nils-herrmann/clean_scirepeval huggingface dataset:
Color image segmentation using connected regions

A color image segmentation algorithm based on region growing is presented. Each region is characterized using two parameters: within region color contrast and between region color contrast. The first parameter is the distance between the two most distant pixels in terms of color. The second parameter is the distance between the candidate pixel and its nearest neighbor in the region. The color similarity measure used is the vector angle, which is invariant to shading. Highlight invariance is accomplished by using a highlight removal transformation, which removes the average pixel intensity from each RGB coordinate. The first calculation is very computationally intensive. To reduce this computational burden, the algorithm keeps track of which pixels already in the region are furthest spatially from the pixel being considered. The assumption would be that the pixels the furthest away would be the ones most different from the pixel being considered. We will present results on artificial and real images to illustrate the effectiveness of the method.
A Bayesian approach to spaceborn hyperspectral optical flow estimation on dust aerosols

The significant role dust aerosols play in the earth's climate system and microbial nutrition cycles have lead to increased efforts of employing remote sensing to monitor their genesis, transport and deposition. This contribution extends earlier approaches of using Bayesian hierarchical models to extract dust activity from multi-spectral MSG-SEVIRI measurements by focusing on the signal-to-noise ratio with respect to post hoc motion analysis via optical flow. While interpreting also the latter in a completely Bayesian fashion, we show that our novel dust indication scheme reduces background noise and thereby renders the optical flow more decisive in terms of detecting even faint dust plumes. As a side effect of the indicators stability in case of dust absence, we point out the potential usage of its temporal variance to characterize dust at an early stage of the genesis and thus close to the corresponding source region.
NeRD: A Neural Response Divergence Approach to Visual Saliency Detection

In this letter, a novel approach to visual saliency detection via neural response divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low-level cues that can be used to compute image region distinctiveness. Based on this concept, an efficient visual saliency detection framework is proposed using deep convolutional StochasticNets. Experimental results using complex scene saliency dataset and MSRA10k natural image datasets show that the proposed NeRD approach can achieve improved performance when compared to state-of-the-art image saliency approaches, while attaining low computational complexity necessary for near-real-time computer vision applications.
Extended-Hungarian-JPDA: Exact Single-Frame Stem Cell Tracking

The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.
Autocalibration: Finding Infinity in a Projective Reconstruction

In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associated with auto calibration is the nonlinear optimization of a cost function based on extracting camera intrinsics from a potential upgrading transform, and evaluating fitness with respect to prior knowledge of physical cameras. The nonlinearity of the problem leads, in general, to poor convergence and a failure of the calibration process. This paper presents a novel auto calibration pipeline that seeks to develop a more robust approach to the nonlinear optimization. After testing a variety of methods, none of which yielded satisfactory solutions, we have developed a strategy combining the best aspects of two methods representing the current state of the art. The former method preconditions the projective space by ensuring it is quasi-affine with respect to camera centers, allowing a naive initialization in the new space, and uses a fitness measure resistant to focal length collapse. The latter method initializes using the best results of an exhaustive search over reasonable values of focal length. Our novel approach, presented here, uses the exhaustive search initialization of the latter combined with the improved fitness measure of the former, producing results that outperform both of its predecessors.
Phase-adaptive image signal fusion using complex-valued wavelets

This paper presents a novel method for medical image signal fusion using complex-valued wavelets to enhance the information content in the fused signal from a perceptual manner. The proposed method introduces an adaptively weighted aggregation of signal characteristics based on the phase characteristics of medical image signals. The proposed method exploits the phase characteristics of the image signals to adaptively accentuate important anatomical and functional characteristics captured by each image signal during the signal fusion process. Experimental results show that the proposed method can improve the visualization of important anatomical and functional characteristics from different medical image signals in the fused image signal when compared with non-adaptive image signal fusion methods.
A general multiresolution approach to the estimation of dense fields in remote sensing

A fast multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This multiscale algorithm produces solution and error estimates which are consistent with those obtained from exact least-squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least-squares, that is problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. Contrary to methods previously proposed for solving large least-squares problems, the multiscale approach provides error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations.
Large scale dynamic estimation of ocean surface temperature

This paper addresses the dynamic estimation of the ocean surface temperature based on data from the Along-Track Scanning Radiometer (ATSR) for large (512/spl times/512) fields. For such huge problems, the conventional dynamic estimation tool (the Kalman filter) is not directly applicable, instead, we develop a recursive estimation algorithm that emulates the Kalman filter. Our approach uses a multiscale estimation algorithm for the update step, and makes simplifying assumptions about the surface dynamics leading to a computationally efficient prediction step.
Correlated non-linear wavelet shrinkage

