nils-herrmann/clean_scirepeval huggingface dataset:
Stochastic optimization of linear sparse arrays

In conventional beamforming systems, the use of aperiodic arrays is a powerful way to obtain high resolution employing few elements and avoiding the presence of grating lobes. The optimized design of such arrays is a required task in order to control the side-lobe level and distribution. In this paper, an optimization method aimed at designing aperiodic linear sparse arrays with great flexibility is proposed. Simulated annealing, which is a stochastic optimization methodology, has been utilized to synthesize the positions and the weight coefficients of the elements of a linear array in order to minimize the peak of the sidelobes and to obtain a beam pattern that meets given requirements. An important novelty is the fact that the latter goal can be achieved in parallel to the minimization of both the number of elements and the spatial aperture, resulting in a "global" optimization of the array characteristics. The great freedom that simulated annealing allows in defining the energy function to be minimized is the main reason for the notable versatility and the good results of the proposed method. Such results show an improvement in the array characteristics and performances over those reported in the literature.
A distributed algorithm for adaptive regulation of image processing parameters

The authors describe an algorithm able to regulate and control the parameters that manage some common processing algorithms, to obtain the best data quality. The processing chain to be regulated consists of an acquisition module and two filtering processes. The system has been implemented as an expert system, which, in addition to the processing units, includes a regulation module. Each regulation module has been specialized with knowledge about the parameters regulating the related processing unit. Criteria to judge the quality of data at each level have been devised to develop methods to select and regulate the processing parameters to be adjusted. The system is described specifying the knowledge-based architecture and single units where knowledge is embedded. The functioning of each processing module is illustrated by defining the parameters used to judge data and those used to regulate the units. Results on a set of indoor images, acquired by using different camera parameters, are reported.<<ETX>>
A tensor voting approach for the hierarchical segmentation of 3-D acoustic images

We present a hierarchical and robust algorithm addressing the problem of filtering and segmentation of three-dimensional acoustic images. This algorithm is based on. the tensor voting approach - a unified computational framework for the inference of multiple salient structures. Unlike most previous approaches, no models or prior information of the underwater environment, nor the intensity information of acoustic images is considered in this algorithm. Salient structures and outlier noisy points are directly clustered in two steps according to both the density and the structural information of input data. Our experimental trials show promising results, very robust despite the low computational complexity.
A distributed approach to 3D road scene recognition

A distributed 3D scene recognition system based on a multilevel representation of object models and signals is described. The solution to a recognition problem is obtained through a set of object-observation couples at the different abstraction levels. The various system modules exchange two kinds of information: (1) top-down messages, which are used to communicate to lower modules the predictions made on the basis of a priori knowledge on the application domain, (2) bottom-up messages, which are used to communicate to higher modules the evidence supporting possible local solutions. A local scheme for the combination of message flows is defined, and messages are interpreted by using a probabilistic network of estimators of random variables. The proposed model is suitable for addressing the problem of distributed geometric reasoning aimed at 3D road scene recognition by an autonomous vehicle. Recognition results include road detection and obstacle localization, together with a study of the relative computational load required by different modules of the system. The proposed approach is currently simulated on a workstation, while an effective implementation on board of an autonomous vehicle is under development in the contest of the CEC-EUREKA Prometheus project.
FPGA-based robust ellipse estimation for circular road sign detection

Estimating parametric curves from images using robust fitting algorithms is a well-known and important computer vision task. We present a complete FPGA design and implementation of a fast and robust model fitting algorithm for real-time ellipse detection on video streams. The proposed solution relies on a the RANSAC algorithm, modified for FPGA deployment, in combination with an image-preprocessing pipeline in order to perform the intensive pixel-level analysis, reducing each frame to a simple binary image of edges. The design has been developed in a parallel fashion and with specific architectural solutions so as to allow a fast response without degrading the functional performances. Experimental results on synthetic and real data show that our implementation, synthesized onto a Xilinx Spartan-3A DSP 3400A device, succesfully runs in real-time with a low resource occupation, while maintaining a functionality comparable with the floating-point software version.
Manifold Constrained Low-Rank Decomposition

