nils-herrmann/clean_scirepeval huggingface dataset:
Non-linear generative embeddings for kernels on latent variable models

Generative embeddings use generative probabilistic models to project objects into a vectorial space of reduced dimensionality - where the so-called generative kernels can be defined. Some of these approaches employ generative models on latent variables to project objects into a feature space where the dimensions are related to the latent variables. Here, we propose to enhance the discriminative power of such spaces by performing a non-linear mapping of space dimensions leading to the formulation of novel generative kernels. In this paper, we investigate one possible non-linear mapping, based on a powering operation, able to equilibrate the contributions of each latent variable of the model, thus augmenting the entropy of the latent variables vectors. The validity of the idea has been shown in the case of two generative kernels, which have been evaluated with tests on shape recognition and gesture classification, with really satisfying results that outperform state-of-the-art methods.
3D acoustic image segmentation by a RANSAC-based approach

In this paper, a new technique for 3D acoustic image segmentation and modelling is proposed. Especially, in the underwater environment, in which optical sensors suffer from visibility problems, the acoustical devices may provide efficient solutions, but, on the other hand, acoustic image interpretation is surely more difficult for a human operator. The proposed application involves the use of an acoustic camera which directly acquires images structured as a set of 3D points. Due to the noisy nature of this type of data, the segmentation problem becomes more challenging and the standard algorithms for range image segmentation are likely to fail. The proposed method is based on a simplified version of the so called recover and select paradigm in which the seed areas, from which the segmentation starts, are generated by adopting a robust approach based on the RANSAC (RANdom Sample And Consensus) algorithm. Superquadric primitives are directly recovered from raw data without any pre-segmentation processing. Experimental trials using real acoustical images confirm the goodness of the method, and a large robustness of the resulting segmented images, associated to a relatively low computational load.
Audio-Visual Foreground Extraction for Event Characterization

This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities.
Significance of envelope detection in underwater acoustic imaging systems

This paper deals with the significance of the envelope detection operation, commonly performed on the output signals generated by a sonar imaging system, before of the visualisation. Although imaging systems are often linear processes, envelope detection destroys phase information, thus producing a nonlinear contribution. Within this paper, the relative importance of the nonlinear contribution is evaluated, in a general way, and its incidence in real sonar systems has been analysed, in a practical way, by performing several simulations. Simulations results have pointed out a considerable incidence of such nonlinearities on the quality of obtained images.
Summarization and Classification of Wearable Camera Streams by Learning the Distributions over Deep Features of Out-of-Sample Image Sequences

A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially for images which are veering too far away from the training distribution. This can lead to surprising results as an imperceptible change in an image can be enough to completely change the predicted class. This occurs in particular in applications involving personal data, typically acquired with wearable cameras (e.g., visual lifelogs), where the problem is also made more complex by the dearth of new labeled training data that make supervised learning with deep models difficult. To alleviate these problems, in this paper we propose a new generative model that captures the feature distribution in new data. Its latent space then becomes more representative of the new data, while still retaining the generalization properties. In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.
A joint structural and functional analysis of in-vitro neuronal networks

The acquisition, analysis and representation of experimental data describing both anatomical and functional information at cellular level is an innovative opportunity to investigate neuronal network processing and organization. In this paper we propose an image processing pipeline to study in-vitro neuronal networks with a joint analysis of anatomy and electrophysiology. Neuronal nuclei are detected by segmenting fluorescence images of neuronal cultures. The high resolution Multi Electrode Arrays (MEAs) technology is used to collect functional information on cellular electrophysiological activity. Finally, detailed maps, representing both structural and functional information, are obtained which provide statistics on neuron distribution and spiking activity.
A numerical and symbolic fusion method for interpretation of image sequence

The problem of analyzing a sequence of images by taking into account the symbolic and numerical content of the signal is considered. An algorithm for segmentation and tracking of regions among images of a sequence is presented. The method is based on a Gibbs Markov random field (GMRF) model which couples the image process to a spatial-temporal region process. Optical flow field is used to adaptively decide the temporal clique to be used during annealing of energy. Displacement vectors of pixels belonging to recognized regions are predicted by using prior knowledge about object behavior.<<ETX>>
Extraction of geographical entities from aerial images

