nils-herrmann/clean_scirepeval huggingface dataset:
Automatic 3D graph cuts for brain cortex segmentation in patients with focal cortical dysplasia

In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected segmentation method can highly affect the final FCD lesion detection. In this work, we propose an improved graph cuts algorithm integrating Markov random field-based energy function for more accurate brain cortex MRI segmentation. Our method uses three-label graph cuts and preforms automatic 3D MRI brain cortex segmentation integrating intensity and boundary information. The performance of the method is tested on both simulated MR brain images with different noise levels and real patients with FCD lesions. Experimental quantitative segmentation results showed that the proposed method is effective, robust to noise and achieves higher accuracy than other popular brain MRI segmentation methods. The qualitative validation, visually verified by a medical expert, showed that the FCD lesions were segmented well as a part of the cortex, indicating increased thickness and cortical deformation. The proposed technique can be successfully used in this, as well as in many other clinical applications.
Quasar, a high-level programming language and development environment for designing smart vision systems on embedded platforms

We present Quasar, a new programming framework that handles many complex aspects in the design of smart vision systems on embedded platforms, such as parallelization, data flow management, scheduling and load balancing. Quasar, as a high-level programming language, is nearly hardware-agnostic, has a low barrier of entry and is therefore well suited for algorithm design and rapid prototyping. Through several benchmarks and application use cases we demonstrate that programs written in Quasar have a performance that is on a par with (or better than) hand-tuned CUDA and OpenACC code while the development requires much less time and is future-proof.
Improving textures discrimination in the local binary patterns technique by using symmetry & group theory

The underlying working mechanism of Local Binary Pattern (LBP) techniques is still a topic to investigate. In this paper we explore symmetry & group theory for grouping functions that represent binary intensity changes obtained with the LBP technique. This additionally offers a strong mathematical foundation for the basis of the technique. We include complement and mirror invariants to the known LBP rotational invariant. We tested our algorithm using 13 textures from the Brodatz database. The statistical analysis shows that combining rotational, mirrored and complemented versions of local texture results in an improvement in the performance of the technique in terms of accuracy describing textures and discrimination distinguishing textures.
Segmentation and length measurement of the abdominal blood vessels in 3-D MRI images

In diagnosing diseases and planning surgeries the structure and length of blood vessels is of great importance. In this research we develop a novel method for the segmentation of 2-D and 3-D images with an application to blood vessel length measurements in 3-D abdominal MRI images. Our approach is robust to noise and does not require contrast-enhanced images for segmentation. We use an effective algorithm for skeletonization, graph construction and shortest path estimation to measure the length of blood vessels of interest.
Towards online mobile mapping using inhomogeneous lidar data

In this paper we present a novel approach to quickly obtain detailed 3D reconstructions of large scale environments. The method is based on the consecutive registration of 3D point clouds generated by modern lidar scanners such as the Velodyne HDL-32e or HDL-64e. The main contribution of this work is that the proposed system specifically deals with the problem of sparsity and inhomogeneity of the point clouds typically produced by these scanners. More specifically, we combine the simplicity of the traditional iterative closest point (ICP) algorithm with the analysis of the underlying surface of each point in a local neighbourhood. The algorithm was evaluated on our own collected dataset captured with accurate ground truth. The experiments demonstrate that the system is producing highly detailed 3D maps at the speed of 10 sensor frames per second.
Adaptive warped polynomial contour descriptors

This paper introduces a new set of discrete-contour descriptors, which are called improved Fourier descriptors (IFDs) and which are generalisations of the classical Fourier descriptors (FDs). A contour is represented by a complex-valued function which is approximated by a warped trigonometric polynomial (TP) of degree N. The IFDs are the 2N+1 coefficients of this TP. The paper shows theoretically and experimentally that the contour approximation using a given number of IFDs is more accurate than the corresponding approximation using the same number of FDs. In particular, IFDs represent visually important shape features much better than FDs. Therefore, they are promising shape descriptors in pattern recognition.
Feature extraction for hyperspectral images based on semi-supervised local discriminant analysis

