nils-herrmann/clean_scirepeval huggingface dataset:
Reconstruction of complex environments by robust pre-aligned ICP

This paper proposes a technique for three-dimensional reconstruction of an underwater environment from range views acquired by an acoustic camera. The final target of the work lies in improving the understanding of a human operator driving an underwater remotely operated vehicle floating close to an offshore structure. Due to the narrow field of view and the absence of control of the sensor position, no information is available about the degree of over-lapping between the range images; further, speckle noise and low resolution make more difficult the registration process. In this paper, we propose a preprocessing method which gives a coarse alignment of range images prior to running the Iterative Closest Point (ICP) algorithm for the accurate registration of views pairs. The pre-alignment is based on the matching between the three-dimensional skeletons extracted from the images. A comparative analysis is presented where our method is compared with plain ICP, and with a technique based on principal components analysis.
Multi-view Photometric Stereo Using Semi-isometric Mappings

Classical uncalibrated Photometric Stereo approaches are mostly constrained to the static view assumption that enforces the camera to be fixed in front of an object illuminated by different light sources. Attempts to extend PS to multi-views has delivered methods that can only be robust to images taken with short camera baselines. In this paper, we present a new uncalibrated Multi-View Photometric Stereo (MVPS) method that can obtain a dense 3D reconstruction from views subject to strong baseline variations and extreme changes in illumination conditions. This approach is intrinsically photo geometric obtaining robust results using a combination of multi-view geometry and photometry. At the core of the algorithm, there is an efficient planar embedding and local image pixel registration among views that renders the problem tractable and computationally solvable. In the experiments, the results are evaluated and compared with the existing methods as well as the ground truth and shows the method is able to deal with the most complex objects and lighting conditions.
Curriculum Dropout

Dropout is a very effective way of regularizing neural networks. Stochastically “dropping out” units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization. Besides, Dropout can be interpreted as an approximate model aggregation technique, where an exponential number of smaller networks are averaged in order to get a more powerful ensemble. In this paper, we show that using a fixed dropout probability during training is a suboptimal choice. We thus propose a time scheduling for the probability of retaining neurons in the network. This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem. This idea of “starting easy” and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models. Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations within the network architecture. Experiments on seven image classification datasets and different network architectures show that our method, named Curriculum Dropout, frequently yields to better generalization and, at worst, performs just as well as the standard Dropout method.
Augmented representation of underwater scenes by segmentation and reconstruction of 3D acoustic images

In this paper, a system aimed at the augmented representation of underwater scenes is presented. The system operates on three-dimensional acoustic images and is composed of several modules devoted to noise filtering, segmentation in pipe-shaped structures, recognition and reconstruction. Finally, identified objects are backprojected in the acoustic image in order to facilitate its interpretation. The final aim is to obtain an augmented (or virtual) representation of such images providing a useful tool to support a human operator for navigating in and inspecting underwater environments.
Automatic White Matter Fiber Clustering Using Dominant Sets

We present an unsupervised approach based on the Dominant Sets framework to automatically segment the white matter fibers into bundles. This framework, rooted in the Game Theory, allows for the automatic determination of the number of clusters from the data itself, without any prior assumption. The clustered bundles are a key information for the generation of unbiased structural connectivity atlases. We have thoroughly validated our algorithm both quantitatively and qualitatively. Indeed, we used biologically plausible synthetic datasets to numerically validate the performance in terms of Precision, Recall and other measures employed in the literature. We also evaluated the algorithm on a real Diffusion Tensor Imaging tractography of a whole mouse brain obtaining promising results. In fact, some of the most prominent brain structures determined by the algorithm correspond to white matter expected anatomy.
Prediction of the Leadership Style of an Emergent Leader Using Audio and Visual Nonverbal Features

