nils-herrmann/clean_scirepeval huggingface dataset:
A new non-invasive approach for early classification of renal rejection types using diffusion-weighted MRI

Although renal biopsy remains the gold standard for diagnosing the type of renal rejection, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. Therefore, there is an urgent need to explore a non-invasive technique that can early classify renal rejection types. In this paper, we develop a computer-aided diagnostic (CAD) system that can classify acute renal transplant rejection (ARTR) types early via the analysis of apparent diffusion coefficients (ADCs) extracted from diffusion-weighted (DW) MRI data acquired at low-(accounting for perfusion) and high-(accounting for diffusion) b-values. The developed framework mainly consists of three steps: (i) data co-alignment using a 3D B-spline-based approach (to handle local deviations due to breathing and heart beat motions) and segmentation of kidney tissue with an evolving geometric (level-set based) deformable model guided by a voxel-wise stochastic speed function, which follows a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and visual kidney-background appearances of DW-MRI data (image intensities and spatial interactions); (ii) construction of a cumulative empirical distribution of ADC at low and high b-values of the segmented kidney accounting for blood perfusion and water diffusion, respectively, to be our discriminatory ARTR types feature; and (iii) classification of ARTR types (acute tubular necrosis (ATN) anti-body- and T-cell-mediated rejection) based on deep learning of a non-negative constrained stacked autoencoder. Results show that 98% of the subjects were correctly classified in our “leave-one-subject-out” experiments on 39 subjects (namely, 8 out of 8 of the ATN group and 30 out of 31 of the T-cell group). Thus, the proposed approach holds promise as a reliable non-invasive diagnostic tool.
Fast weighted cost propagation with smoothness constraint on a tree

In this paper, we propose a novel fast cost propagation algorithm on spanning tree structures. By introducing local smoothness constraint during the weighted cost aggregation process on tree structures, we overcome the shortage of the “fronto-parallel plane” assumption used in most local and non-local cost aggregation algorithms. By applying it to our stereo correspondence framework, accurate results with fine disparity boundaries can be obtained. We also introduce a linear time implementation of the proposed algorithm, thus the computational complexity is kept extremely low.
Robust moving camera calibration for synthesizing free viewpoint soccer video

In this paper, a robust moving camera calibration method is proposed in order to synthesize a free viewpoint soccer video with a high degree of accuracy. The main problem in video registration-based moving camera calibration is that the calibration accuracy is very low if the detected feature points are from moving objects. In order to solve this problem, the proposed method tracks the feature points along video frames to construct a trajectory matrix of feature points, and the trajectory matrix is decomposed into a low-rank matrix representing global camera motion and a sparse matrix representing local individual motion. Therefore, according to such decomposition, the individual motions of dynamic feature points that are from moving objects could be suppressed and removed. Experimental results show that the proposed method achieves more accurate calibration result and the visual quality of a synthesized free viewpoint soccer video is also improved by the proposed method.
Local refinement for 3D deformable parametric surfaces

Biomedical image segmentation is an active field of research where deformable models have proved to be efficient. The geometric representation of such models determines their ability to approximate the shape of interest as well as the speed of convergence of related optimization algorithms. We present a new tensor-product parameterization of surfaces that offers the possibility of local refinement. The goal is to allocate additional degrees of freedom to the surface only where an increase in local detail is required. We introduce the possibility of locally increasing the number of control points by inserting basis functions at specific locations. Our approach is generic and relies on refinable functions which satisfy the refinement relation. We show that the proposed method improves brain segmentation in 3D MRI images.
A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results

Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results.
Fisher-selective search for object detection

An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning.
Perceptual contrast sensitivity based video quality assessment in DCT domain

Video quality assessment can be performed by comparing distorted video with the undistorted version by taking human vision system (HVS) into account. A perceptual vision sensitivity model is developed in this paper by systematically integrating visual attention and foveation mechanism into contrast sensitivity function (CSF). The model can accurately estimate a critical frequency beyond which the HVS cannot perceive contrast changes. Subsequently, a video quality metric is proposed by applying the new sensitivity model to a similarity measure between distorted video and reference one in the DCT domain. Experimental results with respect to publicly available video quality databases have demonstrated promising performance of the proposed quality metric.
Bayesian logistic regression with sparse general representation prior for multispectral image classification

In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.
The visibility of motion artifacts and their effect on motion quality

The visibility of motion artifacts in a video sequence e.g. motion blur and temporal aliasing, affects perceived motion quality. The frame rate required to render these motion artifacts imperceptible is far higher than is currently feasible or specified in current video formats. This paper investigates the perception of temporal aliasing and its associated artifacts below this frame rate, along with their influence on motion quality, with the aim of making suitable frame rate recommendations for future formats. Results show impairment in motion quality due to temporal aliasing can be tolerated to a degree, and that it may be acceptable to sample at frame rates 50% lower than those needed to eliminate perceptible temporal aliasing.
A weighted total variation approach for the atlas-based reconstruction of brain MR data

