nils-herrmann/clean_scirepeval huggingface dataset:
Decoding Behavior Study of LDPC Codes Under a Realistic Magnetic Recording Channel Model

Low-density parity check (LDPC) codes have shown near-capacity performance in additive white Gaussian noise (AWGN) channels. However, in magnetic recording systems, the readback signals suffer from various impairments (namely, transition noise, nonlinear transition shift, and partial erasure) in addition to AWGN. In this paper, we describe an FPGA-based advanced magnetic recording channel simulator (that includes these impairments) and an LDPC coding system. The error correcting performances of LDPC codes in the presence of such realistic impairments are investigated down to bit error rate (BER) of 10-11 and frame error rate (FER) of 10-8
Efficient Calculation of Primary Images from a Set of Images

A set of images is modeled as a stochastic process and Karhunen-Loeve expansion is applied to extract the feature images. Although the size of the correlation matrix for such a stochastic process is very large, we show the way to calculate the eigenvectors when the rank of the correlation matrix is not large. We also propose an iterative algorithm to calculate the eigenvectors which save computation time andc omputer storage requirements. This iterative algorithm gains its efficiency from the fact that only a significant set of eigenvectors are retained at any stage of iteration. Simulation results are also presented to verify these methods.
Low-density parity check codes for partial response channels

The goals of this article are twofold: (1) to provide a brief tutorial of the application of low-density parity check (LDPC) codes for partial response (PR) channels under the framework of turbo equalization and (2) to highlight the use of structured LDPC codes in PR systems. We begin by introducing LDPC codes, their graph representations and associated sum-product decoding algorithm, followed by describing the general framework of iterative equalization and decoding approach to combat ISI. We then present explicit constructions of structured LDPC codes, which facilitate efficient implementation of encoding and decoding and show simulation results.
Field programmable gate array (FPGA) for iterative code evaluation

Iterative codes such as low density parity check (LDPC) codes and turbo product codes (TPC) are of interest for data storage applications. To apply these codes to digital recording systems, two possible schemes are considered in this paper. In the first scheme, a single LDPC code of column weight j>2 is used to replace the Reed-Solomon (RS) code of the conventional channel. In the second scheme, an iterative code is used as the inner code and is concatenated with an outer RS code. Single parity check TPC code is considered for this scheme. We developed a high-throughput field programmable gate array (FPGA) platform to evaluate the error floor performance of LDPC codes and the error statistics of TPC codes in partial response (PR) channel with turbo equalization. High rate codes (rate 8/9 for LDPC code and rate 0.935 for TPC code) are evaluated for magnetic recoding application. The bit error rate (BER) performance of LDPC code down to 10/sup -10/ can be reached within 2 h using the FPGA platform. The TPC code error statistics are evaluated using about 10/sup 11/ bits at different signal-to-noise (SNR) levels. For practical implementation complexity and power consumption, 2 channel iterations and 2 TPC decoder iterations are employed. The results show that the gain by applying TPC code under 2 channel iterations and 2 TPC decoder iterations is marginal.
Error Event Analysis of Partial Response Targets for Perpendicular Magnetic Recording

An analytical method to identify lists of the most probable error events for arbitrary partial response (PR) targets is described. The dominant error events are extracted via a computer search considering not only the minimum distance but also additive white noise amplification and data-dependent transition jitter noise. The method uses an efficient scheme to take into account the effect of data-dependent transition jitter noise. Combining the error event analysis and a multinomial model, approximate sector error rates can be calculated without the need for time-consuming simulations. The results from the analytical method are compared with simulation results.
A modified hyperplane method for null synthesis in an array pattern

It has recently been shown by Prasad that the problem of synthesizing nulls in an array pattern admits a geometrical formulation for which the alternative orthogonal projection (AOP) algorithm (previously used in image restoration applications) is a useful solution. We apply a modification to the original AOP suggested by Ramakrishnam et al. for image restoration purposes and show that it results in a computational savings in array pattern synthesis problems. A numerical example is presented to illustrate the computational advantages of this modification.
Multiple-Shot Person Re-identification by HPE Signature

In this paper, we propose a novel appearance-based method for person re-identification, that condenses a set of frames of the same individual into a highly informative signature, called Histogram Plus Epitome, HPE. It incorporates complementary global and local statistical descriptions of the human appearance, focusing on the overall chromatic content, via histograms representation, and on the presence of recurrent local patches, via epitome estimation. The matching of HPEs provides optimal performances against low resolution, occlusions, pose and illumination variations, defining novel state-of-the-art results on all the datasets considered.
Associative and symbolic algorithms for viewpoint-independent object recognition

