nils-herrmann/clean_scirepeval huggingface dataset:
An efficient multicategory classifier based on AdaBoosting

In this paper, we propose an efficient multicategory classifier based on AdaBoosting scheme. The multicategory problems can be solved by multiple use of two-category classifiers or by use of a single classifier with multiple discriminant functions. In the case of boosting algorithms, since the use of simple classifier is one of the most important ingredients, they have focused on two-category classifier for each weak classifier. But for applying the two-category booster to m-category problems, we need O(m/sup 2/) boosters instead of O(m) ones arrangement scheme of the boosters as like detector-pyramid (S.Z. Li and Z. Zhang, 2004). We propose a multicategory boosting algorithm named M-Booster, where each weak classifier is the multicategory classifier. We focused on efficient method to extract the features and update the weights of data. The label for the each category is represented by m-dimensional vector, and the weights for the feature and other parameters are also modified accordingly. We have performed simulation for the artificial data and the face data with different rotation angles. It is shown that the use of single M-Booster can solve the multicategory problems more efficiently than the method based on 2-category classifiers and previous method (Adaboost.MH) (Y. Freund and R.E. Schapire, 1997).
Fast blind equalization by using frequency domain block constant modulus algorithm

This paper presents fast algorithms for the CMA (constant modulus algorithm), which is one of the widely used blind equalization algorithms. To derive the fast algorithm of the CMA for high-speed equalization, we first introduce BCMA (block CMA) which adjusts the equalizer coefficients by block processing of the received symbols in the time domain. Based on the BCMA, we propose the FBCMA (frequency domain block CMA) which employs fast linear convolution in the DFT domain by using the overlap save method. In this paper, a non-linear error function in the frequency domain is derived using the Parseval's relation. Also, an adaptive algorithm in the DFT domain is developed to adjust the DFT domain filter coefficients. If the block size and filter length is N, the multiplications required for the conventional CMA and proposed FBCMA are in the order of O(N/sup 2/) and O(N log N), respectively. The computer simulations show that the proposed FBCMA presents comparable performance to the conventional CMA, while requiring less computations.
An image compression algorithm based on the Karhunen Loève transform

The Karhunen-Loève Transform (KLT) is the optimal transform for a block of signal in terms of decorrelation and energy compaction performances. However, it is not used in practice because we need to transmit the transform kernel for every block, which requires huge amount of side information. Hence fixed-kernel discrete cosine transform (DCT) is used instead in many of current image compression algorithms. In this paper, considering a recent trend of requiring high resolution, high quality, multiple view images, we propose an image adaptive transform (IAT) approach where the transform kernels are derived from the KLT for a group of image blocks. The contribution of this paper is to develop a simple method to group the similar image blocks according to DCT coefficient statistics, and also to reduce the number of transform kernels by using the relationship between the DCT coefficients and the corresponding block properties such as edge directions. Since this method needs some amount of side information for transmitting IAT kernels, it does not perform well for the low resolution images at low bit rates, but it outperforms JPEG for the high resolution, high quality and multiple view images that can share the same transforms.
Approach to direct coning/sculling error compensation based on the sinusoidal modelling of IMU signal

A direct coning mitigation algorithm based on the detection of a sinusoidal component in the gyro measurements is proposed. The coning error, which is one of dominant error sources affecting the accuracy of attitude computation in the inertial navigation system (INS), is induced by the sinusoidal motion such as a manoeuvre of vehicle or the vibration of a dithering of ring laser gyro. However, many researchers have developed coning error mitigation algorithms based on the higher-order polynomial model and its correction terms are extracted from the gyro output without faithful consideration of the periodicity of motion. Hence, the authors first detect and estimate the sinusoidal components from gyro measurements, and then find the higherorder correction terms from the estimated sinusoidal parameters. The authors also show that this algorithm can be applied to the mitigation of sculling error because of its duality to the coning motion. Simulation results show that the direct compensation algorithm works effectively for the navigation of vehicles with continuous and steady oscillations. Also, the rate table test with HG1700 inertial measurement unit (IMU) is conducted to show the effectiveness of proposed algorithm on real INS signal.
New intra prediction methods based on channel correlation and their combination for color image compression

