nils-herrmann/clean_scirepeval huggingface dataset:
Modelling radial distortion chains for video recapture detection

This paper presents a novel cue for automatic recapture detection of videos. The problem of recapture detection is important to the field of digital forensics as recapture is often an indicator of prior tampering activity. In this paper, we tackle the problem by considering the deformation underwent by geometric primitives, such as straight lines, when processed along recapture chains.We mathematically derive a general curve model for straight lines deformed after single capture under a radial distortion model. The model is then extended to the case of recapture, demonstrating how to automatically classify videos on a per-frame basis from its compliance with a low-order radial distortion model. Finally, we test our model with a practical detector to automatically extract deformed straight lines for classification, which is applied to synthetic sequences.
Discrete multidirectional wavelet bases

The application of the wavelet transform in image processing is most frequently based on a separable construction. While simple, such an approach is not capable of capturing properly all 2D properties in images. In this paper, a new truly separable multidirectional transform is proposed with a subsampling method based on lattice theory. Applications are possible in many areas of image processing. Some promising improvements are achieved in nonlinear approximation and denoising of images.
On the Reconstruction of Wavelet-Sparse Signals From Partial Fourier Information

The problem of reconstructing a wavelet-sparse signal from its partial Fourier information has received a lot of attention since the emergence of compressive sensing (CS). The latest theory within the CS framework analyzes the local coherence between the Fourier and wavelet bases, and recover the signal from frequencies randomly selected according to a variable density profile. Unlike these developments, we adopt a new approach that does not need to analyze the (local) coherence. We show that the problem can be tackled by recovering the wavelet coefficients from the finest to the coarse scale, and only a small set of frequencies are needed to recover the coefficients exactly. As long as the scaling function satisfies a mild condition, the reconstruction is exact. Moreover the frequency set can be deterministically pre-selected and does not need to change even if the wavelet basis changes.
Deconvolution with wavelet footprints for ill-posed inverse problems

In recent years, wavelet based algorithms have been successful in different signal processing tasks. The wavelet transform is a powerful tool, because it manages to efficiently represent sharp discontinuities. Indeed, discontinuities carry most of the signal information and, so, they represent the most critical part to analyse. We have recently introduced the notion of footprints, which form an overcomplete basis built on the wavelet transform. With footprints, one can exactly model the dependency across scales of the wavelet coefficients generated by a discontinuity and this allows to further improve wavelet based algorithms. In this paper we present a footprint based algorithm for signal deconvolution. The algorithm is fast and works for blind deconvolution too. With footprints we manage to deconvolve efficiently the irregular part of the signal. Thanks to the property of footprints of exactly modeling discontinuities, the deconvolved signal does not present artifacts around discontinuities. Moreover, we show that the residual, that is, the difference between the deconvolved signal with footprints and the observed signal, is regular. Thus, this residual can be further deconvolved with any other traditional method. We show that our system outperforms other deconvolution methods.
Sampling and interpolation of the plenoptic function

In this paper, we present reconstruction schemes for the plenoptic function. Using new sampling methods, we show that, for some particular scenes, it is possible to perfectly reconstruct the plenoptic function from a finite number of cameras with finite resolution (Theorem 1 and Corollary 1). In more general cases, we demonstrate new ways of interpolating exactly the plenoptic function (Theorem 2). Finally, we show that it is possible to infer camera locations from a finite set of images. In all cases, we have perfect solutions due to the super-resolution property of the sampling. First numerical experiments on noisy observations show the potentiality of this new theoretical developments.
Optimal filter banks for multiple description coding: Analysis and synthesis

