nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of optical flow and traditional sensors in UAV navigation

Optical flows have great potential for navigation of small or micro unmanned aerial vehicles (UAVs) in GPS-degraded or GPS-denied environments, inspired by the study of the flight of several insects. This paper focuses on a comparative study between optical flow and traditional navigation sensors with validation provided through UAV flight tests. More specifically, optical flow calculated from videos is compared side-by-side with the corresponding combination of GPS velocity, range, and IMU measurements. Scale invariant feature transform (SIFT) algorithm is used to convert camera videos into optical flows due to its stability and robustness for feature extraction purposes. Four basic motions are analyzed through ground tests including two rotational and two translational motions, with rotation axis parallel/orthogonal to optical axis. The UAV flight data are used for comparisons of more general motions. The flight results show that the measured optical flow has a mean error of 1.10/1.16 pixel per frame and a standard deviation of 1.05/1.18 pixel per frame in the longitudinal/lateral direction for a 33.4 millisecond interval (29.97 Hz), using the corresponding combination of GPS/INS/range data as the ground truth.
A Study on Cross-Population Age Estimation

We study the problem of cross-population age estimation. Human aging is determined by the genes and influenced by many factors. Different populations, e.g., males and females, Caucasian and Asian, may age differently. Previous research has discovered the aging difference among different populations, and reported large errors in age estimation when crossing gender and/or ethnicity. In this paper we propose novel methods for cross-population age estimation with a good performance. The proposed methods are based on projecting the different aging patterns into a common space where the aging patterns can be correlated even though they come from different populations. The projections are also discriminative between age classes due to the integration of the classical discriminant analysis technique. Further, we study the amount of data needed in the target population to learn a cross-population age estimator. Finally, we study the feasibility of multi-source cross-population age estimation. Experiments are conducted on a large database of more than 21, 000 face images selected from the MORPH. Our studies are valuable to significantly reduce the burden of training data collection for age estimation on a new population, utilizing existing aging patterns even from different populations.
Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression

Human age estimation has recently become an active research topic in computer vision and pattern recognition, because of many potential applications in reality. In this paper we propose to use the kernel partial least squares (KPLS) regression for age estimation. The KPLS (or linear PLS) method has several advantages over previous approaches: (1) the KPLS can reduce feature dimensionality and learn the aging function simultaneously in a single learning framework, instead of performing each task separately using different techniques; (2) the KPLS can find a small number of latent variables, e.g., 20, to project thousands of features into a very low-dimensional subspace, which may have great impact on real-time applications; and (3) the KPLS regression has an output vector that can contain multiple labels, so that several related problems, e.g., age estimation, gender classification, and ethnicity estimation can be solved altogether. This is the first time that the kernel PLS method is introduced and applied to solve a regression problem in computer vision with high accuracy. Experimental results on a very large database show that the KPLS is significantly better than the popular SVM method, and outperform the state-of-the-art approaches in human age estimation.
Still to video face recognition using a heterogeneous matching approach

In this paper, we address the problem of still-to-video (S2V) face recognition. Still images usually have high qualities, captured from cooperative users under controlled environment, such as the mugshot photos. On the contrary, video clips may be acquired with low resolutions and low qualities, from non-cooperative users under uncontrolled environment. Because of these significant differences, we consider the S2V as a heterogeneous matching problem, and propose to develop a method to bridge the gap between the two heterogeneous modalities. A Grassmann manifold learning method is developed to construct subspaces for the purpose of bridging the gap between the two face modalities smoothly. We conduct extensive experiments on two large scale benchmark databases, COX-S2V and PaSC, with different recognition tasks: face identification and verification. The experimental results show that the proposed approach outperforms the state-of-the-art methods under the same experimental settings.
Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics

As a severe psychiatric disorder disease, depression is a state of low mood and aversion to activity, which prevents a person from functioning normally in both work and daily lives. The study on automated mental health assessment has been given increasing attentions in recent years. In this paper, we study the problem of automatic diagnosis of depression. A new approach to predict the Beck Depression Inventory II (BDI-II) values from video data is proposed based on the deep networks. The proposed framework is designed in a two stream manner, aiming at capturing both the facial appearance and dynamics. Further, we employ joint tuning layers that can implicitly integrate the appearance and dynamic information. Experiments are conducted on two depression databases, AVEC2013 and AVEC2014. The experimental results show that our proposed approach significantly improve the depression prediction performance, compared to other visual-based approaches.
Joint estimation of age, gender and ethnicity: CCA vs. PLS

Human age, gender and ethnicity are valuable demographic information about a population. These measures are also considered important soft biometric traits for human recognition or search. Usually the three traits are studied separately. A recent study  shows that the three traits can be estimated simultaneously based on a multi-label regression formulation. The linear and kernel partial least squares (PLS) models are adopted to solve the multi-label regression problem in . In this study, we investigate the canonical correlation analysis (CCA) based methods, including linear CCA, regularized CCA (rCCA), and kernel CCA (KCCA), and compare to the PLS models in solving the joint estimation problem. Interestingly, we found a consistent ranking of the five methods in estimating age, gender, and ethnicity. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity, which has not been shown in previous research, to the best of our knowledge. The experiments are conducted on a very large database of more than 55,000 face images.
Patch-based Image Correlation with Rapid Filtering

This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.
Is gender recognition affected by age?