This paper proposes two new intra prediction methods for color image compression, which exploit the correlation between the base and non-base colors. The first method predicts a pixel in non-base color as a weighted sum of neighboring pixels, where the weights are determined as a function of photometric distances from the pixel to the neighboring ones in the base color. The second exploits the linear relationship between the channels, where the parameters for the model are found from the already reconstructed base color, so that no side information is needed. In the implementation, it is also found that the weighted combination of new predictors gives better coding gain, where one of predictors is chosen based on the RD optimization. The experimental results show that the proposed method provides higher coding gain than the conventional H.264/AVC, up to 1.2dB for luminance and 2.3dB for the chrominance image.
A new intra prediction method using channel correlations for the H.264/AVC intra coding

This paper introduces a new intra prediction algorithm that exploits the correlations between the color channels, for the H.264/AVC intra coding. The main idea is to use the weighted sum of neighboring pixels as a new chroma prediction tool, where the weighting coefficients are determined considering the inter-channel correlations. Specifically the weighting coefficients are determined under the assumption that the similarity of a chrominance channel intensity increases as the distance of the luminance channel intensity decreases. The proposed method is added to the conventional chroma prediction modes with a little modification in the chroma prediction mode binarization. The experiments show that the proposed method provides up to 1.0dB gain for the color-abundant images.
A new method to find an optimal warping function in image stitching

In image stitching applications, it is very important to find a suitable warping function for the visual quality of a composite (stitching result). In this paper, we present a new mathematical criterion to select an optimal warping function among a set of possible candidates (e.g., parametric family). The proposed criterion can be considered as a direct view condition for image stitching , i.e., it is desirable that each part of the composite image looks like its corresponding input image. More specifically, we do not use an explicit modeling of a compositing surface, but, we focus on the differential properties of a warping function. That is we design a cost function so that the Jacobian matrix of a warping function is close to a shape preserving matrix such as rotation and reflection matrices. The proposed cost function can be effectively minimized by using Levenberg-Marquardt algorithm. The experimental results show that the proposed method results in visually pleasing stitched results because the original shape of each image is preserved in the composite.
Robust watermarking of 3D mesh models

A robust watermarking algorithm for the 3D mesh models is proposed. The algorithm is based on the watermarking of images from a virtual 3D scanner, which mimics the operation of 3D scanner in the real world. The position of the object in the scanner is determined by the principle component analysis of the vertex points. After obtaining 2D range image from the virtual scanner, we embed the watermark using the conventional 2D image watermarking method based on the DCT. Then, the vertices of the model are moved according to the range values modified by the 2D watermark. For the watermark extraction, the virtual ranging is performed and then the retrieval process of 2D image watermarking is performed. Experimental results show that the proposed algorithm is robust against the attacks such as mesh simplification and Gaussian noise.
An overlap save algorithm for block convolution with reduced complexity

We propose a block convolution algorithm that requires shorter length FFT than the conventional overlap save algorithm (OSA). It is shown that the OSA can be split into two separate processes related to the previous and current data blocks. Hence, only current data block needs to be transformed in the proposed OSA, whereas the concatenated block of previous and current data is transformed in the conventional method. As a result, the number of arithmetic operations for the block convolution is reduced. Also, the reduced transform size gives additional advantage in data manipulation when implemented on DSP and PC.
Multiple face tracking method in the wild using color histogram features

This paper presents a new framework for the tracking of multiple faces. In order to address this tracking problem in the challenging environments, we first adopt a robust face detector based on Multi-task Cascaded Convolutional Networks (MTCNN) and a very efficient tracker exploiting Kernelized Correlation Filters (KCF). Then, we incorporate the detector and tracker into our framework by proposing a new data association method. In our association scheme, we consider color histogram features as well as geometric overlaps, so that it works robustly in the presence of occlusions and crossovers. We conducted experiments on the selected examples of 300VW database and a challenging test video sequence (TMBS). Experimental results have shown that the proposed method works robustly for challenging scenarios in real time (49 fps).
Text-Line Extraction in Handwritten Chinese Documents Based on an Energy Minimization Framework

Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods.
Discrimination and description of repetitive patterns for enhancing object recognition performance

Objects with repetitive patterns are not well recognized by SIFT/SURF based matching because the features from those patterns are too similar and thus it is often difficult to find the homography of matched pairs. In this paper, we propose a new feature matching strategy to alleviate this problem by differentiating repetitive patterns from the other salient ones and also by developing a way of utilizing the patterns for robust feature matching. Specifically, we develop a classifier that tells whether the features are from repetitive patterns or salient features, based on mean shift clustering followed by support vector data description. Then the homography is found over the salient features by excluding the repetitive features at first, which is then validated and refined by the patterns. The proposed method is tested with the examples of matching the buildings with repeating patterns, and it is shown to be robuster and more reliable than the conventional ones.
On the performance analysis of ALE using an IIR lattice notch filter