A complete system for extracting geographical entities from aerial images is proposed. The method treats separately the extraction of green areas, forestry and buildings from the extraction of roads. The former issue is addressed with a color segmentation followed by a region growing approach. The latter issue is addressed by a probabilistic contour tracking approach, able to follow roads in highly complex images showed that the proposed method is able to extract most part of the geographical entities present in the image, even in highly urbanized areas.
Acoustic Range Image Segmentation by Effective Mean Shift

Image perception in underwater environment is a difficult task for a human operator, and data segmentation becomes a crucial step toward an higher level interpretation and recognition of the observing scenarios. This paper contributes to the related state of the art, by fitting the mean shift clustering paradigm to the segmentation of acoustical range images, providing a segmentation approach in which whatever parameter tuning is absent. Moreover, the method exploits actively the connectivity information provided by the range map, by using reverse projection as acceleration technique. Therefore, the method is able to produce, starting from raw range data, meaningful segmented clouds of points in a fully automatic and efficient fashion.
Stel component analysis: Modeling spatial correlations in image class structure

As a useful concept in the study of the low level image class structure, we introduce the notion of a structure element - `stel.' The notion is related to the notions of a pixel, superpixel, segment or a part, but instead of referring to an element or a region of a single image, stel is a probabilistic element of an entire image class. Stels often define clear object or scene parts as a consequence of the modeling constraint which forces the regions belonging to a single stel to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of most meaningful image parts, even when in different images of similar objects the corresponding parts may not have similar local measurements. The stel itself is expected to be consistent within a class, yet flexible, which we accomplish using a novel approach we dubbed stel component analysis. Experimental results show how stel component analysis can assist in image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.
Synthesis of unequally spaced arrays by simulated annealing

Simulated annealing is applied to the synthesis of arrays in order to reduce the peaks of side lobes by acting on the elements' positions and weight coefficients. In the case considered, the number of array elements and the spatial aperture of an unequally spaced array are a priori fixed. Thanks to the high flexibility of simulated annealing, the results obtained for a 25-element array over an aperture of 50/spl lambda/ improve those reported in the literature.
Unsupervised mouse behavior analysis: A data-driven study of mice interactions

Automatic analysis of rodent behavior has been receiving growing attention in recent years since rodents have been the reference species for many neuroscientific studies, with the social interaction being among the subjects of the most important ones. Systems that are employed in these studies are mainly based on tracking of mice and activity classification through supervised learning methods, trained on datasets manually annotated by experts. In this paper, we introduce a completely unsupervised way of analysing tracking data for the automatic identification of social and non-social behaviors using models capable of spotting regularities in the data. In particular, a mean-covariance Restricted Boltzmann Machine is employed to abstract higher-level behavioral configurations of mice interacting in an arena for a long time.
Multilevel GMRF-based segmentation of image sequences

A probabilistic method for obtaining a complete image representation on the basis of spatial-temporal knowledge is presented. The main goal of the algorithm is to obtain a consistent segmentation of a noisy image sequence. Consistent means that the same region must maintain the same label in all consequent images of the sequence where it appears. To this end, a processing scheme is presented which extends Bayesian networks of Gibbs-Markov random fields (GMRF) to segmentation of dynamic scenes.<<ETX>>
High resolution video mosaicing with global alignment

Image composition (or mosaicing) has attracted a growing attention in recent years as one of the main elements in video analysis and representation. In this paper we deal with the problem of global alignment and super-resolution. We also propose to evaluate the quality of the resulting mosaic by measuring the amount of blurring. Global registration is achieved by combining a graph-based technique that exploits the topological structure of the sequence induced by the spatial overlap - with a bundle adjustment which uses only the homographies computed in the previous steps. Experimental comparison with other techniques shows the effectiveness of our approach.
A closed form solution to the microphone position self-calibration problem