The coordination of a leader with group members is very important for an effective leadership given that this figure is the person who actually manages the team members to achieve a desired goal. Investigating the leadership and especially the leadership style is a prominent research topic in social and organizational psychology. However this is a new problem in social signal processing that can actually make valuable contributions by analyzing multimodal data in a more effective and efficient way. In this work we identify the leadership style of an emergent leader (i.e. the leader who naturally arises from a group not designated) as autocratic or democratic. The proposed method is applied to a dataset in-the-wild; in other words there is no role-playing which is novel for this problem. Multiple kernel learning (MKL) using multimodal nonverbal features is utilized to predict leadership styles that proved to achieve better predictions as compared to traditional learning methods. Thanks to MKL and a simple heuristic proposed the best performing features are also identified showing that better predictions can be reached only by using those features. Additionally correlation analysis between the extracted nonverbal features and the results of social psychology questionnaire is also performed. This shows that significantly high correlations exist for speaking activity based and prosodic nonverbal features.
Spurious effects reduction by the reconstruction of acoustic images from bispectrum

A method to improve the quality of acoustic images obtained by systems based on beamforming is described. The specific goal is to eliminate two additive spurious terms present in the beam signals. The first effect is due to side lobes and the second one is due to the nonlinear effects introduced by the modulus extractor present at the output of the beamformer. The relative importance of these two terms is assessed, showing that the term due to modulus extraction has a strong negative incidence on the final image quality. The Gaussian distribution of these spurious terms is discussed and verified in the case of punctiform scenes. Owing to the Gaussianity of the spurious terms, a method to eliminate them from the beam signals, based on higher order spectra analysis, is proposed, resulting in an evident image quality and reliability improvement.
A stochastic graph-based technique for grouping of inhomogeneous image primitives

Two main motivations are at the basis of the current interest of computer vision researchers in grouping methods: psychophysical evidence about the presence of pre-attentive mechanisms in human vision and expected reduction in computational complexity of recognition tasks. In this paper, a new probabilistic approach to grouping is proposed which is based on the representation of descriptive primitives (DPs) of different kind as sets of random variables associated with nodes of a relational graph. Grouping is modelled as the operation of assigning integer values to one among the variable of a graph node, i.e. as a labeling process. The set of random variables is described as a Markov random field with a multiple neighbourhood system. Each neighbourhood system is based on a different geometrical relation between nodes. The energy function of the field can be considered as a computational expression for some Gestalt laws which have been suggested by several psychologists as basic perceptual criteria. Two different shape descriptive primitives (i.e., circular arcs and straight segments) are here used to show the feasibility of the approach for a specific application which consists in the crowding evaluation and characterization of a surveilled environment.<<ETX>>
A Multi-View Learning Approach to Deception Detection

Recently, automatic deception detection has gained momentum thanks to advances in computer vision, computational linguistics and machine learning research fields. The majority of the work in this area focused on written deception and analysis of verbal features. However, according to psychology, people display various nonverbal behavioral cues, in addition to verbal ones, while lying. Therefore, it is important to utilize additional modalities such as video and audio to detect deception accurately. When multi-modal data was used for deception detection, previous studies concatenated all verbal and nonverbal features into a single vector. This concatenation might not be meaningful, because different feature groups can have different statistical properties, leading to lower classification accuracy. Following this intuition, we apply, for the first time in deception detection, a multi-view learning (MVL) approach, where each view corresponds to a feature group. This results in improved classification results over the state of the art methods. Additionally, we show that the optimized parameters of the MVL algorithm can give insights into the contribution of each feature group to the final results, thus revealing the importance of each feature and eliminating the need of performing feature selection as well. Finally, we focus on analyzing face-based low level, not hand crafted features, which are extracted using various pre-trained Deep Neural Networks (DNNs), showing that face is the most important nonverbal cue for the detection of deception.
HMM-based geometric signatures for compact 3D face representation and matching

