nils-herrmann/clean_scirepeval huggingface dataset:
A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting

In garment manufacturing, an automatic sewing machine is desirable to reduce cost. To accomplish this, a high-speed vision system is required to track fabric motions and recognize repetitive weave patterns with high accuracy, from a microperspective near a sewing zone. In this letter, we present an innovative framework for real-time texture tracking and weave pattern recognition. Our framework includes a module for motion estimation using blob detection and feature matching. It also includes a module for lattice detection to facilitate the weave pattern recognition. Our lattice-detection algorithm utilizes blob detection and template matching to assess pair-wise similarity in blobs’ appearance. In addition, it extracts information of dominant orientations to obtain a global constraint in the topology. By incorporating both constraints in the appearance similarity and the global topology, the algorithm determines a lattice that characterizes the topological structure of the repetitive weave pattern, thus allowing for thread counting. In our experiments, the proposed thread-based texture tracking system is capable of tracking denim fabric with high accuracy (e.g., 0.03$^{\circ }$ rotation and 0.02 weave-thread translation errors) and high speed (3 frames per second), demonstrating its high potential for automatic real-time textile manufacturing.
Depth map estimation in DIBR stereoscopic 3d videos using a combination of monocular cues

We propose a method to reconstruct the depth map from multiple estimated depth maps relying on monocular cues. Based on extracted depth cues from luminance, chrominance, motion and texture, we obtain an optimal depth estimation by analytically deriving the best combinations. We first analyze a ground truth depth map to extract a set of depth cues. Then, using these depth cues, we process the colored reference video to reconstruct the depth map. We tested this approach on different video sequences with different monocular properties. The results show that the extracted depth maps generate a 3D video with quality close to the video rendered using the ground truth depth map. We report subjective and objective results using 3VQM.
Boosting in image quality assessment

In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. On the contrary of existing studies that propose a single quality estimator, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion studies, we propose using neural networks in the boosting. To span different types of image quality assessment algorithms, we use quality estimators based on fidelity, perceptually-extended fidelity, structural similarity, spectral similarity, color, and learning. In the experiments, we perform k-fold cross validation using the LIVE, the multiply distorted LIVE, and the TID 2013 databases and the performance of image quality assessment algorithms are measured via accuracy-, linearity-, and ranking-based metrics. Based on the experiments, we show that boosting methods generally improve the performance of image quality assessment and the level of improvement depends on the type of the boosting algorithm. Our experimental results also indicate that boosting the worst performing quality estimator with two or more methods lead to statistically significant performance enhancements independent of the boosting technique and neural network-based boosting outperforms support vector machine-based boosting when two or more methods are fused.
3TP: an application-Layer protocol for streaming 3-D models

This paper addresses the problem of streaming progressively compressed three-dimensional (3-D) models over lossy networks. Out of all encoded packets that can be transmitted, we intelligently choose a subset of packets to be transmitted using transport control protocol in order to meet a distortion constraint, while transmitting the remaining packets using user datagram protocol to minimize the end-to-end delay. We call this new application-layer protocol 3-D models transport protocol. We show the effectiveness of this protocol both experimentally and theoretically. We compare the performance of the proposed protocol with systems that do not optimize transmission according to the content of the encoded bitstream. When the maximum distortion is 30, measured using the Hausdorff distance, we achieve savings in delay time ranging from 39% to 68% for packet-loss rates between 1% and 19%.
Completed local derivative pattern for rotation invariant texture classification

In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative pattern, reflects the directional smoothness of local textures without increasing computation complexity. Experimental results on the Outex database show that CLDP, as a uni-scale pattern, outperforms uni-scale state-of-the-art texture descriptors on texture classification and has comparable performance with multi-scale texture descriptors.
Hybrid Variable Length Coding for Image and Video Compression

The conventional run-level variable length coding (RL-VLC), commonly adopted in block-based image and video compression to code quantized transform coefficients, is not efficient in coding consecutive nonzero coefficients. To overcome the deficiency, hybrid variable length coding (HVLC) is proposed in this paper. HVLC takes advantage of the clustered nature of nonzero transform coefficients in the low-frequency (LF) region and the scattered nature of nonzero transform coefficients in the high-frequency (HF) region by employing two types of VLC schemes. A novel two-dimensional position and one-dimensional amplitude coding scheme is proposed to code the LF coefficients while RL-VLC or an equivalent VLC scheme is retained to code the HF coefficients. Experimental results show that HVLC greatly favors the coding of high-resolution, high-complexity scenes, while it preserves low computational complexity.
Phase Congruency for image understanding with applications in computational seismic interpretation

