nils-herrmann/clean_scirepeval huggingface dataset:
A Markov random field approach for restoration of underwater acoustic images

This paper describes a method for the restoration of underwater 3D acoustic images. Beamforming is used to build a 3D map associated with another image representing the confidence (i.e. reliability) of measures. Beamforming processes backscattered echoes (received by a two-dimensional array antenna) coming from a scene previously insonified, forming two images where each pixel represents the distance from the sensor in a specified direction and the confidence (reliability) of measures. In the proposed algorithm, depth and confidence images are modelled as Markov random fields whose associated probability distributions are specified by a single energy functional. This functional embeds a-priori knowledge on the images and on the noise affecting measurements in the image formation process. Confidence measures are also considered to improve the restoration. The maximum a-posteriori (MAP) criterion is adopted to perform restoration of both images and simulated annealing is used to find the optimal MAP estimate. Experimental results show the improvement in the resulting images as compared with initial data.
Statistical 3D Shape Analysis by Local Generative Descriptors

In this paper, we propose a new approach for surface representation. Generative models are exploited for encoding the variations of local geometric properties of 3D shapes. Surfaces are locally modeled as a stochastic process which spans a neighborhood area through a set of circular geodesic pathways, captured by a modified version of a Hidden Markov Model (HMM) named multicircular HMM (MC-HMM). The approach proposed consists of two main phases: 1) local geometric feature collection and 2) MC-HMM parameter estimation. The effectiveness of our proposal is demonstrated by several applicative scenarios, all using well-known benchmark data sets, such as multiple view registration, matching of deformable shapes, and object recognition on cluttered scenes. The results achieved are very promising and open up the use of generative models as geometric descriptors in an extensive range of applications.
Semantic Multi-body Motion Segmentation

This paper presents a method to deal with the multi-body segmentation problem using a set of 2D points matches between two views. The key feature of our approach is the explicit inclusion of a higher semantic information as given by general purpose object detectors that boost the segmentation of the moving objects. In the classical formulation of the problem, only 2D matched points between views are used to identify independently moving objects based on the principle that a set of points belonging to a moving object would satisfy some given multi-view relations (e.g. multi-body epipolar constraints). We improve and speedup such process by including the information that a set of 2D matches may belong to the same object given the output of a detector. As such, instead of sampling points uniformly with a RANSAC based strategy, the selection of the matches is driven by the position and score confidence of the object detectors. Evaluation on challenging synthetic and real datasets shows a remarkable improvement in respect to previous approaches, regarding both the number of iterations required to segment a scene and the effectiveness of the segmentation itself, often making the difference between satisfying segmentation and almost complete failure.
Three-dimensional image generation and processing in underwater acoustic vision

Underwater exploration is becoming more and more important for many applications involving physical, biological, geological, archaeological, and industrial issues. This paper aims at surveying the up-to-date advances in acoustic acquisition systems and data processing techniques, especially focusing on three-dimensional (3-D) short-range imaging for scene reconstruction and understanding. In fact, the advent of smarter and more efficient imaging systems has allowed the generation of good quality high-resolution images and the related design of proper techniques for underwater scene understanding. The term acoustic vision is introduced to generally describe all data processing (especially image processing) methods devoted to the interpretation of a scene. Since acoustics is also used for medical applications, a short overview of the related systems for biomedical acoustic image for motion is provided. The final goal of the paper is to establish the state of-the art of the techniques and algorithms for acoustic image generation and processing, providing technical details and results for the most promising techniques, and pointing out the potential capabilities of this technology for underwater scene understanding.
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation

In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to separate the modes of a multimodal density by using a kernel-based technique. Here, the attention is focused on the selection of the kernel bandwidth which typically strongly affects the level of accuracy of the segmentation results. In particular, a set of geometric features is computed from each 3D point of the given data. This set is projected onto a number of independent sub-spaces, each one associated to a different estimated feature, and overall forming a joint multidimensional (feature) space. In this space, we propose a method for selecting the best multidimensional kernel bandwidth in an automatic fashion, based on stability criteria. The final kernel considers each sub-space in an adaptive way in relation to the discrimination power of each feature, leading to accurate results when dealing with different types of 3D data
Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly $98$ % given different tests with different types of residuals and duct structures.
Stereo-Based Framework for Pedestrian Detection with Partial Occlusion Handling