This paper presents a novel algorithm for the automatic 3D localization of a set of microphones in an unknown environment. Given the times of arrival at each microphone of a set of sound events, the approach simultaneously estimates the 3D positions of the sensors and the sources that have generated the events. The only assumption made is that the emission time of the sound events must be known in order to measure the time of flight for each event. A closed form solution is also proposed whenever a sound event coincides with a microphone position. Simulated and real experiments show the validity of the approach for different setups of sensors and number of events.
Online subjective feature selection for occlusion management in tracking applications

Most of the state-of-the-art tracking algorithms are prone to error when dealing with occlusions, especially when the involved moving objects are hardly discernible in appearance. In this paper, we propose a multi-object particle filtering tracking framework particularly suited to manage the occlusion problem. The presented solution consists in the introduction of a online subjective feature selection mechanism, which highlights and employs the most discriminant features characterizing a single object with respect to the neighbouring objects. The policy adopted fits formally in the observation step of the particle filtering process, it is effective and not computationally costly. Trials carried out on illustrative synthetic data and on recent challenging benchmark sequences report compelling performances and encourage further development of the technique.
Adaptive camera regulation for investigation of real scenes

An adaptive strategy for regulating the intrinsic parameters of a charge coupled device (CCD) visual camera is presented. The parameter regulation procedure is based on the computation of the "quality" of an acquired signal by means of a set of functions which estimate the goodness of the datum. Each function performs a quantitative evaluation by computing several features derived from the high-frequency content of an image or from the histogram analysis. The regulation strategy uses such values to compute the actual values of the camera parameters, i.e., focusing distance, aperture diameter, electronic gain, and black level. Moreover, the same strategy can be applied both to the whole image and to smaller subareas, therefore providing a focus-of-attention mechanism. This is useful for automatic investigation and control of environments subject to dynamically changing conditions (e.g., illumination and structure of a scene), whenever it is not possible to interact directly with such environments.
A comparison of crowd commotion measures from generative models

Detecting abnormal events in video sequences is a challenging task that has been broadly investigated over the last decade. The main challenges come from the lack of a clear definition of abnormality and from the scarcity, often absence, of abnormal training samples. To address these two shortages, the computer vision community made use of generative models to learn normal behavioral patterns in videos. Then, for each test observation, a (crowd) commotion measure is computed quantifying the deviation from the normal model. In this paper, we evaluated two different families of generative models, namely topic models, representing the standard choice, and the most recent Counting Grids which have never been considered for this task. Moreover, we also extended the 2D Counting Grid, introduced for the analysis of images, to three dimensions, making the model able to capture the spatial-temporal relationships of the videos. In the experimental section, we compared all the approaches on five challenging sequences showing the superiority of the 3-D counting grid.
CREST: Convolutional Residual Learning for Visual Tracking

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from the end-to-end training. In this paper, we propose the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network. Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. To reduce model degradation during online update, we apply residual learning to take appearance changes into account. Extensive experiments on the benchmark datasets demonstrate that our CREST tracker performs favorably against state-of-the-art trackers.
Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layer-based solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.
Robust Object Tracking with Online Multiple Instance Learning

In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.
Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking

Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Online Object Tracking With Sparse Prototypes

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l1 regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
Top-down visual saliency via joint CRF and dictionary learning

Top-down visual saliency facilities object localization by providing a discriminative representation of target objects and a probability map for reducing the search space. In this paper, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a discriminative dictionary. The proposed model is formulated based on a CRF with latent variables. By using sparse codes as latent variables, we train the dictionary modulated by CRF, and meanwhile a CRF with sparse coding. We propose a max-margin approach to train our model via fast inference algorithms. We evaluate our model on the Graz-02 and PASCAL VOC 2007 datasets. Experimental results show that our model performs favorably against the state-of-the-art top-down saliency methods. We also observe that the dictionary update significantly improves the model performance.
Learning shape priors for object segmentation via neural networks

We present a joint algorithm for object segmentation that integrates both global shape and local edge information in a deep learning framework. The proposed architecture uses convolutional layers to extract image features, followed by a fully connected section to represent shapes specific to a given object class. This preliminary mask is further refined by matching segmentation mask patches to local features. These processing steps facilitate learning the shape priors effectively with a feedforward pass rather than complex inference methods. Furthermore, our novel convolutional refinement stage presents a convincing alternative to Conditional Random Fields, with promising results on multiple datasets.