Gender recognition is important for many applications including human computer interaction (HCI). This paper shows that gender recognition accuracy is affected significantly by the age of the person. Our empirical studies on a large face database of 8,000 images with ages from 0 to 93 years show that gender classification accuracy on adult faces can be 10% higher than that on young or senior faces, evaluated using one of the state-of-the-art methods. We examine aging effects on human faces, which motivates us to investigate which features can incorporate shape and texture variations on faces together with gender encoding. Based on the aging effects, the local binary pattern (LBP) and histograms of oriented gradients (HOG) methods are evaluated for gender characterization with age variation. We also investigate a biologically-inspired method for gender recognition. Overall, no matter what methods are used, the accuracies on adult faces are consistently higher than on young or senior faces. This new finding suggests new efforts in both psychological studies and computational visual recognition for the purpose of HCI applications.
A study of large-scale ethnicity estimation with gender and age variations

In this paper we study large-scale ethnicity estimation under variations of age and gender. The biologically-inspired features are applied to ethnicity classification for the first time. Through a large number of experiments on a large database with more than 21,000 face images, we systematically study the effect of gender and age variations on ethnicity estimation. Our finding is that ethnicity classification can have high accuracy in most cases, but an interesting phenomenon is observed that the ethnic classification accuracies could be reduced by 6∼8% in average when female faces are used for training while males for testing. The study results provide a guide for face processing on a multi-ethnic database, e.g., image collection from the Internet, and may inspire further psychological studies on ethnic grouping with gender and age variations. We also apply the methods to the whole MORPH-II database with more than 55,000 face images for ethnicity classification of five races. It is the first time that ethnicity estimation is performed on so large a database.
Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure

Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.
An image labeling algorithm based on cooperative game theory

Many image analysis and computer vision problems can be formulated as a scene labeling problem in which each site is to be assigned a label from a discrete or continuous label set with contextual information. In this paper we present a new labeling algorithm based on game theory. More precisely, we use Markov random fields to model images, and we design an n-person cooperative game which yields a deterministic optimization algorithm. Experimental results show that the algorithm is efficient and effective, exhibiting very fast convergence, and producing better results than the recently proposed non-cooperative game approach. We also compare this algorithm with other labeling algorithms on real-world and synthetic images.
The linear geo-photometric constraint for image matching, structure reconstruction, image synthesis and object recognition

We show that under orthographic projection (can be relaxed to weak perspective, or scaled orthographic projection) and with a fixed camera and fixed light sources, the images of a moving Lambertian surface can be modelled as a linear combination of three distinct images, with identical coefficients for both shape and intensity. We call this the linear geo-photometric constraint. Using this new constraint, we can match images, reconstruct structure, synthesize new images and recognize objects. Experimental results are shown.
(H)exaShrink: Multiresolution compression of large structured hexahedral meshes with discontinuities in geosciences

We propose a new compression method devoted to large structured hexahedral meshes having discontinuities. It is dedicated to applications such as visualization or physical simulations whose management by any workstation or mobile device with limited memory and bandwidth is critical. Our method relies on a multiresolution analysis that generates a hierarchy of meshes at increasing resolutions. Our technique also uses a discontinuity tracking feature for their preservation, whatever the resolution, and consequently maintains a coherent geometry with respect to the original mesh. Experimental results emphasize the quality of our compression, in terms of both geometrical distortion and compression ratio.
Fast convex optimization for connectivity enforcement in gene regulatory network inference

With the advent of microarrays, arose the need to analyze gene expression data. Tools for building gene regulation networks are indeed of high interest for regulatory relationship sketching and gene interaction prediction. Given all pairwise gene regulation information available, we propose to determine the presence of edges in the final gene regulatory network by adopting a convex optimization formulation. Our energy minimization strategy includes a regularization term accounting for the difference of connectivity of particular genes (i.e. transcription factors), and we employ proximal methods to compute the optimal solution. The resulting algorithm, called “Brane relax”, outperforms state-of-the-art methods while keeping a reduced computational cost.
An overview of signal processing issues in chemical sensing