Multiple description (MD) coding is a source coding technique for information transmission over unreliable networks. In MD coding, the coder generates several different descriptions of the same signal and the decoder can produce a useful reconstruction of the source with any received subset of these descriptions. In this paper, we study the problem of MD coding of stationary Gaussian sources with memory. First, we compute an approximate MD rate distortion region for these sources, which we prove to be asymptotically tight at high rates. This region generalizes the MD rate distortion region of El Gamal and Cover (1982), and Ozarow (1980) for memoryless Gaussian sources. Then, we develop an algorithm for the design of optimal two-channel biorthogonal filter banks for MD coding of Gaussian sources. We show that optimal filters are obtained by allocating the redundancy over frequency with a reverse "water-filling" strategy. Finally, we present experimental results which show the effectiveness of our filter banks in the low complexity, low rate regime.
Spatiotemporal sampling trade-off for inverse diffusion source problems

We consider the spatiotemporal sampling of diffusion fields induced by M point sources, and study the associated inverse problem of recovering the initial parameters of the unknown sources. In particular, we focus on characterising qualitatively the error of the obtained source estimates. To achieve this, we obtain an expression with which we can trade the sensor density for performance accuracy. In other words, by evaluating the optimal sampling instant for a given sensor density—and using the corresponding field samples at that instant—we can expect to obtain an improvement in the estimation performance when compared to an arbitrary sampling instant. Finally, several numerical simulations are presented, to support the theoretical results obtained.
Perfect Reconstruction Schemes for Sampling Piecewise Sinusoidal Signals

Consider sampling a signal that is piecewise sinusoidal. Classical sampling theory does not enable a perfect reconstruction of the continuous time signal since the band is not limited (C.E. Shannon, 1949). However, we show that it is still possible to recover all the parameters of the sinusoids and the exact locations of the discontinuities using the annihilating filter method and recently developed Finite Rate of Innovation (FRI) sampling schemes (M. Vetterli et al., 2002) (P.L. Dragotti et al., 2005). Moreover, we show that there is a tradeoff between the number of sinusoids per piece and the proximity of the discontinuities in order to have a unique solution. This result recalls a sort of uncertainty principle
Line-edge extraction based on E-spline acquisition model and a fast optimization algorithm

We propose a line-edge extraction algorithm that uses an E-spline data acquisition model and a fast optimization algorithm. The proposed method can retrieve line-edge parameters, including orientation, offset, and amplitude at sub-pixel accuracy almost independently of the resolution of the images. Because of the optimization approach, the proposed method is robust against model mismatch such as noise, point spread function (PSF) model mismatch, or step line-edge assumption. These properties are verified by simulations using images taken with a digital SLR camera.
Compression of multispectral images by three-dimensional SPIHT algorithm

The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms.
Enhancing latent fingerprints on banknotes

Matching unknown latent fingerprints lifted from various objects or surfaces at crime scenes to fingerprints of known subjects is of vital importance for law enforcement agencies to identify suspects. Banknotes are one of the most common objects containing valuable latent fingerprints. However, due to the complex pattern printed on banknotes, it is a challenging problem even for human experts to mark minutiae in such fingerprints. In this paper a novel technique is proposed to enhance fingerprints on banknotes so that they can be successfully identified by existing fingerprint matchers. The proposed algorithm is based on subtraction of the reference orientation in reference banknote, which is registered to the latent fingerprint by a coarse-to-fine registration algorithm. Promising results are reported on a database which contain 192 latents on banknotes, which proves the effectiveness of the proposed algorithm.
Topology preserving graph matching for partial face recognition

In this paper, we propose a topology preserving graph matching (TPGM) method for partial face recognition. Most existing face recognition methods extract features from holistic face images, yet faces in real-world unconstrained environments are usually occluded by objects or other faces, which cannot provide the whole face images for recognition. Latest keypoint-based partial face recognition methods only match on the detected keypoints to remove the occluded regions. However, they simply measure the node-wise similarity without higher order geometrical graph information, thereby depending heavily on descriptors which are susceptible to noises. To address this, our TPGM method estimates a non-rigid transformation encoding the second order geometric structure of the graph, so that more accurate and robust correspondence can be computed with the topological information. Experimental results on three widely used face datasets show that the proposed TPGM outperforms most existing state-of-the-art partial face recognition methods.
Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields

Thrombosis has become a global disease threatening human health. The left atrial appendage (LAA) is a major source of thrombosis in patients with atrial fibrillation (AF). Positive correlation exists between LAA volume and AF risk. LAA morphology has been suggested to influence thromboembolic risk in AF patients and to help predict thromboembolic events in low-risk patient groups. Automatic segmentation of LAA can greatly help physicians diagnose AF. In consideration of the large anatomical variations of the LAA, we proposed a robust method for automatic LAA segmentation on computed tomographic angiography (CTA) data using fully convolutional neural networks with three-dimensional (3–D) conditional random fields (CRFs). After manual localization of ROI of LAA, we adopted the FCN in natural image segmentation and transferred their learned models by fine-tuning the networks to segment each 2–D LAA slice. Subsequently, we used a modified dense 3–D CRF that accounts for the 3–D spatial information and larger contextual information to refine the segmentations of all slices. Our method was evaluated on 150 sets of CTA data using five-fold cross validation. Compared with manual annotation, we obtained a mean dice overlap of $\text{94.76}\%$ and a mean volume overlap of $\text{91.10}\%$ with a computation time of less than 40 s per volume. Experimental results demonstrated the robustness of our method in dealing with large anatomical variations and computational efficiency for adoption in a daily clinical routine.
Detection and Rectification of Distorted Fingerprints

Elastic distortion of fingerprints is one of the major causes for false non-match. While this problem affects all fingerprint recognition applications, it is especially dangerous in negative recognition applications, such as watchlist and deduplication applications. In such applications, malicious users may purposely distort their fingerprints to evade identification. In this paper, we proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is viewed as a two-class classification problem, for which the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to perform the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. Promising results have been obtained on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent fingerprint database.
Fingerprint matching based on global minutia cylinder code

Although minutia set based fingerprint matching algorithms have achieved good matching accuracy, developing a fingerprint recognition system that satisfies accuracy, efficiency and privacy requirements simultaneously remains a challenging problem. Fixed-length binary vector like IrisCode is considered to be an ideal representation to meet these requirements. However, existing fixed-length vector representations of fingerprints suffered from either low distinctiveness or misalignment problem. In this paper, we propose a discriminative fixed-length binary representation of fingerprints based on an extension of Minutia Cylinder Code. A machine learning based algorithm is proposed to mine reliable reference points to overcome the misalignment problem. Experimental results on public domain plain and rolled fingerprint databases demonstrate the effectiveness of the proposed approach.
Learning Rotation-Invariant Local Binary Descriptor

In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.
Separating Overlapped Fingerprints

Fingerprint images generally contain either a single fingerprint (e.g., rolled images) or a set of nonoverlapped fingerprints (e.g., slap fingerprints). However, there are situations where several fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) fingerprints are lifted from crime scenes or residue fingerprints are left on fingerprint sensors. Overlapped fingerprints constitute a serious challenge to existing fingerprint recognition algorithms, since these algorithms are designed under the assumption that fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped fingerprints into component or individual fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped fingerprints that leads to a significant improvement in the matching accuracy.
Nonlinear Discrete Hashing

In this paper, we propose a nonlinear discrete hashing approach to learn compact binary codes for scalable image search. Instead of seeking a single linear projection in most existing hashing methods, we pursue a multilayer network with nonlinear transformations to capture the local structure of data samples. Unlike most existing hashing methods that adopt an error-prone relaxation to learn the transformations, we directly solve the discrete optimization problem to eliminate the quantization error accumulation. Specifically, to leverage the similarity relationships between data samples and exploit the semantic affinities of manual labels, the binary codes are learned with the objective to: 1) minimize the quantization error between the original data samples and the learned binary codes; 2) preserve the similarity relationships in the learned binary codes; 3) maximize the information content with independent bits; and 4) maximize the accuracy of the predicted labels based on the binary codes. With an alternating optimization, the nonlinear transformation and the discrete quantization are jointly optimized in the hashing learning framework. Experimental results on four datasets including CIFAR10, MNIST, SUN397, and ILSVRC2012 demonstrate that the proposed approach is superior to several state-of-the-art hashing methods.
Robust and Efficient Algorithms for Separating Latent Overlapped Fingerprints

