nils-herrmann/clean_scirepeval huggingface dataset:
On the N-dimensional extension of the discrete prolate spheroidal window

The optimal one-dimensional (1-D) window, an index-limited sequence with maximum energy concentration in a finite frequency interval, is related to a particular discrete prolate spheroidal sequence. This letter presents the N-dimensional (N-D) extension, i.e., the N-D window with limited support and maximum energy concentration in a general nonseparable N-D passband. These windows can be applied to multidimensional filter design and the design of optimal convolution functions. We show the three-dimensional (3-D) optimal window based on a rhombic dodecahedron as a passband region.
Two-stage denoising method for hyperspectral images combining KPCA and total variation

This paper presents a two-stage denoising method for hyper-spectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorre-lated noise. In the second stage, the information content is largely separated from the remaining noise by means of principal component analysis (PCA). The remaining noise is then efficiently removed by fast primal-dual TV denoising in low-energy PCA channels. Experimental results on simulated and real HSIs are very encouraging.
Taking Optimal Advantage of Fine Spatial Resolution: Promoting partial image reconstruction for the morphological analysis of very-high-resolution images

Diverse sensor technologies have allowed us to measure different aspects of objects on Earth's surface [such as spectral characteristics in hyperspectral images and height in light detection and ranging (LiDAR) data] with increasing spectral and spatial resolutions. Remote-sensing images of very high geometrical resolution can provide a precise and detailed representation of the monitored scene. Thus, the spatial information is fundamental for many applications. Morphological profiles (MPs) and attribute profiles (APs) have been widely used to model the spatial information of very-high-resolution (VHR) remote-sensing images. MPs are obtained by computing a sequence of morphological operators based on geodesic reconstruction. However, both morphological operators based on geodesic reconstruction and attribute filters (AFs) are connected filters and, hence, suffer the problem of leakage (i.e., regions related to different structures in the image that happen to be connected by spurious links are considered as a single object). Objects expected to disappear at a given stage remain present when they connect with other objects in the image. Consequently, the attributes of small objects are mixed with their larger connected objects, leading to poor performances on postapplications (e.g., classification).
A comparison on multiple level features for fusion of hyperspectral and LiDAR data

Remote sensed images contain a wealth of information. Next to diverse sensor technologies that allow us to measure different aspects of objects on the Earth (spectral characteristics in hyperspectral (HS) images, height in Light Detection And Ranging (LiDAR) data), we also have advanced image processing algorithms that have been developed to mine relevant information from multisensor remote sensing data for Earth observation. However, automatic interpretation of remote sensed images is still very difficult. In this paper, we compare multiple level features for fusion of HS and LiDAR data for urban area classification. Experimental results on fusion of HS and LiDAR data from the 2013  Data Fusion Contest demonstrate that middle-level morphological attribute features outperform high-level deep learning features. Compared to the methods using raw data fusion and deep learning fusion, with the graph-based fusion method , overall classification accuracies were improved by 8%.
A Distributed Coding-Based Content-Aware Multi-View Video System

Compared to traditional mono-view systems, stereo or in general multi-view systems provide interesting additional information about a captured scene, which can significantly facilitate content extraction. This property makes them very useful for many emerging applications, such as 3D TV and video surveillance. However, the use of such systems has been limited so far because of the processing time and bandwidth requirements for multi-view data. These major drawbacks can only be relieved by the development of dedicated algorithms. In this paper, we present an efficient, flexible and content-aware coding method for a multi-view video system. The framework consists of a central processor and camera, completed by a flexible number of smart Wyner-Ziv cameras. The latter ones provide a content-aware representation of their viewpoint, thus greatly reducing the amount of data to be sent to the central processor. By employing Distributed Video (DV) coding, i.e. joint decoding of the independently encoded frames of the different cameras, we achieve good coding efficiency without inter-camera communication.
Lossless compression of pre-press images using linear color decorrelation