This paper examines non-linear shrinkage methods specifically taking into account the correlation structure of the multiresolution wavelet coefficients. In contrast to hidden Markov trees, which model the relationship of wavelet variance from scale to scale, here we wish to take advantage of coefficient correlation. A linear shrinkage based on the LLS (Linear Least Square) estimator, employing a sample correlation scheme, is tested and verified to have an aesthetic denoising performance. Then, state-of-the-art independent shrinkage functions are applied to exploit the efficiency of such techniques and to introduce non-linearity into the algorithm to compensate for non-Gaussianity of the wavelet statistics. The performance of the non-linear shrinkage technique, as used individually and together with the linear correlated approach, are illustrated.
Hierarchical methods for global-scale estimation problems

There is a substantial signal-processing challenge associated with large-scale (especially global-scale) remote-sensing problems: solving the statistical inverse problem (i.e. deducing the properties of the sensed field from measurements) by brute force, that is by covariance matrix inversion, is completely impractical for fields involving millions of pixels. This paper reports on the ongoing development of an alternative technique, in which the statistical problem is modeled on a multiscale tree, applied to estimating sea-surface temperature (SST) based on infrared radiance observations from the along-track scanning radiometers (ATSRs).
Hierarchical posterior sampling for images and random fields

The estimation of images and random fields from sparse and/or noisy data is highly-developed, to the point where methods such as least-squares estimation, simulated annealing, and wavelet shrinkage are quite standardized. The key problem, however, is that the estimates are not a realistic version of the random field, and do not represent a typical or representative sample of the system being studied. Instead, what is often desired is that we find a random sample from the posterior distribution, a much more subtle and difficult problem than estimation. Typically this is solved using Markov-Chain Monte-Carlo / simulated annealing approaches, however these may be computationally challenging and slow to converge. In this paper we use hierarchical models to formulate a novel, fast posterior sampler.
Synthesis of Remote Sensing Label Fields Using a Tree-Structured Hierarchical Model

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.
Adaptive Monte Carlo Retinex Method for Illumination and Reflectance Separation and Color Image Enhancement

A novel stochastic Retinex method based on adaptive Monte Carlo estimation is presented for the purpose of illumination and reflectance separation and color image enhancement. A spatially-adaptive sampling scheme is employed to generate a set of random samples from the image field. A Monte Carlo estimate of the illumination is computed based on the Pearson Type VII error statistics of the drawn samples. The proposed method takes advantage of both local and global contrast information to provide better separation of reflectance and illumination by reducing the effects of strong shadows and other sharp illumination changes on the estimation process, improving the preservation of the original photographic tone, and avoiding the amplification of noise in dark regions. Experimental results using monochromatic face images under different illumination conditions and low-contrast chromatic images show the effectiveness of the proposed method for illumination and reflectance separation and color image enhancement when compared to existing Retinex and color enhancement techniques.
Mapping and pseudoinverse algorithms for ocean data assimilation

Among existing ocean data assimilation methodologies, reduced-state Kalman filters are a widely studied compromise between resolution, optimality, error specification, and computational feasibility. In such reduced-state filters, the measurement update takes place on a coarser grid than that of the general circulation model (GCM); therefore, these filters require mapping operators from the GCM grid to the reduced state and vice versa. The general requirements are that the state-reduction and interpolation operators be pseudoinverses of each other, that the coarse state define a closed dynamical system, that the mapping operations be insensitive to noise, and that they be appropriate for regions with irregular coastlines and bathymetry. In this paper, we describe three efficient algorithms for computing the pseudoinverse: a fast Fourier transform algorithm that serves for illustration purposes, an exact implicit method that is recommended for most applications, and an efficient iterative algorithm that can be used for the largest problems. The mapping performance of 11 interpolation kernels is evaluated. Surprisingly, common kernels such as bilinear, exponential, Gaussian, and sinc perform only moderately well. We recommend instead three kernels, smooth, thin-plate, and optimal interpolation, which have superior properties. This study removes the computational bottleneck of mapping and pseudoinverse algorithms and makes possible the application of reduced-state filters to global problems at state-of-the-art resolutions.
Median Robust Extended Local Binary Pattern for Texture Classification

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
Fusing Sorted Random Projections for Robust Texture and Material Classification

