nils-herrmann/clean_scirepeval huggingface dataset:
Multiview Soundfield Imaging in the Projective Ray Space

A soundfield image is a data structure that efficiently encodes and represents the wave field as captured by a microphone array. Its representation is based on the directional plenacoustic function, which is defined as the radiance of the acoustic paths (rays) that cross the segment that the array lies upon. The soundfield image can be processed “as is” to develop a variety of applications. In its original formulation, the soundfield image is based on a Euclidean parameterization that can accommodate a limited range of rays and is suitable for managing a single array only. In this paper, we generalize this methodology to the case of multiple microphone arrays deployed in space. The use of multiple arrays allows us to capture truly global information on the sound field but requires us to rethink the ray space, and adopt a global representation of the acoustic rays based on projective geometry. After introducing the new parameterization, we present two examples of applications: the estimation of the mutual poses of two or more arrays (self-calibration); and the localization of multiple acoustic sources. The effectiveness of these applications is proven through simulations as well as real data experiments.
Postural Assessment in Dentistry Based on Multiple Markers Tracking

Postural assessment is a fundamental aspect to prevent long-term Musculoskeletal disorders (MSDs) due to fatiguing jobs. Operative dentistry also belongs to this category and we developed a Computer Vision approach to automatically analyze the dentist posture during operations obtaining an evaluation of MSD risk according to some well-established criteria like RULA and NERPA. In particular we analyze three different set-ups where the dentist operates with naked eyes, medical loupes or using a surgical microscope and we compared the postural effects of these three different configurations. The results present a significant improvement in posture using the microscope and validated our approach as a feasible and effective method to assess posture in fatiguing jobs. The proposed approach allows a continuous monitoring of job activity evaluating accurately posture criticalities. Furthermore the risk of MSD based on international criteria is evaluated in an objective and accurate way. The whole proposed system follows a non-invasive approach based on Augmented Reality markers tracked from a distant camera and can be applied to effective monitoring different working activities providing an accurate and objective estimation of MSD according to modern posture assessment criteria.
Localization of sparse image tampering via random projections

Hashes can be used to provide authentication of multimedia contents. In the case of images, a hash can be used to detect whether the data has been modified in an illegitimate way. When the authentication check fails, it might be useful to localize the tampering in the spatial domain. This paper proposes an algorithm based on compressive sensing principles, which solves both the authentication and the localization problems. The encoder produces a hash using a small bit budget by quantizing a limited number of random projections of the authentic image. The decoder uses the hash to estimate the distortion between the original and the received image. In addition, if the attack is sparse, it can be also localized. In order to keep the size of the hash small, encoding/decoding takes advantage of distributed source codes. This paper also investigates experimentally the tradeoff between the rate allocated to the hash and the performance achieved in terms of tampering localization.
Deconvolution of plenacoustic images

In this paper we propose a methodology aimed at improving the resolution capabilities of plenacoustic imaging, which is based on deconvolution techniques mutuated from aerospace acoustic imaging. In order to reduce the computational burden, we also propose a modification of the minimization problem that exploits the highly structured information contained in the plenacoustic image. Experiments and simulations show the improvement of the accuracy gained by applying the deconvolution operator.
The Ray Space Transform: A New Framework for Wave Field Processing

Soundfield imaging is a special analysis methodology aimed at capturing the directional components of the acoustic field and mapping them onto a domain called “ray space”, where relevant acoustic objects become linear patterns, i.e., sets of collinear points. This allows us to overcome resolution issues while easing far-field assumptions. In this paper, we generalize this concept by introducing the ray space transform for acoustic field representation. The transform is based on a short space-time Fourier transform of the signals captured by a microphone array, using discrete Gabor frames. The resulting transform coefficients are parameterized in the same ray space used for soundfield imaging. The resulting transform enables the definition of analysis and synthesis operators, which exhibit perfect reconstruction capabilities. We show examples of applications of the ray space transform to source localization and spot spatial filtering.
A Model Based Energy Minimization Method for 3D Face Reconstruction

In the paper we present a model based method for generating 3D face models from multiple images by means of an energy minimization algorithm. The energy function takes into account of: i) how well the luminance profiles are transferred (through the 3D model) from one image to the others; ii) how smooth are the reconstructed surfaces; iii) how it is congruent with an adapted face template mode (Candide model). It is important to notice that with the proposed method for each considered face two different 3D models are reconstructed: one with high resolution and one with low resolution obtained a reshaped Candide model. The first model can be used, for example, in 3D face analysis/recognition systems, while the second can be more useful for searching a specific face model in a large database
Countering JPEG anti-forensics

