nils-herrmann/clean_scirepeval huggingface dataset:
Appearance analysis for diagnosing malignant lung nodules

An alternative method of diagnosing malignant lung nodules by their visual appearance rather than conventional growth rate is proposed. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a rotation invariant second-order Markov-Gibbs random field. Its neighborhood system and potentials are analytically learned from a training set of nodule images with normalized intensity ranges. Preliminary experiments on 109 lung nodules (51 malignant and 58 benign ones) resulted in the 96.3% correct classification (for the 95% confidence interval), showing the proposed method is a promising supplement to current technologies for early diagnostics of lung cancer.
Detection and recognition of lung abnormalities using deformable templates

Automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (CT) chest scans is one of the most important problems of today's medical image analysis. We propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. The separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3D and 2D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. Experiments with 200 spiral low dose CT (LDCT) scans confirm the accuracy of our approach.
Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal Transplant Rejection

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.
Latent Multi-view Subspace Clustering

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. The proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
Calibrating Freely Moving Cameras

We present a novel practical method for self-calibrating a camera which may move freely in space while changing it internal parameters by zooming. We show that point correspondences between a pair of images, and the fundamental matrix computed from these point correspondences, are sufficient to recover the internal parameters of a camera. Unlike other methods, no calibration object with known 3D shape is required and no limitations are put on the unknown motion, as long as the camera is projective. The main contribution of this paper is development of a global linear solution which is based on the well-known Kruppa equations. We introduce a formulation different from the Huang and Faugeras constraints. The method has been extensively tested on synthetic and real data and promising results are reported
Camera Motion Quantification and Alignment

We propose a method to synchronize video sequences of distinct scenes captured by cameras undergoing similar motions. For the general camera motion and 3D scene, the camera ego-motions are featured by fundamental ratios obtained from the fundamental matrices. In the case of pure translation, translational magnitude features is used. These extracted features are invariant to the camera internal parameters, and therefore can be computed without recovering camera trajectories along the image sequences. Consequently, the alignment problem reduces to matching sets of feature vectors, obtained without any knowledge of other sequences. Experimental results demonstrate the accuracy and applications of the proposed method
Autoconfiguration of a Dynamic Nonoverlapping Camera Network

In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed. In this paper, a practical framework is proposed to self-calibrate dynamically moving and zooming cameras and determine their absolute and relative orientations, assuming that their relative position is known. A global linear solution is presented for self-calibrating each zooming/focusing camera in the network. After self-calibration, it is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the dynamic network configuration. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic, as well as on real data.
Geometric Occlusion Analysis in Depth Estimation Using Integral Guided Filter for Light-Field Image.

Unlike traditional multi-view images, sampling in angular domain of light field images is distributed in different directions. Therefore, an angular sampling image (ASI), comprising of possible matching points extracted from each view, is available for each point. In this paper, we analyze the geometric relationship between ASIs and reference sub-aperture images, and then prove the occlusion boundary similarity. Based on the geometric relationship in extreme cases, we show that some points in ASI have higher reliability than other points for depth calculation. An integral guided filter is then built based on the sub-aperture image to predict occlusion probabilities in ASIs. The filter is independent of ASIs and has no requirement for high angular resolution so that it is easy to apply to the cost volume calculation. We integrate the filter into our depth estimation framework and other state-of-the-art depth estimation frameworks. Experimental results demonstrate that the proposed filter is more effective to occluded point detection in ASIs than other methods. Results from different data sets show that our method outperforms the existing state-of-the-art depth estimation methods, especially along occlusion boundaries.
From Social to Individuals: A Parsimonious Path of Multi-Level Models for Crowdsourced Preference Aggregation

In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments. However, in reality, annotators are subject to variations due to multi-criteria, abnormal, or a mixture of such behaviors. In this paper, we propose a parsimonious mixed-effects model, which takes into account both the fixed effect that the majority of annotators follows a common linear utility model, and the random effect that some annotators might deviate from the common significantly and exhibit strongly personalized preferences. The key algorithm in this paper establishes a dynamic path from the social utility to individual variations, with different levels of sparsity on personalization. The algorithm is based on the Linearized Bregman Iterations, which leads to easy parallel implementations to meet the need of large-scale data analysis. In this unified framework, three kinds of random utility models are presented, including the basic linear model with $L_2$ loss, Bradley-Terry model, and Thurstone-Mosteller model. The validity of these multi-level models are supported by experiments with both simulated and real-world datasets, which shows that the parsimonious multi-level models exhibit improvements in both interpretability and predictive precision compared with traditional HodgeRank.
Self-calibrated reconstruction of partially viewed symmetric objects