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from rotation or viewpoint changes. We leverage the specific structure of data in order to improve the performance of LRD when the data are not ideal. To this end, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design an alternating direction method of multipliers (ADMM) method which efficiently integrates the manifold constraints during the optimization process. The proposed approach is successfully used to calculate low-rank models from face images, hand-written digits and planar surface images. The results show a consistent increase of performance when compared to the state-of-the-art over a wide range of realistic image misalignments and corruptions.
A geometric approach to the segmentation and reconstruction of acoustic three-dimensional data

This paper presents a technique for the segmentation of three-dimensional (3D) images acquired by an acoustical camera. The proposed algorithm identifies first the most reliable image points likely corresponding to man-made objects, and, second, determines the points belonging to the same surface. Actually, it exploits the geometrical properties embedded in the sparse and noisy 3D information available to group the points which better fit the current quadric surface. This algorithm can be applied for the reconstruction of virtual environments from acoustic data, useful for robotic applications (e.g., vehicle navigation). Results on synthetic and real acoustic images are promising.
A multimodal electronic travel aid device

This paper describes an electronic travel aid device, that may enable blind individuals to "see the world with their ears". A wearable prototype will be assembled using low-cost hardware: earphones, sunglasses fitted with two micro cameras, and a palmtop computer. The system, which currently runs on a desktop computer, is able to detect the light spot produced by a laser pointer, compute its angular position and depth, and generate a corresponding sound providing auditory cues for perception of the position and distance of the pointed surface patch. It permits different sonification modes that can be chosen by drawing, with the laser pointer, a predefined stroke which will be recognized by a hidden Markov model. In this way a blind person can use a common pointer as a replacement for the cane and will interact with the device using a flexible and natural sketch based interface.
What Will I Do Next? The Intention from Motion Experiment

In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems.In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside).We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.
Novel dataset for fine-grained abnormal behavior understanding in crowd

Despite the huge research on crowd on behavior understanding in visual surveillance community, lack of publicly available realistic datasets for evaluating crowd behavioral interaction led not to have a fair common test bed for researchers to compare the strength of their methods in the real scenarios. This work presents a novel crowd dataset contains around 45,000 video clips which annotated by one of the five different fine-grained abnormal behavior categories. We also evaluated two state-of-the-art methods on our dataset, showing that our dataset can be effectively used as a benchmark for fine-grained abnormality detection. The details of the dataset and the results of the baseline methods are presented in the paper.
Object pose estimation in underwater acoustic images

We address the problem of the recognition of man-made objects and the estimation of the related orientation in 2D acoustic images acquired with a forward looking sonar or an acoustic camera. A voting-based approach is described that is able to recognize objects and to estimate their two-dimensional pose by using information coming from boundary segments and their angular relations. The method is directly applied to the edge discontinuities of underwater acoustic images, whose quality is usually affected by some undesired effects such as object blurring, speckle noise, and geometrical distortions degrading the edge detection. The voting approach is robust with respect to these effects, so that good results are obtained even with images of poor quality. The sequences of simulated and real acoustic images are presented in order to test the validity of the proposed method in terms of the average estimation error and computational load.
Person re-identification with a PTZ camera: An introductory study

We present an introductory study that paves the way for a new kind of person re-identification, by exploiting a single Pan-Tilt-Zoom (PTZ) camera. PTZ devices allow to zoom on body regions, acquiring discriminative visual patterns that enrich the appearance description of an individual. This intuition has been translated into a statistical direct reidentification scheme, which collects two images for each probe subject: the first image captures the probe individual, focusing on the whole body; the second can be a zoomed body part (head, torso or legs) or another whole body image, and is the outcome of an action-selection mechanism, driven by feature selection principles. The validation of this technique is also explored: in order to allow repeatability, two novel multi-resolution benchmarks have been created. On these data, we demonstrate that our approach selects effective actions, by focusing on body portions which discriminate each subject. Moreover, we show that the proposed compound of two images overwhelms standard multi-shot descriptions, composed by many more pictures.
Seeing the Sound: A New Multimodal Imaging Device for Computer Vision

Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acoustic-optic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security.
Part-based human detection on Riemannian manifolds

In this paper we propose a novel part-based framework for pedestrian detection. We model a human as a hierarchy of fixed overlapped parts, each of which described by covariances of features. Each part is modeled by a boosted classifier, learnt using Logitboost on Riemannian manifolds. All the classifiers are then linked to form a high-level classifier, through weighted summation, whose weights are estimated during the learning. The final classifier is simple, light and robust. The experimental results show that we outperform the state-of-the-art human detection performances on the INRIA person dataset.
Using hidden Markov models and wavelets for face recognition

In this paper, a new system for face recognition is proposed, based on hidden Markov models (HMM) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using hidden Markov models. The proposed method is compared with a DCT coefficient-based approach (Kohir et al. (1998)), showing comparable results. By using an accurate model selection procedure, we show that results proposed in Kohir can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a 100% recognition rate. This performance proves the suitability of HMM to deal with the new JPEG2000 image compression standard.
Underwater 3D imaging by FFT dynamic focusing beamforming

Describes an application of the dynamic focusing technique for underwater 3D acoustic imaging. In particular, the integration of dynamic focalization with an FFT implementation of phase-shift beamforming has been developed in order to avoid an increase in computational complexity. The resulting system, which uses narrow-band signals, permits the formation of 3D images at a high frame rate and without the limitations imposed by the depth of field on fixed-focused beamforming. Some simulations made it possible to test the proposed system, and the related results are reported.<<ETX>>
Disparity map restoration by integration of confidence in Markov random fields models

This paper proposes some Markov random field (MRF) models for the restoration of stereo disparity maps. The main aspect is the use of confidence maps provided by the symmetric multiple windows (SMW) stereo algorithm to guide the restoration process. The SMW algorithm is an adaptive, multiple-window scheme using left-right consistency to compute disparity and its associated confidence in the presence of occlusions. The MRF approach allows the combining in a single functional of all the available information: observed data with its confidence, noise, and a-priori hypotheses. Optimal estimates of the disparity are obtained by minimizing an energy functional using simulated annealing. Results with a real stereo pair show the improvement obtained by restoration using the MRF approach integrating confidence data.
FPGA-based pedestrian detection using array of covariance features

In this paper we propose a pedestrian detection algorithm and its implementation on a Xilinx Virtex-4 FPGA. The algorithm is a sliding window-based classifier, that exploits a recently designed descriptor, the covariance of features, for characterizing pedestrians in a robust way. In the paper we show how such descriptor, originally suited for maximizing accuracy performances without caring about timings, can be quickly computed in an elegant, parallel way on the FPGA board. A grid of overlapped covariances extracts information from the sliding window, and feeds a linear Support Vector Machine that performs the detection. Experiments are performed on the INRIA pedestrian benchmark; the performances of the FPGA-based detector are discussed in terms of required computational effort and accuracy, showing state-of-the-art detection performances under excellent timings and economic memory usage.
A noncoherent correlation technique and focused beamforming for ultrasonic underwater imaging: a comparative analysis

The purpose of this paper is to describe a noncoherent correlation technique for the formation of 3-D acoustic images, mainly for underwater applications. This technique is applied to the envelope of signals received by a 2-D array. The performances of the technique are compared with those of the coherent technique of focused beamforming in terms of angular resolution, depth of field, beam pattern, and accuracy in range determination. Moreover, special attention is given to the modalities for an efficient implementation on a digital computer. The complete absence of grating lobes, for whatever arrangement of the transducers, and the much higher speed of the imaging process, as compared with that of beamforming in the time domain, are the two major advantages of this technique. A reduction in the speckle effect, the possibility of creating a virtual depth of field, the feasibility of using square-law transducers, and the implementation simplicity are additional interesting characteristics. The results of some simulations are reported and compared with those obtained by the beamforming process.<<ETX>>
Mosaic of a video shot with multiple moving objects