Phase Congruency (PC) can highlight small discontinuities in images with varying illumination and contrast using the congruency of phase in Fourier components. PC can not only detect the subtle variations in the image intensity but can also highlight the anomalous values to develop a deeper understanding of the images content and context. In this paper, we propose a new method based on PC for computational seismic interpretation with an application to subsurface structures delineation within migrated seismic volumes. We show the effectiveness of the proposed method as compared to the edge- and texture-based methods for salt domes boundary detection. The subjective and objective evaluation of the experimental results on the real seismic dataset from the North Sea, F3 block show that the proposed method is not only computationally very efficient but also outperforms the state of the art methods for salt dome delineation.
Fault detection using color blending and color transformations

In the field of seismic interpretation, univariate data-based maps are commonly used by interpreters, especially for fault detection. In these maps, the contrast between target regions and the background is one of the main factors that affect the accuracy of interpretation. Since univariate data-based maps are not capable of providing a high-contrast representation, to overcome this issue, we turn them into multivariate data-based representations using color blending. We blend neighboring time sections or frames that are viewed in the time direction of migrated seismic volumes as if they corresponded to the red, green, and blue channels of a color image. Furthermore, to extract more reliable structural information, we apply color transformations. Experimental results show that the proposed method improves the accuracy of fault detection by limiting the average distance between detected fault lines and the ground truth into one pixel.
Cooperative Delivery Techniques to Support Video-on-Demand Service in IPTV Networks

In this paper, we study the use of peer-assisted server-based cooperative transmission strategies in IPTV networks for the delivery of on-demand services to end users. The proposed techniques aim to support the resource efficient delivery of on-demand content to end users in a timely manner. Within the proposed framework, a cooperative transmission strategy suggests that users who have access to the requested content cooperatively transmit to the targeted set of users. In doing so, we can minimize the servicing requirements at the server side and improve the scalability performance in the network. We conducted extensive simulations and showed that significant performance improvements can be achieved with the proposed delivery techniques to enable efficient access to an ever growing on-demand content. We also showed the robustness of the proposed techniques in regard to variations observed in network state.
Graph matching for efficient classifiers adaptation

In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the labeled samples in the first are projected into the second domain, thus allowing the application of any classifier in the transformed domain. Experiments on VHR series of images show the validity of the proposed method to adapt the classifiers to related domains.
Dimensionality reduction via regression on hyperspectral infrared sounding data

This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular. We illustrate the performance of the algorithm in reducing the dimensionality of IASI hyperspectral image sounding data. We compare DRR with related and invertible methods such as linear PCA and Principal Polynomial Analysis (PPA) in terms of reconstruction error, and expressive power of the extracted features to estimate atmospheric variables.
Recovering wavelet relations using SVM for image denoising

Here we propose an alternative non-explicit way to take into account the relations among wavelet coefficients in natural images for denoising: we use support vector machines (SVM) to learn these relations. Since relations among the coefficients are specific to the signal, SVM regularization removes the noise, which does not share this property. Moreover, due to its non-parametric nature, the method can eventually cope with different noise sources. The results show that: (1) the proposed non-parametric method outperforms conventional methods that assume coefficient independence, and (2) its performance is similar to state-of-the-art parametric methods that do explicitly include these relations. Therefore, the proposed machine learning approach can be seen as a more flexible (model-free) alternative to the explicit description of wavelet coefficient relations in Bayesian approaches.
Perceptual regularization functionals for natural image restoration

Regularization constraints are necessary in inverse problems such as image restoration, optical flow computation or shape from shading to avoid the singularities in the solution. Conventional regularization techniques are based on some a priori knowledge of the solution: usually, the solution is assumed to be smooth according to simple statistical image or motion models. Using the fact that human visual perception is adapted to the statistics of natural images and sequences, the class of regularization functionals proposed in this work are not based on an image model but on a model of the human visual system. In particular, the current nonlinear model of early human visual processing is used to obtain locally adaptive regularization functionals for image restoration without any a priori assumption on the image or the noise. The results show that these functionals constitute a valid alternative to those based on the local autocorrelation of the image.
Dimensionality Reduction via Regression in Hyperspectral Imagery

This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize principal component analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between the PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error. More importantly, unlike other nonlinear dimensionality reduction methods, the invertibility, volume-preservation, and straightforward out-of-sample extension, makes DRR interpretable and easy to apply. The properties of DRR enable learning a more broader class of data manifolds than the recently proposed non-linear principal components analysis (NLPCA) and principal polynomial analysis (PPA). We illustrate the performance of the representation in reducing the dimensionality of remote sensing data. In particular, we tackle two common problems: processing very high dimensional spectral information such as in hyperspectral image sounding data, and dealing with spatial-spectral image patches of multispectral images. Both settings pose collinearity and ill-determination problems. Evaluation of the expressive power of the features is assessed in terms of truncation error, estimating atmospheric variables, and surface land cover classification error. Results show that DRR outperforms linear PCA and recently proposed invertible extensions based on neural networks (NLPCA) and univariate regressions (PPA).
Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.
Adaptive kernel ridge regression for image denoising