Compressed sensing is a powerful approach to reconstruct high-quality images using a small number of samples. This paper presents a novel compressed sensing method that uses a probabilistic atlas to impose spatial constraints on the reconstruction of brain magnetic resonance imaging (MRI) data. A weighted total variation (TV) model is proposed to characterize the spatial distribution of gradients in the brain, and incorporate this information in the reconstruction process. Experiments on T1-weighted MR images from the ABIDE dataset show our proposed method to outperform the standard uniform TV model, as well as state-of-the-art approaches, for low sampling rates and high noise levels.
Print quality assessment for stochastic clustered-dot halftones using compactness measures

Most electro-photographic printers prefer clustered-dot halftone textures for rendering smooth and stable prints. Clustered-dot halftone patterns can be periodic or aperiodic. As periodic clustered-dot halftone can lead to undesirable moiré patterns, stochastic clustered-dot halftone textures are more preferred. There are available different screening methods to generate stochastic clustered-dot halftone textures but there are no standard print quality assessment measures that can be easily used for quantitatively evaluating and comparing different stochastic clustered-dot halftoning methods. We explore the use of compactness measures for this purpose, and also propose a new compactness measure that seems good metric to quantitatively compare and assess the print quality of different stochastic clustered-dot halftoning methods. Using the proposed metric, we compare three different stochastic clustered-dot halftoning methods, and our results are almost in agreement with psychophysical experiments results reported earlier.
Wheal detection from skin prick test images using normalized-cuts and region selection

Skin prick test is a common diagnosis method for allergic diseases. The test causes a controlled allergic response and provokes a wheal on the skin, whose size and shape change depending on the sensitivity of a patient against a specific allergen. The physical size of the wheal can be estimated from images acquired by digital cameras, which requires segmentation of the wheal from the captured SPT images. In this paper, we propose a new method for wheal detection from skin prick test images. The proposed method operates on localized images using a set of calibration marks and a perspective transformation. An initial segmentation of the localized SPT image is performed using normalized cuts to identify coherent regions in the image. The segmented region containing the wheal is selected by formulating an optimization problem to identify the segment with distinctive color, texture, and shape attributes. Our experiments conducted on images acquired from 50 different patients show the performance of the proposed wheal detection algorithm.
Seismic horizon reconstruction on polygonal domains using the Schwarz-Christoffel transformation

Methods of seismic horizon reconstruction based on the solution of a partial derivative equation are generally robust to noise. However, they can only reconstruct horizons on rectangular domains when using fast iterative methods which is inconvenient in regions of uncertainty or irrelevant data in the seismic image. In this paper, we propose a method to reconstruct seismic horizons on any polygonal domain based on a succession of Schwarz-Christoffel transformations which conserve the angles locally. Since these are conformal transformations, the form of the equation is preserved and the solution on the polygonal domain corresponds to the solution on the rectangular domain using the grid data transformed from the rectangular into the polygonal domain.
Signature line detection in scanned documents

In this paper, we present an algorithm to determine the signature line in scanned documents through a fast and computationally efficient technique. To that effect, the algorithm consists of three modules, namely pre-processing, candidate line detection and classification. Preprocessing in this case consists of hard thresholding, which is essential to line detection in the next module. The Hough transform for a single angle (horizontal) is utilized in the line detection module. Connected component analysis is utilized to merge detected Hough lines belonging to the same line on the document. Finally, the classification module attempts to classify each line as a signature line or not. This is accomplished using Histogram of a Gradient (HoG) features and a Euclidean distance measure for comparison. Prior data for this similarity comparison consists of images of target text in different font sizes and styles. The proposed algorithm showed favorable results in terms of precision and accuracy.
Image anonymization for PRNU forensics: A set theoretic framework addressing compression resilience

Image forensics using sensor photo-response nonuniformity (PRNU) provides a powerful method for associating an image with the camera that captured the image. To preserve privacy despite the availability of this powerful tool, we present a new framework for image anonymization. We formulate anonymization as a feasibility problem subject to multiple constraints that seek to ensure non-detectability of the PRNU fingerprint, visual fidelity to the original image, and compatibility to compression. A feasible anonymized image is then obtained via the method of projections onto convex sets using the inherent convexity of several constraints and convex approximations for the others. We demonstrate the effectiveness of our framework by benchmarking it over a publicly available dataset of images from multiple cameras and comparing against a recently presented alternative method. In the process we also highlight a key failing of several prior methods that fail to account for quantization in the compression process and suffer from catastrophic loss of anonymity when the anonymized image is stored in a compressed format, as would commonly be the case in realistic applications. We demonstrate specifically that the compression compatibility constraints we introduce help ensure that our method does not encounter this common pitfall.
Saliency detection via global-object-seed-guided cellular automata