This paper presents a conceptually simple, and robust, yet highly effective, approach to both texture classification and material categorization. The proposed system is composed of three components: 1) local, highly discriminative, and robust features based on sorted random projections (RPs), built on the universal and information-preserving properties of RPs; 2) an effective bag-of-words global model; and 3) a novel approach for combining multiple features in a support vector machine classifier. The proposed approach encompasses the simplicity, broad applicability, and efficiency of the three methods. We have tested the proposed approach on eight popular texture databases, including Flickr Materials Database, a highly challenging materials database. We compare our method with 13 recent state-of-the-art methods, and the experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for Columbia-Utrecht, 97.16% for Brodatz, 99.30% for University of Maryland Database, and 99.29% for Kungliga Tekniska högskolan-textures under varying illumination, pose, and scale. Moreover, the proposed approach significantly outperforms the current state-of-the-art approach in materials categorization, with an improvement to classification accuracy of 67%.
A Novel Hierarchical Model-Based Frame Rate Up-Conversion via Spatio-temporal Conditional Random Fields

In this paper, a hierarchical model-based approach to frame rate-up conversion is presented. Given a sequence of consecutive video frames, a Spatio-Temporal Conditional Random Field (ST-CRF) is trained to capture both the motion and shape characteristics of objects within consecutive frames. A hierarchical tree is then constructed via hierarchical segmentation that sub-divides frames into regions based on color intensity and regional velocity. A hierarchical sampling approach is then introduced to construct new intermediate frames between adjacent video frames, where estimated intermediate frames are constructed at each level of a hierarchical tree constructed such that the probability of the ST-CRF is maximized. Preliminary results using videos with different motion characteristics show that the proposed approach has potential for producing intermediate frames with high visual quality.
Robust snake convergence based on dynamic programming

The extraction of contours using deformable models, such as snakes, is a problem of great interest in computer vision, particular in areas of medical imaging and tracking. Snakes have been widely studied and many methods are available. In most cases, the snake converges towards the optimal contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. This approach is elegant, but frequently mis-converges in the presence of noise or complex contours. To address these limitations, a novel discrete snake is proposed which treats the two energy terms separately. Essentially, the proposed method is a deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a Hidden Markov Model (HMM) and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted. The proposed implementation is straightforward and achieves dramatic speed and accuracy improvement compared to other methods.
Adaptive Wiener filtering of noisy images and image sequences

In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.
A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh resolution optical coherence images of rat retina

Abnormal changes in choroidal blood flow have been linked to various retinal diseases, such as Diabetic Retinopathy (DR) and Age related Macular Degeneration (AMD), which at later stages can lead to blindness. Therefore non-invasive and precise evaluation of choroidal blood flow can aid the diagnosis, treatment and monitoring of retinal disease progression. Doppler Optical Coherence Tomography is an imaging technique capable of measuring blood flow velocity and visualization of retinal and choroidal blood vessles. However accurate assesment of retinal and choroidal blood flow requires precise measurement of the blood vessel thickness. The presence of speckle noise and low image contrast of OCT tomograms makes this task very challenging. This paper proposes a cellular automata based semi-automatic algorithm for the segmentation of choroidal blood vessels. The proposed approach propagates user-defined points in order to identify the vessel boundaries, allowing a thickness profile to be extracted. The performance of the algorithm was tested on a series of retinal images acquired from living rats with a high speed, ultrahigh resolution OCT system (UHROCT). Experimental results show that the proposed approach provides precise thickness profiles even in the suboptimal conditions of low image contrast in the UHROCT images.
An overlapping tree approach to multiscale stochastic modeling and estimation

Recently, a class of multiscale stochastic models has been introduced in which random processes and fields are described by scale-recursive dynamic trees. A major advantage of this framework is that it leads to an extremely efficient, statistically optimal algorithm for least-squares estimation. In certain applications, however, estimates based on the types of multiscale models previously proposed may not be adequate, as they have tended to exhibit a visually distracting blockiness. We eliminate this blockiness by discarding the standard assumption that distinct nodes on a given level of the multiscale process correspond to disjoint portions of the image domain; instead, we allow a correspondence to overlapping portions of the image domain. We use these so-called overlapping-tree models for both modeling and estimation. In particular, we develop an efficient multiscale algorithm for generating sample paths of a random field whose second-order statistics match a prespecified covariance structure, to any desired degree of fidelity. Furthermore, we demonstrate that under easily satisfied conditions, we can "lift" a random field estimation problem to one defined on an overlapped tree, resulting in an estimation algorithm that is computationally efficient, directly produces estimation error covariances, and eliminates blockiness in the reconstructed imagery without any sacrifice in the resolution of fine-scale detail.