The standard (Bayesian) methods for image denoising involve explicit (analytic) models of signal and noise. The performance of these parametric approaches critically depend on using realistic models, but accurate models may ruin analytical tractability. Recently, an alternative nonparametric method was successfully proposed in . The method was based on developing stationary support vector regression (SVR) models in the wavelet domain according to prior knowledge about signal and noise features. Nevertheless, off-line analysis of the particular signal and noise statistics is required to apply it to different problems. In this work, we take a similar non-parametric approach, but we explore the ability of kernel ridge regression (KRR) to locally follow image and noise characteristics, thus trivially obtaining adaptive (non-stationary) description of the image. Making KRR adaptive alleviates the strong assumption of Gaussianity of the noise. The method is embedded in the iterative restoration framework that allows consistent parameter tuning. Promising results are obtained with a model straightforwardly formulated in the spatial domain for different noise sources.
Nonlinear data description with Principal Polynomial Analysis

Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property to evaluate performance in dimensionality reduction. Successful performance of the proposed PPA is illustrated in dimensionality reduction, in compact representation of non-Gaussian image textures, and multispectral image classification.
Variable-size block matching algorithm for motion estimation using a perceptual-based splitting criterion

A variable-size block matching technique for motion estimation under the framework of perceptual coding is presented. It is well-known that fixed size block matching can be improved by using a multiresolution approach. It is shown that further improvement is possible both in terms of compactness and robustness of the predicted motion field if a perceptual criterion is used. This scheme is compared against other variable- and fixed-size block matching algorithms both from the point of view of final complexity of motion description and its robustness.
Kernel Structural SIMIlarity on hyperspectral images

In this paper, we introduce a non-linear and multidimensional generalization of the Structural SIMilarity index (SSIM) for quality assessment of hyperspectral images. We exploit well-known properties of functional analysis and estimate means, variances, and correlation in proper reproducing kernel Hilbert spaces (rkHs). The so-called Kernel SSIM (KSSIM) is shown to generalize the conventional SSIM and the recently introduced Q4 and Qn metrics for remote sensing applications, and naturally works with multidimensional images. For the experimentation, we built a database of different distortions commonly encountered in remote sensing images. KSSIM shows an improved agreement with classification results compared to standard similarity metrics, and high consistency for different noise sources and levels.
Principal polynomial analysis for remote sensing data processing

Inspired by the concept of Principal Curves, in this paper, we define Principal Polynomials as a non-linear generalization of Principal Components to overcome the conditional mean independence restriction of PCA. Principal Polynomials deform the straight Principal Components by minimizing the regression error (or variance) in the corresponding orthogonal subspaces. We propose to use a projection on a series of these polynomials to set a new nonlinear data representation: the Principal Polynomial Analysis (PPA). We prove that the dimensionality reduction error in PPA is always lower than in PCA. Lower truncation error and increased independence suggest that unsupervised PPA features can be better suited to image classification than those identified by other unsupervised techniques. We analyze the performance of Linear Discriminant Analysis in the feature space after dimensionality reduction using the proposed PPA, the classical PCA, and locally linear embedding (LLE). Experiments on very high resolution data confirm the suitability of PPA to describe nonlinear manifolds found in remote sensing data.
Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.
Bit allocation algorithm for codebook design in vector quantisation fully based on human visual system nonlinearities for suprathreshold contrasts

A fully perceptual oriented approach for codebook design in the vector quantisation of images for compression purposes is presented. The proposed algorithm not only exploits the usually considered dependence on frequency of the human eye, but also makes use of the nonlinear behaviour of the visual system for suprathreshold contrasts. From experimental data of human contrast incremental thresholds of sinusoidal grids, we present a perceptually optimal information allocation function. Simulations show that by introducing perceptual contrast nonuniformities, a more efficient bit allocation is attained than that for the JPEG-like contrast uniform algorithm.
Splitting criterion for hierarchical motion estimation based on perceptual coding

A new entropy-constrained motion estimation scheme using variable-size block matching is proposed. It is known that fixed-size block matching as used in most video codec standards is improved by using a multiresolution or multigrid approach. In this work, it is shown that further improvement is possible in terms of both the final bit rate achieved and the robustness of the predicted motion field if perceptual coding is taken into account in the motion estimation phase. The proposed scheme is compared against other variable- and fixed-size block matching algorithms.