3D face recognition(s) systems improve current 2D image-based approaches, but in general they are required to deal with larger amounts of data. Therefore, a compact representation of 3D faces is often crucial for a better manipulation of data, in the context of 3D face applications such as smart card identity verification systems. We propose a new compact 3D representation by focusing on the most significant parts of the face. We introduce a generative learning approach by adapting Hidden Markov Models (HMM) to work on 3D meshes. The geometry of local area around face fiducial points is modeled by training HMMs which provide a robust pose invariant point signature. Such description allows the matching by comparing the signature of corresponding points in a maximum-likelihood principle. We show that our descriptor is robust for recognizing expressions and performs faster than the current ICP-based 3D face recognition systems by maintaining a satisfactory recognition rate. Preliminary results on a subset of the FRGC 2.0 dataset are reported by considering subjects under different expressions.
Augmented scene modeling and visualization by optical and acoustic sensor integration

In this paper, underwater scene modeling from multisensor data is addressed. Acoustic and optical devices aboard an underwater vehicle are used to sense the environment in order to produce an output that is readily understandable even by an inexperienced operator. The main idea is to integrate multiple-sensor data by geometrically registering such data to a model. The geometrical structure of this model is a priori known but not ad hoc designed for this purpose. As a result, the vehicle pose is derived and model objects can be superimposed upon actual images, thus generating an augmented-reality representation. Results on a real underwater scene are reported, showing the effectiveness of the proposed approach.
Optimizing interaction force for global anomaly detection in crowded scenes

This paper presents a novel method for global anomaly detection in crowded scenes. The proposed method introduces the Particle Swarm Optimization (PSO) method as a robust algorithm for optimizing the interaction force computed using the Social Force Model (SFM). The main objective of the proposed method is to drift the population of particles towards the areas of the main image motion. Such displacement is driven by the PSO fitness function aimed at minimizing the interaction force, so as to model the most diffused and typical crowd behavior. Experiments are extensively conducted on public available datasets, namely, UMN and PETS 2009, and also on a challenging dataset of videos taken from Internet. The experimental results revealed that the proposed scheme outperforms all the available state-of-the-art algorithms for global anomaly detection.
Multiple geometrical filters for 3D scale-invariant image representation of planar objects

A low-level approach for 3-D invariant recognition of planar object from a centered monocular view is presented. The proposed scheme uses a distributed bank of filters that perform geometrical mapping on spatial image domain to generate a multidimensional (4-D) description. This image representation achieves invariance to 2-D rotation, scaling and perspective projection. Invariant classification and pose estimation, in terms of slant and tilt of the planar object, are obtained by direct matching between the representation and an undistorted reference model.<<ETX>>
Person re-identification by symmetry-driven accumulation of local features

In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.
Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space

In general terms, surveillance and monitoring technologies aim at understanding what people do in a given environment, whether this means to ensure the safety of workers on the factory floor, to detect crimes occurring in indoor or outdoor settings, or to monitor the flow of large crowds through public spaces. However, surveillance and monitoring technologies rarely consider that they analyze human behavior, a phenomenon subject to principles and laws rigorous enough to produce stable and predictable patterns corresponding to social, affective, and psychological phenomena. On the other hand, these phenomena are the subject of other computing domains, in particular Social Signal Processing and Affective Computing, that typically neglect scenarios relevant to surveillance and monitoring technologies, especially when it comes to social and affective dimensions of space in human activities. The goal of this paper is to show that the investigation of the overlapping area between surveillance and monitoring on one side, and Social Signal Processing and Affective Computing on the other side can bring significant progress in both domains and open a number of interesting research perspectives.
Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats. However, such approaches perform well only on the long term, after a long conversation has been performed, this is a problem, since in the early turns of a conversation, much important information can be stolen. This paper focuses on this issue, presenting a learning approach which boosts the performance of user recognition and verification, allowing to recognize a subject with considerable accuracy. The proposed method is based on a recent framework of one-shot multi-class multi-view learning, based on Reproducing Kernel Hilbert Spaces (RKHS) theory. Our technique reaches a recognition rate of 76.9% in terms of AUC of the Cumulative Matching Characteristic curve, with only 10 conversational turns considered, on a total of 78 subjects. This sets the new best performances on a public conversation benchmark.
Distance Penalization and Fusion for Person Re-identification