Traditional stereo reconstruction techniques, based on point correspondences and estimation of the cameras from the fundamental matrix, introduce a four-fold ambiguity. Moreover, there is a projective ambiguity inherent in the fundamental matrix. We show that a symmetric object can be modeled, even under partial occlusion, by a pair of uncalibrated stereo images. This implies that, unlike traditional stereo algorithms, we can extract 3D information from two arbitrary viewpoints, even when there is no left-to-right point correspondences. To demonstrate the effectiveness of the method, we present experimental results on both synthetic and real images.
Robust Separation of Reflection from Multiple Images

When one records a video/image sequence through a transparent medium (e.g. glass), the image is often a superposition of a transmitted layer (scene behind the medium) and a reflected layer. Recovering the two layers from such images seems to be a highly ill-posed problem since the number of unknowns to recover is twice as many as the given measurements. In this paper, we propose a robust method to separate these two layers from multiple images, which exploits the correlation of the transmitted layer across multiple images, and the sparsity and independence of the gradient fields of the two layers. A novel Augmented Lagrangian Multiplier based algorithm is designed to efficiently and effectively solve the decomposition problem. The experimental results on both simulated and real data demonstrate the superior performance of the proposed method over the state of the arts, in terms of accuracy and simplicity.
A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization

Community structure is one of the most important properties of complex networks and is a foundational concept in exploring and understanding networks. In real world, topology information alone is often inadequate to accurately find community structure due to its sparsity and noises. However, potential useful prior information can be obtained from domain knowledge in many applications. Thus, how to improve the community detection performance by combining network topology with prior information becomes an interesting and challenging problem. Previous efforts on utilizing such priors are either dedicated or insufficient. In this paper, we firstly present a unified interpretation to a group of existing community detection methods. And then based on this interpretation, we propose a unified semi-supervised framework to integrate network topology with prior information for community detection. If the prior information indicates that some nodes belong to the same community, we encode it by adding a graph regularization term to penalize the latent space dissimilarity of these nodes. This framework can be applied to many widely-used matrix-based community detection methods satisfying our interpretation, such as nonnegative matrix factorization, spectral clustering, and their variants. Extensive experiments on both synthetic and real networks show that the proposed framework significantly improves the accuracy of community detection, especially on networks with unclear structures.
Cluster-Based Co-Saliency Detection

Co-saliency is used to discover the common saliency on the multiple images, which is a relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for co-saliency detection. Global correspondence between the multiple images is implicitly learned during the clustering process. Three visual attention cues: contrast, spatial, and corresponding, are devised to effectively measure the cluster saliency. The final co-saliency maps are generated by fusing the single image saliency and multiimage saliency. The advantage of our method is mostly bottom-up without heavy learning, and has the property of being simple, general, efficient, and effective. Quantitative and qualitative experiments result in a variety of benchmark datasets demonstrating the advantages of the proposed method over the competing co-saliency methods. Our method on single image also outperforms most the state-of-the-art saliency detection methods. Furthermore, we apply the co-saliency method on four vision applications: co-segmentation, robust image distance, weakly supervised learning, and video foreground detection, which demonstrate the potential usages of the co-saliency map.
Metrology in uncalibrated images given one vanishing point

In this paper, we describe how 3D Euclidean measurements can be made in a pair of uncalibrated images, when only minimal geometric information are available in the image planes. This minimal information consists of a line in a reference plane, and the vanishing point orthogonal to it. Given such limited information, we show that the length ratio of two objects perpendicular to the reference plane can be expressed as a function of the camera intrinsic parameters. Assuming that the camera intrinsic parameters remain invariant between two views, we perform Euclidean metric measurements directly in the perspective images.
Scene Text Deblurring Using Text-Specific Multiscale Dictionaries