Overlapped fingerprints are frequently encountered in latent fingerprints lifted from crime scenes. It is necessary to separate such overlapped fingerprints into component fingerprints so that existing fingerprint matchers can recognize them. The most crucial step in separating overlapped fingerprints is estimation of component orientation fields, which is a challenging problem for existing orientation field estimation algorithms. We propose a robust orientation field estimation algorithm (called the basic algorithm) for latent overlapped fingerprints whose core is the constrained relaxation labeling algorithm. We also propose improved versions of the basic algorithm for two special but frequent cases: 1) the mated template fingerprint of one component fingerprint is known and 2) the two component fingerprints are from the same finger. In both cases, further constraints are used to reduce ambiguity in relaxation labeling. Experimental results on both real and simulated overlapped fingerprints show that the proposed algorithm outperforms the state-of-the-art algorithm in both accuracy and efficiency. The two improved versions also perform better than the basic algorithm in respective cases. The latent overlapped fingerprint database collected for this study is made publicly available for performance evaluation.
Multi-scale dictionaries based fingerprint orientation field estimation

Orientation field estimation is significantly important for fingerprint recognition. Dictionary based algorithm and its variant, localized dictionaries based algorithm have shown promising performance. In this paper, we extend the original dictionary based algorithm to a multi-scale version. The motivation is that small scale dictionary is more accurate while large scale dictionary is more robust against image noise. Hence information from orientation fields of different scales can be integrated to obtain better results. A multi-layer MRF model is used to formulate and solve the proposed problem. Experimental results on challenging latent fingerprint database demonstrate the advantages of the proposed algorithm.
Multiple Feature Fusion via Weighted Entropy for Visual Tracking

It is desirable to combine multiple feature descriptors to improve the visual tracking performance because different features can provide complementary information to describe objects of interest. However, how to effectively fuse multiple features remains a challenging problem in visual tracking, especially in a data-driven manner. In this paper, we propose a new data-adaptive visual tracking approach by using multiple feature fusion via weighted entropy. Unlike existing visual trackers which simply concatenate multiple feature vectors together for object representation, we employ the weighted entropy to evaluate the dissimilarity between the object state and the background state, and seek the optimal feature combination by minimizing the weighted entropy, so that more complementary information can be exploited for object representation. Experimental results demonstrate the effectiveness of our approach in tackling various challenges for visual object tracking.
Depth Estimation Using a Sliding Camera

Image-based 3D reconstruction technology is widely used in different fields. The conventional algorithms are mainly based on stereo matching between two or more fixed cameras, and high accuracy can only be achieved using a large camera array, which is very expensive and inconvenient in many applications. Another popular choice is utilizing structure-from-motion methods for arbitrarily placed camera(s). However, due to too many degrees of freedom, its computational cost is heavy and its accuracy is rather limited. In this paper, we propose a novel depth estimation algorithm using a sliding camera system. By analyzing the geometric properties of the camera system, we design a camera pose initialization algorithm that can work satisfyingly with only a small number of feature points and is robust to noise. For pixels corresponding to different depths, an adaptive iterative algorithm is proposed to choose optimal frames for stereo matching, which can take advantage of continuously pose-changing imaging and save the time consumption amazingly too. The proposed algorithm can also be easily extended to handle less constrained situations (such as using a camera mounted on a moving robot or vehicle). Experimental results on both synthetic and real-world data have illustrated the effectiveness of the proposed algorithm.