JPEG coding leaves characteristic footprints that can be leveraged to reveal doctored images, e.g. providing the evidence for local tampering, copy-move forgery, etc. Recently, it has been shown that a knowledgeable attacker might attempt to remove such footprints by adding a suitable anti-forensic dithering signal to the image in the DCT domain. Such noise-like signal restores the distribution of the DCT coefficients of the original picture, at the cost of affecting image quality. In this paper we show that it is possible to detect this kind of attack by measuring the noisiness of images obtained by re-compressing the forged image at different quality factors. When tested on a large set of images, our method was able to correctly detect forged images in 97% of the cases. In addition, the original quality factor could be accurately estimated.
Accurate feature detection and matching for the tracking of calibration parameters in multi-camera acquisition systems

The 3D reconstruction's quality of multiple-camera acquisition systems is strongly influenced by the accuracy of the camera calibration procedure. The acquisition of long sequences is, in fact, very sensitive to mechanical shocks, vibrations and thermal changes on cameras and supports, as they could result in a significant drift of the camera parameters. In this paper we propose a technique which is able to keep track of the camera parameters and, whenever possible, to correct them accordingly. This technique does not need any a-priori knowledge or test objects to be placed in the scene, but exploits features that are already present in the scene itself. In fact it performs an accurate detection, matching and back-projection of luminance corners and spots in the scene space. Experimental results on real sequences are reported in order to prove the ability of the proposed technique to detect a change in the calibration and to re-calibrate the camera setup with an accuracy that depends on the number of available feature points.
Visible surface reconstruction with accurate localization of object boundaries

A common limitation of many techniques for 3-D reconstruction from multiple perspective views is the poor quality of the results near the object boundaries. The interpolation process applied to "unstructured" 3-D data ("clouds" of non-connected 3-D points) plays a crucial role in the global quality of the 3-D reconstruction. We present a method for interpolating unstructured 3-D data, which is able to perform a segmentation of such data into different data sets that correspond to different objects. The algorithm is also able to perform an accurate localization of the boundaries of the objects. The method is based on an iterative optimization algorithm. As a first step, a set of surfaces and boundary curves are generated for the various objects. Then, the edges of the original images are used for refining such boundaries as best as possible. Experimental results with real data are presented for proving the effectiveness of the proposed algorithm.
Robust real-time intrusion detection with fuzzy classification

We propose a novel system for indoor video surveillance. It is able to detect and track moving objects even in the presence of significant variations of scene illumination. After a preliminary analysis and clustering of temporal changes in the video sequence, the algorithm performs a classification based on fuzzy logic, aimed at identifying moving regions that really correspond to unexpected objects in the scene. The proposed approach tends to discard shadows, reflections and luminance profile changes due to illumination variations. One key feature of our system is its modest computation complexity, which allows it to operate in real-time on a common PC platform. The system has been tested on a wide variety of situations, proving its effectiveness and robustness.
Reduced memory region based deep Convolutional Neural Network detection

Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on cars brakes, it helps decreasing the probability of injuries and human fatalities. In order to achieve very high accuracy, recent pedestrian detectors have been based on Convolutional Neural Networks (CNN). Unfortunately, such approaches require vast amounts of computational power and memory, preventing efficient implementations on embedded systems. This work proposes a CNN-based detector, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline, we develop an architecture that outperforms methods based on traditional image features and achieves an accuracy close to the state-of-the-art while having low computational complexity. Furthermore, the model is compressed in order to fit the tight constrains of low power devices with a limited amount of embedded memory available. This paper makes two main contributions: (1) it proves that a region based deep neural network can be finely tuned to achieve adequate accuracy for pedestrian detection (2) it achieves a very low memory usage without reducing detection accuracy on the Caltech Pedestrian dataset.
A Robust Method for the Estimation of Reliable Wide Baseline Correspondences