This tutorial paper aims at summarizing some problems, ranging from analytical chemistry to novel chemical sensors, that can be addressed with classical or advanced methods of signal and image processing. We gather them under the denomination of “chemical sensing”. It is meant to introduce the special session “Signal Processing for Chemical Sensing” with a large overview of issues which have been and remain to be addressed in this application domain, including chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, etc. For enlarging and illustrating the points of view of this tutorial, the invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed by various methods, e.g. source separation, Bayesian, and exploiting typical chemical signal priors like positivity, linearity, unit-concentration or sparsity.
A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured “noises”. As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data.
A constrained-based optimization approach for seismic data recovery problems

Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtained through time-varying filtering, compensating inaccuracies in given approximate templates. The under-determined problem can then be formulated as a convex optimization one, providing estimates of both filters and primaries. Within this framework, the criterion to be minimized mainly consists of two parts: a data fidelity term and hard constraints modeling a priori information. This formulation may avoid, or at least facilitate, some parameter determination tasks, usually difficult to perform in inverse problems. Not only classical constraints, such as sparsity, are considered here, but also constraints expressed through hyperplanes, onto which the projection is easy to compute. The latter constraints lead to improved performance by further constraining the space of geophysically sound solutions.
Low Redundancy Oversampled Lapped Transforms and Application to 3D Seismic Data Filtering

In a previous work, we proposed a relatively simple method to build non separable perfect reconstruction oversampled lapped transforms. The main drawback of this method was that the redundancy factor was constrained to be equal to the overlapping one. This constitutes a strong limitation for applications such as seismic processing involving three-dimensional data sets. The memory requirements may indeed become hard to meet if the redundancy is not reduced. In this paper, we propose an approach to guarantee that a given lapped transform is invertible by a finite length filter bank. We show how to compute a corresponding synthesis filter bank. The proposed analysis/synthesis filter bank system is applied to directional filtering of noisy three-dimensional seismic data
A Nonlinear Stein-Based Estimator for Multichannel Image Denoising

The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take into account both the spatial and the intercomponent correlations existing between the different wavelet subbands. More precisely, we propose a new parametric nonlinear estimator which generalizes many reported denoising methods. The derivation of the optimal parameters is achieved by applying Stein's principle in the multivariate case. Experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques.
Graph inference enhancement with clustering: Application to Gene Regulatory Network reconstruction

The obtention of representative graphs is a key problem in an increasing number of fields, such as computer graphics, social sciences, and biology to name a few. Due to the large number of possible solutions from the available amount of data, building meaningful graphs is often challenging. Nonetheless, enforcing a priori on the graph structure, such as a modularity, may reduce the underdetermination in the underlying problem. In this work, we introduce such a methodology in the context of Gene Regulatory Network inference. These networks are useful to visualize gene interactions occurring in living organisms: some genes regulate the expression of others, structuring the network into modules where they play a central role. Our approach consists in jointly inferring the graph and performing a clustering using the graph-Laplacian-based random walker algorithm. We validate our approach on the DREAM4 dataset, showing significant improvement over state-of-the-art GRN inference methods.
Image analysis using a dual-tree M-band wavelet transform

We propose a two-dimensional generalization to the M-band case of the dual-tree decomposition structure (initially proposed by Kingsbury and further investigated by Selesnick) based on a Hilbert pair of wavelets. We particularly address: 1) the construction of the dual basis and 2) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Significant improvements in terms of both overall noise reduction and direction preservation are observed.
Sparse adaptive template matching and filtering for 2D seismic images with dual-tree wavelets and proximal methods

This paper proposes a novel approach for echo-like multiple removal in two-dimensional seismic images. It is based on constrained adaptive filtering associated with geometric wavelets. Approximate templates of multiple reflections are assumed to be available and they are matched to multiple reflections throughout estimated finite impulse response filters. The problem is formulated under a constrained convex optimization form where the data of interest and filters are estimated jointly. Proximal approaches are used to perform the minimization of the derived criterion. The effectiveness of the proposed approach is demonstrated with various noise levels on realistic simulated data and on field seismic data.
Oversampled Inverse Complex Lapped Transform Optimization

When an oversampled FIR filter bank structure is used for signal analysis, a main problem is to guarantee its invertibility and to be able to determine an inverse synthesis filter bank. As the analysis scheme corresponds to a redundant decomposition, there is no unique inverse filter bank and some of the solutions can lead to artifacts in textured image filtering applications. In this paper, the flexibility in the choice of the inverse filter bank is exploited to find the best-localized impulse responses. The design is performed by solving a constrained optimization problem which is reformulated in a smaller dimensional space. Application to seismic data clearly shows the improvements brought by the optimization process.