The pedestrian detection literature has been recently extended by the availability of large-scale multisensory datasets, able to capture complementary aspects of the objects of interest, namely, appearance, motion, and depth. In this paper, we exploit this multimodal scenario to propose a new set of composite descriptors dubbed CO2, CO-variances of visual features and CO-occurrences of depth fields. Covariances of visual features allow us to integrate at low-level heterogeneous visual cues related to intensity and texture. Co-occurrences of depth fields are brand new descriptors, which use range information for characterizing the global shape of a pedestrian while being also able to identify its occluded parts. This paper illustrates how these descriptors can be instantiated and combined together, improving detection capabilities taking also benefit from the proper handling of occlusions. Experimental results show that CO2, fed into a standard discriminative classification system, set state-of-the-art performances on recent multi-modal intensity- and stereo-based pedestrian datasets.
Sleep-stage scoring in mice: The influence of data pre-processing on a system's performance

Sleep-stage analysis in mice and rats has received growing attention in recent years, due to the fact that mice display electrical activity during sleep which has underlying similarities with that of human sleep. Both conventional manual and automatic sleep-wakefulness scoring are rule based tasks which use brain waves measured by Electroencephalogram (EEG) and activity detected by Electromyography (EMG) of skeletal muscles. Several works have been conducted trying to provide an automatic sleep-scoring system on the basis of machine learning methods. In this study we try to understand the reasons behind the complexity of this problem and we emphasize the importance of normalization procedure that leads to a better stage discrimination comparing different classification methods.
Mosaicing of 3D sonar data sets - techniques and applications

This paper presents a system for registration of 3D sonar data sets based on the Echoscope 1600 3D sonar. Although a 3D sonar image provides instantaneous volumetric data sets, many applications require the data sets to be mosaic'ed into larger data sets. The lack of navigation and motion sensors being able to produce mosaics with accuracies comparable to the 3D sonar resolution necessitates the use of registration techniques. Techniques for filtering of 3D sonar data, mesh generation, registration and geometric fusion applied to both on-line and offline mosaicing will be discussed. On-line mosaicing is done at a rate of 5 to 10 data sets per second, a rate that is acceptable in most practical situations. The techniques will find a number of applications, such as scanning harbors and seabed for potentially dangerous objects as well as seabed surveys. As the registration provides the sensor position relative to an object, object related positioning and navigation is also feasible, either as a stand alone underwater navigation system or as a system that improves current techniques like LBL, SBL and others.
A hybrid generative/discriminative classification framework based on free-energy terms

Hybrid, generative-discriminative, techniques have proven to be valuable approaches in tackling difficult object or scene recognition problems. In general, a generative model over the available data for each image class is first learned providing a relatively comprehensive statistical multi-level representation. In this way, new meaningful image features become available, which encode the degree of fitness of the data with respect to the model at different representation levels. Such features are then fed into a discriminative classifier which can exploit the intrinsic data separability. In this paper, we propose the use of variational free energy terms as feature vectors, so that the degree of fitness of the data and the uncertainty over the generative process are explicitly included in the data description. The proposed method is automatically superior to a pure generative classification, and we also experimentally validate it on a wide selection of generative models applied to challenging benchmarks in hard computer vision tasks such as scene, object, and shape recognition. In several instances, the proposed approach outperforms the current state-of-the-art techniques as for classification results, while also showing to be computationally inexpensive.
Latent subcategory models for pedestrian detection with partial occlusion handling

Pedestrian detection is one of the most important tasks in Computer Vision, especially in automotive and security applications. One of the most common problems in real scenarios is related to the detection of occluded pedestrians. In this paper, we propose a novel multi-cue pedestrian detection approach able to deal with non homogeneous object samples by learning latent subcategory models trained on both visual and depth-based features. We also propose a novel self-similarity based feature, namely SSTD, to encode the homogeneity in appearance of pedestrians characterized by similar occlusion patterns. Experiments are performed on the Daimler Pedestrian Detection Benchmark Dataset showing the robustness of our approach in actual scenarios.
Model tracking for video-based virtual reality