In this paper we present a complete method to retrieve reliable correspondences among wide baseline images, that is images of the same scene/object acquired from very different viewpoints. We propose a solution based on matching of affine co-variant features, composed by the following four steps: interest region detection, normalization, description and matching. In our method we implemented improved versions of some techniques recently introduced in the literature: the MSER detector (maximally stable extremal regions) and SIFT and RIFT descriptors (scale/rotation invariant feature transform). After a general introduction to the wide baseline problems and a summary of the recent state-of-the-art solutions, we illustrate the proposed method detailing the added improvements, then we present some experimental results obtained on wide baseline images.
Soundfield rendering with loudspeaker arrays through multiple beam shaping

This paper proposes a method for the acoustic rendering of a virtual environment based on a geometric decomposition of the wave-field into multiple elementary acoustic beams, all reconstructed with a loudspeaker array. The point of origin, the orientation and the aperture of each beam is computed according to the geometry of the virtual environment that we want to render and to the location of the sources. Space-time filters are computed with a Least Squares approach to render the desired beam. Experimental results show the feasibility as well as the critical issues of the proposed algorithm.
Combined motion and edge analysis for a layer-based representation of image sequences

We propose a method for the motion-congruent segmentation of image sequences based on both motion field and luminance information. In order to do so, the affine motion models are determined by analyzing the motion field through a clustering procedure, while their regions of validity are determined by an MRF-based region estimator. This last block performs a pixel-wise re-assignment of a limited number of affine models (those determined via clustering).
Minimum variance multiplexing of multimedia objects

This paper addresses the problem of simultaneous transmission of multiple multimedia objects (such as images or video sequences) over a bandwidth-limited channel. The trivial strategy of partitioning in equal parts the available rate among the bitstreams is suboptimal, when the multimedia objects have different coding complexities. Exploiting object diversity allows us to allocate the bandwidth according to some optimality criteria, e.g. minimizing the average total distortion or minimizing the variance between the distortions of each object. By describing the rate-distortion characteristics of each multimedia object in terms of a simple exponential model, we provide a closed form solution for both the minimum average and the minimum variance problems. In addition, if we consider the statistical distribution of the rate-distortion model parameters, we can show that the minimum variance solution can effectively reduce the quality fluctuations among the objects, with an overall coding efficiency loss, w.r.t. the minimum average solution, of only 0.5dB on average. Some experiments, carried out on different H.264/AVC video sequences, validate our theoretical results.
Minimum Variance Optimal Rate Allocation for Multiplexed H.264/AVC Bitstreams

Consider the problem of transmitting multiple video streams to fulfill a constant bandwidth constraint. The available bit budget needs to be distributed across the sequences in order to meet some optimality criteria. For example, one might want to minimize the average distortion or, alternatively, minimize the distortion variance, in order to keep almost constant quality among the encoded sequences. By working in the rho-domain, we propose a low-delay rate allocation scheme that, at each time instant, provides a closed form solution for either the aforementioned problems. We show that minimizing the distortion variance instead of the average distortion leads, for each of the multiplexed sequences, to a coding penalty less than 0.5 dB, in terms of average PSNR. In addition, our analysis provides an explicit relationship between model parameters and this loss. In order to smooth the distortion also along time, we accommodate a shared encoder buffer to compensate for rate fluctuations. Although the proposed scheme is general, and it can be adopted for any video and image coding standard, we provide experimental evidence by transcoding bitstreams encoded using the state-of-the-art H.264/AVC standard. The results of our simulations reveal that is it possible to achieve distortion smoothing both in time and across the sequences, without sacrificing coding efficiency.
Joint Compressive Video Coding and Analysis

Traditionally, video acquisition, coding and analysis have been designed and optimized as independent tasks. This has a negative impact in terms of consumed resources, as most of the raw information captured by conventional acquisition devices is discarded in the coding phase, while the analysis step only requires a few descriptors of salient video characteristics. Recent compressive sensing literature has partially broken this paradigm by proposing to integrate sensing and coding in a unified architecture composed by a light encoder and a more complex decoder, which exploits sparsity of the underlying signal for efficient recovery. However, a clear understanding of how to embed video analysis in this scheme is still missing. In this paper, we propose a joint compressive video coding and analysis scheme and, as a specific application example, we consider the problem of object tracking in video sequences. We show that, weaving together compressive sensing and the information computed by the analysis module, the bit-rate required to perform reconstruction and tracking of the foreground objects can be considerably reduced, with respect to a conventional disjoint approach that postpones the analysis after the video signal is recovered in the pixel domain. These findings suggest that considerable gains in performance can be potentially obtained in video analysis applications, provided that a joint analysis-aware design of acquisition, coding and signal recovery is carried out.
Exploiting Spatial Redundancy in Pixel Domain Wyner-Ziv Video Coding

