even in mobile devices.
Real-time panorama canvas of natural images

This paper deals with a real-time panorama algorithm for mobile camera systems. The proposed system generates the panoramic images and shows the intermediate results simultaneously on the mobile display. The proposed panorama system consists of feature point extraction, feature tracking, rotation matrix estimation, and image warping onto cylindrical surface. Feature points are extracted by fast Hessian detector in the low resolution and corner detector in the high resolution. Then, the feature points are tracked by matching between input frame and synthesized panorama image. The camera motion is modeled as a rotation matrix which is estimated using the tracked feature points. For realtime operation of panoramic image synthesis, we propose a method to estimate the rotation matrix using non-iterative least square method. We project the feature points of image onto the unit sphere surface in the world coordinates. This enables us to estimate the parameters of rotation matrix as the linear and non-iterative problem. Finally, we project the input frames onto panorama surface using the rotation matrix. We also implement a real-time display interface to show the intermediate panorama results while panorama image is being generated. Thus, the proposed panorama system paints the panorama canvas with real images. According to the experiments in the mobile systems such as mobile phone and tablet PC, the proposed system works well to generate the panoramic images in real-time.