We propose a novel semi-supervised local discriminant analysis (SELD) method for feature extraction in hypers-pectral remote sensing imagery. The proposed method combines a supervised method (Linear Discriminant Analysis (LDA)) and an unsupervised method (Neighborhood Preserving Embedding (NPE)) without any free parameters. The underlying idea is to design optimal projection vectors, which can discover the global discriminant structure of the available labeled samples while preserving the local neighborhood spatial structure of the unlabeled samples. Furthermore, in our approach the number of extracted feature bands is no longer limited by the number of classes, which is a disadvantage of LDA. Experimental results demonstrate that the proposed method outperforms consistently other related semi-supervised methods and that it is also much more stable when the percentage of the labeled samples changes
Wavelet Based Joint Denoising of Depth and Luminance Images

In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Two versions of the algorithm are presented, depending on the method used for the classification of the image contexts. Denoising results are compared with the ground truth images obtained by averaging of the multiple frames of the still scene.
Improved segmentation of ultrasound brain tissue incorporating expert evaluation

The quantitative analysis of medical ultrasound images for the purpose of diagnosis is a difficult task due to the speckle noise present in the images. Nowadays medical doctors depend strongly on the visual interpretation of the images which is subjective to some account. Trying to reduce this noise should assist the experts in a better understanding of some pathologies. We focus on a brain disease called periventricular leukomalacia, also called white matter damage, which occurs frequently on premature neonates. For the moment the affected brain tissue is segmented semi-automatically using two different techniques that take the speckle noise into little account. Here we propose a framework which includes an efficient preprocessing step and relying on expert-based evaluation we develop an integrated segmentation method, which yields a more accurate and better reproducible segmentation
Data compression of ECG's by high-degree polynomial approximation

A method for the compression of ECG (electrocardiogram) data is presented. The method is based on high-degree polynomial expansions. Data rules of about 350 b/s are achievable at an acceptable signal quality. The high compression is obtained by a carefully selected subdivision of the ECG signal into intervals that make optimal use of the special properties of the polynomial base functions. Each interval corresponds to one ECG period. The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error).<<ETX>>
A new restoration method and its application to speckle images

The visual interpretation of ultrasound brain images is a proven method to detect the white matter damage at an early stage. A problem, common to all medical ultrasound images, is the presence of speckle noise, which not only complicates the visual interpretation of images, but also quantitative measurements. This paper proposes a new filter that removes a significant amount of speckle noise from ultrasound images, while preserving details very well. The filter is based on a new cleaning technique that operates on the detail images of a wavelet decomposition. The paper illustrates that the proposed technique has some advantages over other popular techniques, i.e., the ones proposed by Lee (1980), Frost (1982), Malfait and Roose (1997), and Sattar et al. (1997).
Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context

We propose a novel Bayesian wavelet domain denoising method for multispectral images, which adapts itself to the local spatial and spectral image context. The proposed estimator is derived under the minimum mean squared error criterion and it takes into account uncertainty about the presence of a signal of interest in a noisy observation. Comparisons with other recent wavelet domain denoisers for multiband data demonstrate the advantages of the new method both in terms of mean squared error and visually.
Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

This letter introduces a new spectral–spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.
Image resampling between orthogonal and hexagonal lattices

Resampling techniques are commonly required in digital image processing systems. Many times the classical interpolation functions are used, i.e., nearest-neighbour interpolation and bilinear interpolation, which are prone to the introduction of undesirable artifacts due to aliasing such as moire patterns. This paper presents a novel approach which minimizes the loss of information, in a least-squares sense, while resampling between orthogonal and hexagonal lattices. Making use of an extension of 2D splines to hexagonal lattices, the proper reconstruction function is derived. Experimental results for a printing application demonstrate the feasibility of the proposed method and are compared against the classical techniques.
Channelized hotelling observers for the detection of 2D signals in 3D simulated images