An attempt is made at a quantitative understanding of the ALE (adaptive line enhancer) using an adaptive IIR (infinite impulse response) lattice notch filter. The stability of the algorithm is discussed, and an expression for the asymptotic bias of the frequency estimate is provided. The transient behavior of the algorithm is investigated. It is verified by simulation that the analysis substantiates the fast convergent properties shown by N.I. Cho et al. (1989). The cascade structure for retrieving multiple sinusoids is also discussed. The simulation results indicate that the cascade structure based on the lattice ALE algorithm provides results comparable to those described in the recent literature with much less computational complexity (i.e. O(N) versus O(N/sup 2/)), where N is the number of input sinusoids.<<ETX>>
Content-based image retrieval using color features of salient regions

This paper presents a content-based color image retrieval system based on color features from the salient regions and their spatial relationship. The proposed method first extracts the salient regions by a color contrast method, and finds several dominant colors for each region. Then, the spatial distribution of each dominant color is described as a binary map. Specifically, the salient region is partitioned into small sub-blocks, and each sub-block is assigned as 1 or 0 according to the number of pixels corresponding to the dominant color. The set of binary maps define the spatial distribution of dominant colors within and across the salient regions, which approximately reflect the objects' shapes and the spatial relationship of the objects. A simple matching method for this description is also proposed, which needs very few computations for each image matching. According to the experiments with several widely used color image databases, the proposed method shows better retrieval performance than the state-of-the-art and previous color-based methods. The proposed algorithm is suitable for color image retrieval on the web and mobile systems, because it needs very few computations which are mostly binary logical operations.
An image compression algorithm using warped discrete cosine transform

This paper introduces the concept of warped discrete cosine transform (WDCT) and an image compression algorithm based on the WDCT. The proposed WDCT is a cascade connection of a conventional DCT and all-pass filters whose parameters can be adjusted to provide frequency warping. In the proposed image compression scheme, the frequency response of the all-pass filter is controlled by a set of parameters with each parameter for a specified frequency range. For each image block, the best parameter is chosen from the set and is sent to the decoder as a side information along with the result of corresponding WDCT coefficients. It is shown that the WDCT can be implemented by a single matrix computation like the DCT. The proposed algorithm outperforms the DCT based compression, for high bit rate applications and for images with high frequency components.
DSP integration of sound source localization and multi-channel Wiener filter

This paper describes a DSP integration of sound source localization (SSL) and multi-channel Wiener filter (MWF). To develop a robot audition system, we integrated SSL module and MWF module into a DSP system. SSL is a module to perceive the direction of a human user's call. It measures time delay of arrival among microphones and estimates the direction of sound source. Also, it post-processes the resulted estimations of direction by histogram to perceive the direction robustly under noisy environment. MWF is a module to reduce background noises from raw voice signal to enhance the performance of robot's speech recognition. It gathers information of background noises during noise-period and then reduces noises during voice-period. This SSL-MWF combination system will be a cheap, high-performing and convenient solution for robot audition.
Pnoramic mosaic system for mobile devices

This paper deals with a panoramic mosaic system for mobile devices. The proposed system is optimized for mobile devices by integer-programmable algorithms and auto-shot user interface. The proposed system consists of auto-shot interface, transform onto cylindrical surface, color compensation, local alignment, image stitching, and blending. The auto-shot interface senses user's camera motion and takes pictures when the camera motion is matched to the predefined motion model. The captured images are projected onto a cylindrical mosaic surface by modified projection equation. Exposure difference is removed by color compensation, where the luminance differences between images are adjusted and color saturation is eliminated. The projected images are locally aligned by fast hierarchical hexagonal search technique. Then, the optimal boundary of overlapped images are determined using dynamic programming and synthesized seamlessly. According to the experiments using mobile devices, the proposed system shows good performance compared with other PC-based mosaic algorithms.
A new image projection method for panoramic image stitching