Image saliency detection has attracted much attention in recent years, while several challenging problems are still unsolved, such as inaccurate saliency detection in complex scenes and suppressing salient objects near image borders. In this paper, a novel algorithm is proposed to solve these problems. Firstly, we collect background seeds from image borders by boundary information and construct a background-based saliency map via low level features. Then, a novel propagation mechanism named global-object-seed-guided Cellular Automata model is builded. Cellular Automata exploits the intrinsic relevance of similar regions through interactions with neighbors, and global object seeds reduce the difference between dissimilar adjacent regions in the whole salient object. Experimental results on public benchmark datasets demonstrate the superiority of the proposed algorithm over ten state-of-the-art saliency models.
A dynamic motion vector referencing scheme for video coding

Video codecs exploit temporal redundancy in video signals, through the use of motion compensated prediction, to achieve superior compression performance. The coding of motion vectors takes a large portion of the total rate cost. Prior research utilizes the spatial and temporal correlation of the motion field to improve the coding efficiency of the motion information. It typically constructs a candidate pool composed of a fixed number of reference motion vectors and allows the codec to select and reuse the one that best approximates the motion of the current block. This largely disconnects the entropy coding process from the block's motion information, and throws out any information related to motion consistency, leading to sub-optimal coding performance. An alternative motion vector referencing scheme is proposed in this work to fully accommodate the dynamic nature of the motion field. It adaptively extends or shortens the candidate list according to the actual number of available reference motion vectors. The associated probability model accounts for the likelihood that an individual motion vector candidate is used. A complementary motion vector candidate ranking system is also presented here. It is experimentally shown that the proposed scheme achieves about 1.6% compression performance gains on a wide range of test clips.
Predominant color name indexing structure for person re-identification

The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from part-based models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.
Accurate 3D car pose estimation

We propose a new approach for accurate car pose estimation in images using only a dataset of 3D untextured models. Our algorithm detects both a car and its 3D pose. It is based on the matching of 3D models with the car in the image. With a part detector based on Convolutional Neural Networks, interest points corresponding to predefined 3D parts are extracted from the image. Then, we use the car geometry to find which parts are relevant across viewpoints. Finally, a 2D/3D pose estimator is used to recover the 3D pose of the car. The main contribution is to learn appearance and geometry models from 3D models dataset only. Experiments show that the method is very competitive for car detection and coarse viewpoint classification and improves the 3D pose estimation over the state-of-the-art methods.
Robust weighted least squares for guided depth upsampling

In this paper, we propose a new guided depth upsampling method denoted as Robust Weighted Least Squares (RWLS). Our work is inspired by the connection between the Weighted Least Squares (WLS) and the Auto Regressive (AR) model. By adopting a new robust penalty function to model the smoothness of the proposed model, we show that the proposed method performs much better in preserving sharp depth discontinuities than previous work. Through both mathematical analysis and experimental results, we show that our method has promising performance on handling the inconsistency between the guidance image and the depth map in both preserving sharp depth discontinuities and suppressing the texture copy artifacts.
An adaptive QP offset determination method for HEVC

This paper investigates the effect that the QP offset value has on the coding performance of HEVC. We relate QP offset to the type of texture content present in the sequence. These then are used to develop a low-complexity adaptive QP offset selection method. This enables in-loop configuration of the QP offset parameter in a way that is content dependent and utilizes available encoding statistics. The proposed adaptive method is found to offer average bitrate reductions ranging from 1.38% for dynamic texture sequences up to 1.59% for static texture sequences relative to the QP offset used in the JCT-VC common test conditions.
Localization and pose estimation of textureless objects for autonomous exploration missions

In this paper we describe an approach for detection and pose estimation of colored objects with only few or no textural features. The approach consists of two separate stages. First, we perform vision-based object detection and hypothesis filtering. Then, we estimate and validate the object's pose in 3-D laser scans. For object detection we integrate image segmentation results from multiple viewpoints in a set-theoretical filter that provides a probabilistically sound estimate of the number of objects and their respective locations. For validation and pose estimation we search for the best pose by sampling from a geometric measurement model. The system has been validated during autonomous exploration missions in unstructured and space-like environments.
Hyperspectral image classification with support vector machines on kernel distribution embeddings

We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions - by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate each pixel to an empirical distribution of its neighbouring pixels, a judicious representation of which in an RKHS, in conjunction with the spectral information contained in the pixel itself, give a new explicit set of features that can be fed into a suite of standard classification techniques - we opt for a well established framework of support vector machines (SVM). Furthermore, the computational complexity is reduced via random Fourier features formalism. We study the consistency and the convergence rates of the proposed method and the experiments demonstrate strong performance on hyperspectral data with gains in comparison to the state-of-the-art results.