Distributed video coding is a recent paradigm that enables a flexible distribution of the computational complexity between the encoder and the decoder building on top of distributed source coding principles. In this paper we focus on the scenario where most of the complexity is shifted to the decoder, thus achieving light encoding. We elaborate on a well known pixel based Wyner-Ziv architecture and we improve its coding efficiency by exploiting both spatial and temporal correlation at the decoder side, without the need of performing any transform at the encoder. In order to generate the side information, the decoder adaptively chooses spatial or temporal information, based on the local estimate of the correlation noise. Simulations on test sequences demonstrate that a coding gain of up to +1.8 dB can be obtained with respect to the case that generates the side information by motion interpolation only.
Acoustic Source Localization With Distributed Asynchronous Microphone Networks

We propose a method for localizing an acoustic source with distributed microphone networks. Time Differences of Arrival (TDOAs) of signals pertaining the same sensor are estimated through Generalized Cross-Correlation. After a TDOA filtering stage that discards measurements that are potentially unreliable, source localization is performed by minimizing a fourth-order polynomial that combines hyperbolic constraints from multiple sensors. The algorithm turns to exhibit a significantly lower computational cost compared with state-of-the-art techniques, while retaining an excellent localization accuracy in fairly reverberant conditions.
Colored visual tags: a robust approach for augmented reality

This paper presents a robust method for fast visual tags reading, suitable for augmented reality (AR) environments. Tag detection is based on well known tools of image-processing, but their combination, together with the use of colored markers, allows a robust recognition even with low-cost CMOS or CCD cameras and in poorly illuminated environments. In particular the color mix and the structure of the tag are quite unusual in common environments and can be easily detected with color filtering and geometric analysis. The proposed tag carries binary information encoded in its structure: in the presented implementation a 32-bit code with 12 parity bits is encoded in the tag but extensions to longer codes can be easily devised.
A moving object identification algorithm for image sequence interpolation

An effective motion compensated image interpolation technique is presented. The proposed algorithm has been developed for the interpolation of missing frames in an image sequence and it is based on two principal elements. The first is a region-based motion estimation and representation technique, which combines, for each known image, an approximate initial motion field estimate and an initial image over-segmentation (obtained using both luminance and chrominance information) to produce a very accurate affine-model regularized motion field. The proposed algorithm uses a robust identification technique for the estimation of the motion parameters associated to each region. The second important element is the use of an interpolation technique, that starting from the available motion information defines, for each point of the image to be interpolated a suitable reconstruction strategy taking into account the different situations that can appear (for example a moving object can occlude either a stationary background or an another object and so on). The principal aim of the proposed algorithm is the reconstruction of the missing frames in a reasonable way, without introducing significant artifacts and assuring a pleasant representation of object displacement.
Multiple compression detection for video sequences

Nowadays, thanks to the increasingly availability of powerful processors and user friendly applications, the editing of video sequences is becoming more and more frequent. Moreover, after each editing step, any video object is almost always encoded in order to store it using a less amount of memory. For this reason, inferring the number of compression steps that have been applied to such a multimedia object is an important clue in order to assess its authenticity. In this paper we propose a method to recover the number of compression steps applied to a video sequence. In order to accomplish this goal, we make use of a classifier based on multiple Support Vector Machines (SVM) exploiting the Benford's law. Indeed, the feature vectors used to train and test the SVM are based on the statistics of the most significant digit of quantized transform coefficients. The proposed method is tested with a generic hybrid video encoder combining motion-compensation and block coding. Results show that this method is able to discriminate up to three compression stages with high accuracy.
A robust video watermarking technique for compression and transcoding processing

We present a video watermarking technique proposed as an indexing technique for video sequences. The watermarking approach is based on an algorithm working in the spatial video domain, in order to reduce the computational cost, while the invisibility is obtained by masks computed in space and time domains. Finally, to improve robustness we work with different error correcting codes and we test their performances. The proposed scheme has been implemented and tested. Our tests are mainly focused on compression and transcoding attacks that are the most important operations that the video suffers in a journalistic report production. The robustness is increased by using an error correction code. The obtained results show that this is a good approach.