Current clinical practice is increasingly moving in the direction of volumetric imaging. However, model observers for 3D images have been little explored so far. This study is investigating the task of detecting 2D signals in multi-slice simulated image data. We propose a novel design of a multi-slice model observer. To evaluate it, we compare three different model designs of the channelized Hotelling observer (CHO), two multi-slice and one single-slice model. The multi-slice models are built as a sequence of a 2D CHO and 1D HO, where the CHO is used to calculate a vector of metrics for each slice in the planar view and the HO is used to calculate the final scalar statistic of the model. The single-slice model is a 2D CHO applied on the location of the lesion. Our results show that the multi-slice models outperform the single-slice one, and here the new model surpasses the existing one.
Semi-supervised graph fusion of hyperspectral and lidar data for classification

This paper proposes a semi-supervised graph-based fusion framework to couple dimensionality reduction and the fusion of multi-sensor data for classification. First, morphological features are used to model the elevation and spatial information contained in both LiDAR data and on the first few principal components (PCs) of the original hyperspectral (HS) image. Then, we fuse the features by projecting the spectral, spatial and elevation features onto a lower subspace through our proposed semi-supervised fusion graph. Experimental results on fusion of HS and LiDAR data from the 2013  Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or unsupervised graph fusion, with the proposed method, overall classification accuracies were improved by 9% and 4%, respectively.
2-D shape representation using improved Fourier descriptors

Fourier descriptors (FD's) are widely used shape descriptors. By first warping the scanning speed of the contour before calculating the FD's, the shape approximation can be improved. This approach has never been properly tested on real applications. In this paper we compare these new shape descriptors to the regular FD's. A database of over 400 leaf shapes is approximated using these shape descriptors and compared to the approximation based on FD's. The error of the approximation was measured with the Hausdorff distance and modified Hausdorff distance, resulting for both criteria in an average improvement of more than 10% over approximation based on FD's.
An improved semi-supervised local discriminant analysis for feature extraction of hyperspectral image

We propose an improved semi-supervised local discriminant analysis (ISELD) for feature extraction of hyperspectral image in this paper. The proposed ISELD method aims to find a projection which can preserve local neighborhood information and maximize the class discrimination of the data. Compared to the previous SELD, the proposed ISELD better models the correlation of labeled and unlabeled samples. Experimental results on an ROSIS urban hyperspectral image are encouraging. Compared to some recent feature extraction methods, our approach has more than 2% improvements as the training sample size changes.
New insights in Huber and TV-like regularizers in microwave imaging

In this paper we give new insights into quantitative microwave tomography with robust Huber regularizer and Gauss-Newton optimization. Firstly, we validate this approach for the first time on real electromagnetic measurements. Secondly, we extend the framework with a modified Huber function, which behaves like TV regularization. This is interesting for reconstructing piece-wise constant permittivities that appear in non-destructive testing of installations and other man-made objects.
Polynomial and warped polynomial image and object representations

This paper deals with the representation of images by polynomials and warped polynomials. It summarises the author's work in that field. The paper first addresses fast algorithms for texture approximation on an arbitrary region by bi-variate polynomials. Next, it briefly considers the description of the boundary of an object by complex-valued warped polynomials. Finally, it discusses image representation in terms of image-dependent base functions (warped polynomials). The (warped) polynomial image and object representations can be used for coding purposes and in pattern recognition.
Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds

We present a novel technique for fast and accurate reconstruction of depth images from 3D point clouds acquired in urban and rural driving environments. Our approach focuses entirely on the sparse distance and reflectance measurements generated by a LiDAR sensor. The main contribution of this paper is a combined segmentation and upsampling technique that preserves the important semantical structure of the scene. Data from the point cloud is segmented and projected onto a virtual camera image where a series of image processing steps are applied in order to reconstruct a fully sampled depth image. We achieve this by means of a multilateral filter that is guided into regions of distinct objects in the segmented point cloud. Thus, the gains of the proposed approach are two-fold: measurement noise in the original data is suppressed and missing depth values are reconstructed to arbitrary resolution. Objective evaluation in an automotive application shows state-of-the-art accuracy of our reconstructed depth images. Finally, we show the qualitative value of our images by training and evaluating a RGBD pedestrian detection system. By reinforcing the RGB pixels with our reconstructed depth values in the learning stage, a significant increase in detection rates can be realized while the model complexity remains comparable to the baseline.
Passive Error Concealment for Wavelet-Coded I-Frames With an Inhomogeneous Gauss–Markov Random Field Model