In this paper we describe an application which takes a video shot as input and produces a compact representation composed by a background layer and segmented moving objects. We deal with the problems of global registration, super-resolution mosaicing, objects segmentation and tracking. Global registration is achieved with a graph-based technique that exploits situations when the camera returns to a previously seen area. Objects segmentation is based on motion analysis using a robust statistical model of the background. Tracking is based on blob matching using singular value decomposition.
A Bilinear Approach to the Position Self-Calibration of Multiple Sensors

This paper presents a novel algorithm for the automatic 3D localization of a set of sensors in an unknown environment. Given the measures of a set of time of arrival delays at each sensor, the approach simultaneously estimates the 3D position of the sensors and the sources that have generated the event. Such inference is obtained with no assumption about the sensor localization; the only assumption made is that the emission time of the sources must be known in order to evaluate the time of flight for each event. Moreover, we propose a further method that deals with the likely case of missing data in the measurements. This occurs when sensors are far apart or behind natural barriers that avoids the registration of the given event. Simulated experiments show the validity of the approach for different setups of sensors and number of events.
Adaptive Local Movement Modelling for Object Tracking

In this paper we present a novel strategy for modelling the motion of local patches for single object tracking that can be seamlessly applied to most part-based trackers in the literature. The proposed Adaptive Local Movement Modelling (ALMM) method is able to model the local spatial distribution of the image patches defining the object to track and the reliability of each image patch. Given the output of a base tracking algorithm, a Gaussian Mixture Model (GMM) is first used to model the distribution of the movement of local patches relative to the gravity center of the tracked object. Then, the GMM is combined with the base tracker in a boosting framework, which gives a novel integrated boosting classifier for the tracking task. This provides a robust procedure to detect outliers in the local motion of the patches. The algorithm is highly configurable with the possibility to change the number of local patches used for tracking and to adapt to the variations of the tracked object. Tracking results on standard datasets show that equipping state-of-the-art trackers with our tehcnique remarkably improves their performance.
Reading between the turns: Statistical modeling for identity recognition and verification in chats

Identity safekeeping has recently become an important problem for the social web: as a case study, we focus here on instant messaging platforms, proposing novel soft-biometric cues for user recognition and verification. Specifically, we design a set of features encoding effectively how a person converses: since chats are crossbreeds of written text and face-to-face verbal communication, the features inherit equally from textual authorship attribution and conversational analysis of speech. Importantly, our cues ignore completely the semantics of the chat, relying solely on non-verbal aspects, taking care of possible privacy and ethical issues. We apply our approach on a novel dataset of 94 different individuals, whose chat conversations have been recorded for an average period of five months; recognition rate, intended as normalized AUC on CMC curve, is 95.73%, while verification rate amounts to 95.66%, as normalized AUC on ROC curve.
Fast FPGA-based architecture for pedestrian detection based on covariance matrices

Pedestrian detection is a crucial task in several video surveillance and automotive scenarios, but only a few detection systems are designed to be realized on an embedded architecture, allowing to increase the processing speed which is one of the key requirements in real applications. In this paper, we propose a novel SoC (System on Chip) architecture for fast pedestrian detection in video. Our implementation is based on a linear SVM (Support Vector Machine) classification framework, learned on a set of overlapped image patches. Each patch is described by a covariance matrix of a set of image features. Exploiting the inner parallelism of the FPGA (Field Programmable Gate Array) boards, we dramatically accelerate the covariance matrices computation that plays a crucial role in the framework. In the experiments, we show the effectiveness and the efficiency of our pedestrian detection system, reaching a detection speed of 132 fps at VGA resolution.