In the pre-press industry color images have both a high spatial and a high color resolution. Such images require a considerable amount of storage space and impose long transmission times. Data compression is desired to reduce these storage and transmission problems. Because of the high quality requirements in the pre-press industry only lossless compression is acceptable. Most existing lossless compression schemes operate on gray-scale images. In this case the color components of color images must be compressed independently. However, higher compression ratios can be achieved by exploiting inter-color redundancies. We present a new technique for exploiting inter-component redundancies. The technique is based on a modified Karhunen-Loeve transform (KLT) scheme in combination with a novel quantization scheme that guarantees losslessness. The KLT decorrelates the color components. It is recomputed on a block by block basis and is therefore spatially adaptive. Spatial redundancies are removed using predictive techniques (lossless JPEG predictor no. 7 and the CALIC-predictor). The data which remains after the (spatial and color) decorrelation should be entropy-coded, but in the current implementation of our scheme only the entropy of the remaining data is computed. Note that in each block some block-dependent information must be sent, such as entropy-coder initialization information and KLT-descriptors (i.e., the rotation angles of the orthogonal KLT-matrix).
An improved fuzzy clustering approach for image segmentation

Fuzzy clustering techniques have been widely used in automated image segmentation. However, since the standard fuzzy c-means (FCM) clustering algorithm does not consider any spatial information, it is highly sensitive to noise. In this paper, we present an extension of the FCM algorithm to overcome this drawback, by incorporating spatial neighborhood information into a new similarity measure. We consider that spatial information depends on the relative location and features of the neighboring pixels. The performance of the proposed algorithm is tested on synthetic and real images with different noise levels. Experimental quantitative and qualitative segmentation results show that the proposed method is effective, more robust to noise and preserves the homogeneity of the regions better than other FCM-based methods.
Efficient design of a low redundant Discrete Shearlet Transform

Recently, there has been a huge interest in multiresolution representations that also perform a multidirectional analysis. The Shearlet transform provides both a multiresolution analysis (such as the wavelet transform), and at the same time an optimally sparse image-independent representation for images containing edges. Existing discrete implementations of the Shearlet transform havemainly focused on specific applications, such as edge detection or denoising, and were not designed with a low redundancy in mind (the redundancy factor is typically larger than the number of orientation subbands in the finest scale). In this paper, we present a novel design of a Discrete Shearlet Transform, that can have a redundancy factor of 2.6, independent of the number of orientation subbands, and that has many interesting properties, such as shift-invariance and self-invertability. This transform can be used in a wide range of applications. Experiments are provided to show the improved characteristics of the transform.
Bit-plane stack filter algorithm for focal plane processors

This work presents a novel parallel technique to implement stack morphological filters for image processing. The method relies on applying the image bitwise decomposition to manipulate the grayscale image at a bit-plane level, while simple logical operations and Positive Boolean Functions (PBF's) are executed in parallel to derive the transformed bit-planes. The relationship between the bitwise and threshold decomposition is closely investigated and analysed, which lead us to derive an algorithm whose control flow is full binary encoded. Furthermore, the algorithm exhibits an interesting performance, which depends on the image histogram thanks to its hierarchical processing and the study of the relationship among binary decompositions.
Feature Extraction of Hyperspectral Images With Semisupervised Graph Learning

We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised graph, we connect labeled samples according to their label information and unlabeled samples by their nearest neighborhood information. By sorting the mean distance between a unlabeled sample and labeled samples of each class, we connect the unlabeled sample with all labeled samples belonging to its nearest neighborhood class. Moreover, the proposed SEGL better models the actual differences and similarities between samples, by setting different weights to the edges of connected samples. Experimental results on four real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.
Combined non-local and multi-resolution sparsity prior in image restoration