In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.
Segmented image coding of palettized images

This paper addresses the efficient high-compression coding of palettized color images. The most common methods for the lossy compression of color images rely on independent block oriented transform coding of the three or four color components. These techniques do not make use of the high redundancy of the color components and introduce some very undesirable errors at high compression, in particular block distortion. We present an efficient and original technique to code color images with a small number of colors. This is an important class of images in multimedia applications. The technique codes the according luminance image using an existing segmented image coding method for monochrome images. The color information is independently represented in a bit map. The method does not rely on the commonly used color separation and shows a far better subjective image quality than JPEG at high compression.
Spectral-spatial classification for hyperspectral image by bilateral filtering and morphological features

Hyperspectral (HS) imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Conventional spectral-spatial classification methods cannot fully exploit both spectral and spatial information of HS image. In this paper, we propose a new method to fuse the spectral and spatial information for HS image classification. Our approach transfers the spatial structures of the whole morphological profile into the original HS image by using bilateral filtering, and obtains an enhanced HS image enriching both spectral and spatial information. Meanwhile, the enhanced HS image has the same spectral and spatial dimensions as the original HS image, which may provide a new input to improve the performances of existing HS image classification methods. Experimental results on real HS images are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed fusion method improves the overall classification accuracy more than 10% and 5%, respectively.
Regularization of deconvolution using steerable pyramids

In this paper, a method is proposed to regularize deconvolution based on steerable pyramids. Deconvolution is the process of recovering an ideal image from an observation, usually degraded by blurring and a certain noise process (additive, multiplicative, ...). Since deconvolution is an ill-posed problem, noise will be amplified when no regularization is applied. This regularization step formulates our prior knowledge about the image, like general smoothness, ... We propose a regularization approach where deconvolution is combined with a noise reduction scheme, based on steerable pyramids.
A novel method for adaptive enhancement and unsupervised segmentation of MRI brain image

This paper describes a novel global-to-local method for the adaptive enhancement and unsupervised segmentation of brain tissues in MRI (magnetic resonance imaging) images. Three brain tissues are of interest: CSF (cerebrospinal fluid), GM (gray matter), WM (white matter). Firstly, we de-noise the image using wavelet thresholding, and segment the image with minimum error thresholding. Both the thresholdings are global-wise. Subsequently, we combine locally adaptive weighted median and weighted average filters with FCM (fuzzy C-means) clustering to achieve a local-wise segmentation. The performance of the proposed method is quantitatively validated by four indices with respect to a MRI brain phantom.
Real-time hand tracking by invariant hough forest detection

This paper proposes a robust real-time hand tracking approach by combining a discriminative random forest classifier with generative color based cues using a particle filter. The proposed detector is scale and rotation invariant and is able to overcome ambiguities and local maxima in the color based likelihood function in real-time. A new hand tracking dataset with manually annotated groundtruths is created and made freely available for research purposes. Thorough evaluation shows the robustness and advantages of our proposal compared to other state of the art object tracking methods.
3D Object Finding Using Geometrical Constraints on Depth Images

Finding an object in a 3D scene is an important problem in the robotics, especially in assistive systems for visually impaired people. In most systems, the first and most important step is how to detect an object in a complex environment. In this paper, we propose a method for finding an object using geometrical constraints on depth images from a Kinect. The main advantage of the approach is it is invariant to lighting condition, color and texture of the objects. Our approach does not require a training phase, therefore it can reduce the time of preparing data and learning model. The objects of interest have a simple geometrical structure such as coffee mugs, bowls, boxes and are on a table. Overall, our approach is faster and more accurate than methods using 2D features on depth images for training an object model.