This paper presents a technique for tracking complex objects (both polyhedral and smooth boundaries) in a monocular sequence. Our aim is to use this model tracking method in an augmented reality context to compute the pose of a real object to be able to register it with a synthetic one. A scalar score function for an object pose is defined, based on the local image gradient along the projected model boundaries. A local search is then carried out in the configuration space of the pose to maximize the score. This technique is robust to occlusions, since the whole object contour is used, not just a few control points. The proposed method is effective yet simple. No image feature extraction is necessary and no complex temporal evolution is used. Experimental results with a real sequence show the good performance of our technique.
Neuronal network structural connectivity estimation by probabilistic features and graph heat kernels

It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.
Decentralized particle filter for joint individual-group tracking

In this paper, we address the task of tracking groups of people in surveillance scenarios. This is a major challenge in computer vision, since groups are structured entities, subjected to repeated split and merge events. Our solution is a joint individual-group tracking framework, inspired by a recent technique dubbed decentralized particle filtering. The proposed strategy factorizes the joint individual-group state space in two dependent subspaces where individuals and groups share the knowledge of the joint individual-group distribution. In practice, we establish a tight relation of mutual support between the modeling of individuals and that of groups, promoting the idea that groups are better tracked if individuals are considered, and viceversa. Extensive experiments on a published and novel dataset validate our intuition, opening up to many future developments.
Registration of very time-distant aerial images

We address the alignment of historical and present-day aerial photographs. Historical images refer to regions bombed during the Second World War. In these regions, the risk of unexploded bombs is still high, especially where the bombing was more frequent. Alignment is required to fill in an unexploded bombs risk map. The task is challenging because many features in the historical images have changed or are missing (and vice versa). Moreover, in the historical images, bomb craters introduce large gray level variations so that it is difficult to extract features automatically. This work propose a semi-automatic application for image alignment in order to improve accuracy and to speed up the alignment process.
Geo-located image analysis using latent representations

Image categorization is undoubtedly one of the most challenging open problems faced in computer vision, far from being solved by employing pure visual cues. Recently, additional textual ldquotagsrdquo can be associated to images, enriching their semantic interpretation beyond the pure visual aspect, and helping to bridge the so-called semantic gap. One of the latest class of tags consists in geo-location data, containing information about the geographical site where an image has been captured. Such data motivate, if not require, novel strategies to categorize images, and pose new problems to focus on. In this paper, we present a statistical method for geo-located image categorization, in which categories are formed by clustering geographically proximal images with similar visual appearance. The proposed strategy permits also to deal with the geo-recognition problem, i.e., to infer the geographical area depicted by images with no available location information. The method lies in the wide literature on statistical latent representations, in particular, the probabilistic latent semantic analysis (pLSA) paradigm has been extended, introducing a latent aspect which characterizes peculiar visual features of different geographical zones. Experiments on categorization and georecognition have been carried out employing a well-known geographical image repository: results are actually very promising, opening new interesting challenges and applications in this research field.
Crowd motion monitoring using tracklet-based commotion measure

Abnormal detection in crowd is a challenging vision task due to the scarcity of real-world training examples and the lack of a clear definition of abnormality. To tackle these challenges, we propose a novel measure to capture the commotion of a crowd motion for the task of abnormality detection in crowd. The unsupervised nature of the proposed measure allows to detect abnormality adaptively (i.e. context dependent) with no training cost. The extensive experiments on three different levels (e.g. pixel, frame and video) show the superiority of the proposed approach compared to the state of the arts.
View synthesis from uncalibrated images using parallax