This paper presents a novel person re-identification framework based on data fusion. The pipeline of the proposed method is composed of two stages. First, a metric learning paradigm is applied on a bunch of distinct feature extractors to produce an ensemble of estimated distance measures, which are subsequently penalized according to their confidence in evidencing the correct matches from the false ones, and averaged as to draw a final decision. Second, the close persons from the gallery are selected based on the previously fused distance estimates, and utilized to build a dictionary as to reconstruct a given probe pattern. Evaluated on benchmark datasets, the proposed framework advances the state-of-the-art by interesting margins. In particular, Rank1 gains amounting to about 12%, 1%, 6%, and 12%, were scored on VIPeR, CAVIAR4REID, iLIDS, and 3DPeS, respectively.
Efficient on-line mosaicing from 3D acoustical images

This paper presents a system for the 3D reconstruction of an underwater environment on the basis of multiple range views from an acoustical camera. The challenge is to provide the reconstruction on-line, as the range views are obtained from the sensor. The final target of the work is to improve the understanding of a human operator driving an underwater remotely operated vehicle (ROV). The acoustic camera provides a sequence of 3D images in real time (about 5 fps in the current version). Data must be registered and fused to generate a unique 3D mosaic in the form of a triangle mesh, which is rendered through a graphical interface. Available technologies for registration and meshing have been modified to match time constraints. We report experiments on real data.
Biomarker Evaluation by Multiple Kernel Learning for Schizophrenia Detection

In this paper, we use the promising paradigm of Multiple Kernel Learning (MKL) to challenge the problem of biomarker evaluation for schizophrenia detection. We use eight different Regions of Interest (ROIs) extracted from Magnetic Resonance Images (MRIs). For each region we evaluate both tissue and geometric properties. We show that with MKL we not only obtain more accurate classifiers than using single source support vector machines (SVMs), feature concatenation and kernel averaging but also we evaluate the relevance of the brain biomarkers in predicting this disease. On a data set of 50 patients and 50 healthy controls we can achieve an increase of 7% accuracy compared to standard methods. Moreover, we are able to quantify the importance of each source of information by highlighting the synergies between the involved brain characteristics.
Simulated annealing approach for the design of unequally spaced arrays

A synthesis method aimed at designing an array antenna is proposed. Simulated annealing (SA), which is a probabilistic methodology used to solve combinatorial optimization problems, has been utilized to optimize the position and the weighting coefficients of the array elements in order to improve the antenna performance. The sensor position and related weighting coefficients are considered as parameters to be tuned in order to constrain the directivity function (i.e., the beam power pattern) of an antenna to satisfy specific requirements. Conventional beamforming is utilized to compute the beam power pattern having the desired properties, such as a narrow width of the main lobe, and side lobe amplitudes under a certain threshold, etc., taking into account the need to reduce to number of sensors with a small spatial aperture. Several results are presented showing a notable improvement in the antenna performance utilizing the SA approach with respect to those considered in the literature.
Virtual environment modeling by integrated optical and acoustic sensing

In this paper, the problem of underwater scene understanding from multisensory data is addressed. Acoustic and optical devices onboard an underwater vehicle are used to sense the environment in order to produce an output which is readily understandable even by an inexperienced operator. The main idea is to integrate multiple sensory data by geometrically registering data to a model. In this way vehicle pose is derived, and the model objects can be superimposed on actual images, generating an augmented reality representation. Results on a real underwater scene are provided, showing the effectiveness of the proposed approach.
When Kernel Methods Meet Feature Learning: Log-Covariance Network for Action Recognition From Skeletal Data

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.