We propose a new image projection method in an attempt to reduce the perceptual distortion in panoramic image mosaics. Specifically, we reduce the stretching distortion of some image patches and bending of straight lines. Since the stretching distortion usually occurs when projecting a viewing sphere to the cylindrical image surface in an oblique direction, we propose to use an adjustable cylindrical surface to match the viewing direction with the equator of the cylindrical surface. Also, in order to find the trade-off between the stretching distortion and bending of straight lines, we also adjust the curvature of cylindrical surface according to the object of interest in the image. The warping function from the viewing sphere to the adjustable image surface is derived and the amount of distortion caused by this warping function is also defined. From the measure of distortion, the optimal pose of the cylindrical image plane and its curvature are determined, and the image on the viewing sphere is projected on the optimal plane. The experimental results show that the proposed method produces the panoramic image with less distortion than the existing methods.
Gaze estimation using 3-D eyeball model under HMD circumstance

This paper presents a gaze estimation algorithm using 3-D eyeball model and 2-D pupil center — inner eye corner(PC-IEC) vector. The conventional methods using feature points in the eye images need lots of calibration markers and long calibration time. However, since the pupil and gaze movements are closely related to the 3-D rotation of eyeball, the long and complicated calibrations are not necessary. This paper derives the relationship between the 3-D eyeball model and 2-D PC- IEC vector with a single reference calibration point which is located at the center of the screen. Also, the proposed algorithm compensates for the eyeball movements using the eyelid height against the inner eye corner. According to the experiment, the proposed method estimates the gaze within 2 degree error.
Graph cuts using a Riemannian metric induced by tensor voting

In this paper, we present a new algorithm that combines the advantages of tensor voting into graph cuts. Tensor voting has been a popular tool for a number of early vision problems since it can use principles of perceptual grouping, which are not well considered in graph cuts. We attempt to encode the power of tensor voting into an energy minimization framework. For this, we assume that the tensor map obtained by tensor voting induces a Riemannian metric in image domain, and the metric is constructed according to the conventional ways of tensor interpretation. Finally, by embedding the induced Riemannian metric into the graph via edge weights, the graph cuts algorithm can have priors considering principles of perceptual grouping. The proposed method can be used in the labeling of occluded regions, object segmentation using only edge information, and boundary regularization.
Error resilient video coding using optimal multiple description of DCT coefficients

This paper proposes an algorithm for the robust transmission of video in error prone environment using multiple description (MD) scheme and optimal splitting of DCT coefficients. We use the redundancy rate-distortion (RRD) criteria to split a one-layer compressed video stream into two correlated streams or descriptions. For optimal splitting, recursive structure is exploited and Lagrange optimization and dynamic programming are used. Compared to the existing RRD based methods, the proposed algorithm can find more optimal points on the RRD curve. For compliance with the standards, the proposed MD video coder is implemented based on the H.263. Hence, each description can be decoded independently by the H.263 decoder. Also, several descriptions can be decoded into a single stream by additional simple merge stage and the H.263 decoder. Simulation results show that the proposed MD video coder yields better performance than the conventional MD splitting algorithms based on the RRD criteria at all redundancy rates.
Design of perfect reconstruction QMF lattice with signed powers-of-two coefficients using CORDIC algorithm

The lattice structure has 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 filters (QMF), because the perfect reconstruction (PR) is conserved even under the severe coefficient quantization. Moreover, if lattice coefficients are implemented by signed powers-of-two (SPT), the hardware complexity can also be reduced. But the discrete space represented by the SPT is sparse when the number of non-zero bits is small. This paper proposes an orthogonal QMF lattice with SPT coefficients that can provide much denser discrete coefficient space than the conventional structure. For this purpose, we employ the CORDIC algorithm that is structurally related to the PR lattice filter with SPT coefficients. The paraunitariness of CORDIC subrotation also continues to hold the PR condition to our wishes. Since the proposed architecture provides denser coefficient space, it shows less coefficient quantization error than the conventional QMF lattice.
A hybrid motion compensation for wide and fixed field-of-view image sequences

In this paper, a rotation-aware motion compensation method for wide and fixed field-of-view(FOV) image sequences is presented. Most predictive models of conventional video compression standards such as translational motion models perform well for narrow FOV cameras like television cameras but not for wide FOV cameras. Therefore the image quality of rotationally moving scenes are significantly degraded after the compression process with bandwidth limitation. The proposed rotation-aware predictive model is designed for wide and fixed FOV cameras of mobile phones, and the main objective of the proposed method is to reduce the variance of motion vectors. Experimental results show that the proposed method reduces the variance of motion vectors while the image quality is maintained or improved.