This work deals with the view synthesis problem, i.e., how to generate snapshots of a scene taken from a "virtual" viewpoint different from all the viewpoints of the real views. Starting from uncalibrated reference images, the geometry of the scene is recovered by means of the relative affine structure. This information is used to extrapolate novel views using planar warping plus parallax correction. The contributions of this paper are twofold. First we introduce an automatic method for specifying the virtual viewpoint based on the replication of the epipolar geometry linking two reference views. Second, we present a method for generating synthetic views of a soccer ground starting from a single uncalibrated image. Experimental results using real images are shown.
Analyzing Tracklets for the Detection of Abnormal Crowd Behavior

This paper presents a novel video descriptor, referred to as Histogram of Oriented Tracklets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called tracklets in the literature. Following the standard procedure, we divided video sequences in spatio-temporal cuboids within which we collected statistics on the tracklets passing through them. In particular, we quantized orientation and magnitude in a 2-dimensional histogram which encodes the motion patterns expected in each cuboid. We classify frames as normal and abnormal by using Latent Dirichlet Allocation and Support Vector Machines. We evaluated the effectiveness of the proposed descriptors on three datasets: UCSD, Violence in Crowds and UMN. The experiments demonstrated (i) very promising results in abnormality detection, (ii) setting new state-of-the-art on two of them, and (iii) outperforming former descriptors based on the optical flow, dense trajectories and the social force model.
Traces of human functional activity: Moment-to-moment fluctuations in fMRI data

Dynamic functional connectivity (dFC) measured by functional magnetic resonance imaging (fMRI) shows evidence of large-scale networks with highly dynamic (re)configurations. We propose a novel approach to extract traces of human brain function by the construction of a trajectory in a meaningful low-dimensional space. This allows studying dFC in more detail and identify possible meaningful brain states from the moment-to-moment fluctuations of the brain signals during resting state or naturalistic conditions such as passive movie watching. Specifically, we explored dynamic organization of sub-networks derived from the time-dependent graph Laplacian in combination with Riemannian manifold distance to measure dissimilarity over time of dFC and to subsequently build the trajectory of brain activity. As a proof-of-principle, we show results for an fMRI dataset containing both rest and movie epochs in 15 healthy participants. The movie condition varied (i.e., fearful, joyful, and neutral movie excerpts) and clearly influenced the subsequent resting-state period in terms of FC brain state.
2LDA: Segmentation for Recognition

Following the trend of “segmentation for recognition”, we present 2LDA, a novel generative model to automatically segment an image in 2 segments, background and foreground, while inferring a latent Dirichlet allocation (LDA) topic distribution on both segments. The idea is to merge two separate modules, LDA and the segmentation module, explicitly considering (and exchanging) the uncertainty between them. The resulting model adds spatial relationships to LDA, which in turn helps in using the topics to segment an image. The experimental results show that, unlike LDA, our model can be used to recognize objects, and also outperforms the state of the art algorithms.
Dirichlet Process Mixtures of Multinomials for Data Mining in Mice Behaviour Analysis

Automatic analysis of rodents behaviour has received growing attention in recent years as rodents are the reference species for large scale pharmacological and genetic screenings. In this paper we propose a new method to identify prototypical high-level behavioural patterns which go beyond simple atomic actions. The method is embedded in a data mining pipeline thought to support behavioural scientists in exploratory data analysis and hypothesis formulation. A case study is presented where the method is capable of learning high-level behavioural prototypes which help discriminating between two strains of mouse having known differences in their behaviour.
Kernelized covariance for action recognition

In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV, which generalizes the original covariance representation without compromising the efficiency of the computation. In the experiments, we validate the proposed framework against many previous approaches in the literature, scoring on par or superior with respect to the state of the art on benchmark datasets for 3D action recognition.
Acoustic image improvement by confidence levels

This paper proposes a method for improving the quality of 2D and 3D underwater acoustic images, operating directly on the information extracted from each beam signal computed by a beamforming process. Some problems may arise when an acoustic image is built directly:on the basis of the envelope of the beam signals (as it is generally done) and this affects the quality of the results. The image quality can improve by using a general criterion operating as an interface between the beam signals and the image pixels. For each beam signal, this measure, called confidence level, assesses the reliability of the actual echoes in function of the similarity with expected echoes. Results obtained, in both real underwater 2D and simulated 3D images, are reported