In the field of image denoising, the non-local means (NLMS) filter is a conceptually simple, yet powerful technique. This filter exploits non-local, i.e. spatially repetitive, structure in natural images to estimate noise-free structure. In contrast, a wide variety of image restoration problems have been solved exploiting local smoothness of natural images, e.g. by enforcing sparsity of images when subjected to a multi-resolution transform. In this paper we introduce the prior knowledge of non-local repetitiveness of image structures into a broad multi-resolution image restoration framework. The proposed framework allows the power of the NLMS filter, supplemented by multi-resolution sparsity, to be extended for a wide variety of image restoration problems, such as demosaicing, deconvolution, reconstruction from insufficient measurements,... in a conceptually simple way.
Image fusion using blur estimation

In this paper, a new wavelet based image fusion method is proposed. In this method, the blur levels of the edge points are estimated for every slice in the stack of images. Then from corresponding edge points in different slices, the sharpest one is brought to the final image and others are eliminated. The intensities of non-edge pixels are assigned by the slice of its nearest neighbor edge. Results are promising and outperform other methods in most cases of the tested methods.
Adaptive contour coding using warped polynomials

This paper presents a new method for compactly representing contours. The new method is a generalization of the Fourier descriptor (FD) method. Like the FD method it approximates a complex-valued parameter representation of the contour by a trigonometric polynomial (TP). In the FD method, the TP is the best least mean squares approximation of the parameter representation. However, the mean squared error is not a good a criterion for judging the accuracy of a contour approximation. The paper proposes a better criterion and a method for computing the corresponding optimal TP. It shows that computing this optimal TP is equivalent to fitting a so-called warped TP to the parameter representation in the least mean squares sense. The paper presents preliminary results which show that the new method produces more accurate contour approximations than FD approximations of the same degree.
A comparison of DCT-like transform coders for medical images

Compression of medical images is of great importance in the implementation of systems such as PACS (Picture Archival and Communication System) and teleradiology. Significant compression is only achievable by lossy methods, though distortion must be kept low to preserve all the diagnostic information. The JPEG lossy compression standard has some shortcomings, in particular block distortion. We investigated two block-based DCT transform techniques which tend to suppress the block effect, the DCT/DST coder and the LOT coder, and compared them with JPEG and an ordinary DCT coder for different types of medical images. The results show that the LOT coder performs best at all compression ratios for all kinds of investigated medical images. At very high image quality, JPEG is preferable for it requires almost the same bit rate as the LOT and is computationally less demanding.
Fusion of multi-scale hyperspectral and lidar features for tree species mapping

The added value of multiple data sources on tree species mapping has been widely analyzed. In particular, fusion of hyperspectral (HS) and LiDAR sensors for forest applications is a very hot topic. In this paper, we exploit the use of multi-scale features to fuse HS and LiDAR data for tree species mapping. Hyperspectral data is obtained from the APEX sensor with 286 spectral bands. LiDAR data has been acquired with a TopoSys sensor Harrier 56 at full waveform. We generate multi-scale features on both HS and LiDAR data, by considering the diameter and the height layer of different tree species. Experimental results on a forested area in Belgium demonstrate the effectiveness of using multi-scale features for fusion of HS image and LiDAR data both visually and quantitatively.
Image Denoising Using Quadtree-Based Nonlocal Means With Locally Adaptive Principal Component Analysis

In this letter, we present an efficient image denoising method combining quadtree-based nonlocal means (NLM) and locally adaptive principal component analysis. It exploits nonlocal multiscale self-similarity better, by creating sub-patches of different sizes using quadtree decomposition on each patch. To achieve spatially uniform denoising, we propose a local noise variance estimator combined with denoiser based on locally adaptive principal component analysis. Experimental results demonstrate that our proposed method achieves very competitive denoising performance compared with state-of-the-art denoising methods, even obtaining better visual perception at high noise levels.
Despeckling SAR images using wavelets and a new class of adaptive shrinkage estimators

We propose an efficient and fast wavelet based technique for speckle removal from SAR images. It relies on realistic distributions of the wavelet coefficients which represent mainly speckle noise on the one hand and those that represent the useful signal corrupted by speckle on the other. We propose analytic models for these distributions, and compute their parameters automatically from a given SAR image. The resulting algorithm strongly suppresses speckle, while preserving image details and sharpness.
Spatio-Temporal Approach for Noise Estimation