Texts in natural scenes carry critical semantic clues for understanding images. When capturing natural scene images, especially by handheld cameras, a common artifact, i.e., blur, frequently happens. To improve the visual quality of such images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. In this paper, we study the problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the text and nontext fields. The TMD-based text field reconstruction helps to deal with the different scales of strings in a blurry image effectively. Furthermore, an adaptive version of nonuniform deblurring method is proposed to efficiently solve the real-world spatially varying problem. Dictionary learning allows more flexible modeling with respect to the text field property, and the combination with the nonuniform method is more appropriate in real situations where blur kernel sizes are depth dependent. Experimental results show that the proposed method achieves the deblurring results with better visual quality than the state-of-the-art methods.
Trust your wallet: A new online wallet architecture for Bitcoin

Online wallet has become an important method to manage Bitcoin. In a Bitcoin transaction, online wallet manages the private key automatically, and stores the encrypted private key in remote to ensure the accessibility of Bitcoin anywhere. In the traditional online wallet, the private key is stored centrally in a storage unit. However, if the storage unit is collapsed or hacked, users will suffer the risk of losing their Bitcoins. Motivated by this, in this paper, we propose a new online wallet architecture: HA-eWallet. In HA-eWallet, the transaction of Bitcoin is signed by multiple private keys rather than one, and private keys are stored separately in different places. In addition, we introduce a second service unit to construct the Active-Active architecture to rotate the capability and workload. Besides, we adopt a disaster recovery strategy in our proposed architecture in case of any disaster. According to the running states of each service unit, HA-eWallet have three operation models, and can be switched smoothly. Theoretical analyses and experiments show that: HA-eWallet can achieve higher availability compared with the traditional online wallet architecture, and users will not suffer a loss as long as the number of lost private keys are less than 50% of the users' total number of private keys.
Image Deblurring via Extreme Channels Prior

Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low-illumination images. However, it has limitations and is less likely to support the kernel estimation while bright pixels dominate the input image. We observe that the bright pixels in the clear images are not likely to be bright after the blur process. Based on this observation, we first illustrate this phenomenon mathematically and define it as the Bright Channel Prior (BCP). Then, we propose a technique for deblurring such images which elevates the performance of existing motion deblurring algorithms. The proposed method takes advantage of both Bright and Dark Channel Prior. This joint prior is named as extreme channels prior and is crucial for achieving efficient restorations by leveraging both the bright and dark information. Extensive experimental results demonstrate that the proposed method is more robust and performs favorably against the state-of-the-art image deblurring methods on both synthesized and natural images.
Video Face Clustering via Constrained Sparse Representation

In this paper, we focus on the problem of clustering faces in videos. Different from traditional clustering on a collection of facial images, a video provides some inherent benefits: faces from a face track must belong to the same person and faces from a video frame can not be the same person. These benefits can be used to enhance the clustering performance. More precisely, we convert the above benefits into must-link and cannot-link constraints. These constraints are further effectively incorporated into our novel algorithm, Video Face Clustering via Constrained Sparse Representation (CS-VFC). The CS-VFC utilizes the constraints in two stages, including sparse representation and spectral clustering. Experiments on real-world videos show the improvements of our algorithm over the state-of-the-art methods.
Matching-CNN meets KNN: Quasi-parametric human parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN [12] in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts [29, 30], for the human parsing task.
Photographic Composite Detection Using Two Circles

Image forgery technology has become popular for tempering with digital photos. In this work, we propose a new technique for detecting the photographic composites using two or more equal and coplanar circles. In particular, we describe how to (i) estimate the focal length based on the equality of the sizes of two coplanar circles, (ii) estimate the normal vector of the world circle plane on the basis of the estimated focal length. Inconsistencies in the angles among the normal vectors (each circle determines a normal vector) are used as evidence of tampering. To demonstrate the efficiency of the approach, we test our method on both synthetic and visually plausible composite images.
Geo-location estimation from two shadow trajectories

The position of a world point's solar shadow depends on its geographical location, the geometrical relationship between the orientation of the sunshine and the ground plane where the shadow casts. This paper investigates the property of solar shadow trajectories on a planar surface and shows that camera parameters, latitude, longitude can be estimated from two observed shadow trajectories. Our contribution is that we use the design of the analemmatic sundial to get the shadow conic and furthermore recover the camera's geographical location. The proposed method does not require the shadow casting objects or a vertical object to be visible in the recovery of camera calibration. This approach is thoroughly validated on both synthetic and real data, and tested against various sources of errors including noise and number of observations.