We propose an efficient and accurate wavelet based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio-temporal image sequence content, by a novel correction function. The main application of the proposed algorithm is for the estimation of the stationary Gaussian noise in wavelet based video processing, for which we show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio-temporal content and noise level. Furthermore, we adapt the algorithm for local noise estimation and test its performance
Static Fourier transform hyperspectral imaging polarimeter

Hyperspectral imaging technique has been widely used in remote sensing, surveillance in agriculture, environmental monitoring, etc. However, spectral information alone cannot well discriminate objects made with the same material. Conventional methods either fuse complementary information from other sensors or mine relevant information from the original hyperspectral data to improve the recognition rations. It may increase the cost and reduce the efficiency. In this paper, we propose an easier alternative approach: we present the prototype of a compact static Fourier transform hyperspectral imaging polarimeter, which couples hyperspectraland polarization imaging in a unified instrument and allows better material discrimination. The instrument, which is formed by cascading two crystal retarders and a birefringent interferometer, offers significant advantages over traditional implementations. Specifically, without any internal moving parts or electronics controllable elements, the spectrum, full wavelength dependent polarization and spatial information of a scene can be acquired simultaneously. Principles and experimental results in a case study are encouraging.
Machine vision detection of isolated and overlapped nematode worms using skeleton analysis

In this paper we present a novel method for detection of individual C.Elegans worms in population images in presence of overlapping. First, in a pre-processing phase the worms skeletons are obtained by morphological skeleton operation after image binarization and filling small holes. Then, after pruning the small branches of the skeletons, the skeletons are splited into several branches from the pixels with more than two neighbors. Angle of each branch side is calculated in the next stage and the neighbor branches with angle difference less than a predefined threshold are merged. Finally, a simple post-processing based on stastical analysis of worms' length on their widths is used in order to increase the automatic efficiency of the method. We have applied our method to a database of 147 isolated and overlapped worms and obtained 81.43% accuracy.
Road network inference from GPS traces using DTW algorithm

This paper proposes a method for inferring the road network from Global Position System (GPS) traces, which is composed of intersections and the roads between each pair of directly-connected intersections. Random Sampling (RANSAC) algorithm is used to cluster the turning points, where the users change their moving directions, into intersections. All of the GPS traces are segmented by the intersections, resulting in connectivity matrix of the intersections and small GPS tracks for each pair of directly-connected intersections. At last, the road between each two directly-connected intersections is extracted through aligning and averaging all of the tracks using Dynamic Time Warping (DTW) algorithm. The main novelty of our methods is aligning the tracks point by point for each road using a “stretching and compression” strategy, which not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis using their time alignment, such as analyzing the users' speed stability at a specific location. The experimental results show that our algorithm outperforms other methods by producing clean road network without spurious edges.
Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
Adaptive partitioning method in high resolution speckle imagery for sub-pixel digital image correlation

Image processing methods have gained wide acceptance in the field of experimental mechanics for the measurement of full field displacements and strains in materials that undergo mechanical stress. Over the years various block based methods have been developed focusing on pixel and sub-pixel accurate displacement estimation, using as input data speckle images of the material before and after the deformation process. As high resolution imaging systems become more affordable, the limitations of block-based approaches become apparent because of their inherent inability to create high density motion fields while keeping the errors in the calculated motion vectors to a minimum due to the aperture problem. In this paper a novel low-level partitioning method is presented which adaptively divides the image into cells, according to the underlying speckle structure with the purpose of minimizing motion estimation errors. The method starts by determining relevant 'central' areas for speckles over a certain minimum size and subsequently builds the cells around these areas according to spatial distances and image structure. The results show clear improvements in motion accuracy compared to the regular block approach and provide a first step in the accurate isolation of various features in the images such as holes and cracks.