The authors address a specific problem related to image processing and understanding, i.e., recognition of three-dimensional (3-D) objects from a set of 2-D views. The chosen application is recognition of road scenes for semiautonomous vehicle driving. A recognition scheme is proposed that uses an associative mechanism as a guide sensor to estimate scene probabilities and probabilistic expectations of objects and their views in order to support a symbolic search process. Associative matching results are used to assess object probabilities of being present in the scene; such probabilities are utilized to define a top-down searching order to be followed by the symbolic recognition process. Some new results are reported on generation of specific keys for coding 2-D object views in associative memories.<<ETX>>
Depth estimation by an adaptive depth from motion algorithm

The utility of autonomous underwater vehicles (AUVs) is increasing due to their capability to inspect the environment where they operate, especially in risk (e.g., deep) areas. Vision sensors, on board an AUV, can provide a large amount of information and are often used to extract important visual cues in a scene. The authors discuss the problem of depth extraction by using a depth from motion (DFM) procedure robust to the inherent ill structure (e.g., bad illumination) of the environment to be investigated. This algorithm is capable of extracting 3D information from a sequence of visual images, and can be useful to build up the 3D relief of a scene. The robustness is due to the implementation of an auxiliary regulation procedure aimed at improving depth reliability when the data quality is insufficient. Comparative results are shown, which demonstrate the goodness of the regulation approach.<<ETX>>
Investigating hidden Markov models' capabilities in 2D shape classification

In this paper, Hidden Markov Models (HMMs) are investigated for the purpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.
Distributed propagation of a-priori constraints in a Bayesian network of Markov random fields

In this paper, Bayesian networks of Markov random fields (BN-MRFs) are proposed as a technique for representing and applying apriori knowledge at different abstraction levels inside a distributed image processing framework. It is shown that this approach, thanks to the common probabilistic basis of the two techniques, is able to combine in a natural way causal inference properties at different abstraction levels as provided by Bayesian networks with optimisation criteria usually applied to find the best configuration for an MRF. Examples of two-level BN-MRFs are given, where each node uses a coupled Markov random field which has to solve a coupled restoration and segmentation problem. Experiments are concerned with expert-driven registered segmentation and tracking of regions from image sequences.
Joint laplacian diagonalization for multi-modal brain community detection

In this paper we present a novel approach to group-wise multi-modal community detection, i.e. identification of coherent sub-graphs across multiple subjects with strong correlation across modalities. This approach is based on joint diagonalization of two or more graph Laplacians aiming at finding a common eigenspace across individuals, over which spectral clustering in fewer dimension is then applied. The method allows to identify common sub-networks across different graphs. We applied our method on 40 multi-modal structural and functional healthy subjects, finding well known sub-networks described in literature. Our experiments revealed that detected multi-modal brain sub-networks improve the consistency of group-wise unimodal community detection.
Robust 3D segmentation for underwater acoustic images

A 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 both synthetic and 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.
Dissimilarity-Based Detection of Schizophrenia

We propose to approach the detection of patients affected by schizophrenia by means of dissimilarity-based classification techniques applied to brain magnetic resonance images. Instead of working with features directly, pairwise distances between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments were carried out on a set of 64 patients and60 controls and several pairwise dissimilarity measurements have been analyzed. We demonstrate that good results are possible and especially significant improvements can be obtained when combining over different ROIs and different distance measures. The lowest error rate obtained is 0.210.
Buried object detection by auto-regressive pre-whitening

An advanced signal processing technique devoted to the detection of buried objects by exploiting an active sonar system is proposed. The technique is based on the modeling of the reverberation phenomenon as an auto-regressive process. The detector consists of an adaptive pre-whitening filter and a bank of matched filters. The auto-regressive parameters are computed by a higher order statistics algorithm that works on short successive reverberation segments. No echoes of the buried target have been used to arrange the matched filters, but only echoes of the target in free water. The proposed technique has been tested with an experimental data set related to a steel cylinder deeply buried in the sea bottom, obtaining impressive results in spite of the very low signal to reverberation ratio. This work was performed owing to the European Commission support, in the context of the contract DEO (Detection of Embedded Objects).
Edge/region-based segmentation and reconstruction of underwater acoustic images by Markov random fields

This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired data. Beamforming, a method widely applied in acoustic imaging, is used to build a three-dimensional image, associated point by point with another kind of information representing the reliability (i.e. "confidence") of such an image. Unfortunately, this kind of images is plagued by several problems due to the nature of the signal and to the related sensing system, thus heavily affecting data quality. Specifically, speckle noise and the broad directivity characteristic of the sensor lead to very degraded images. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields whose associated probability distributions are specified by a single energy functional. A three-fold process has been applied able to reconstruct, segment, and restore the involved acoustic images exploiting both types of data. Our approach showed better performances with respect to other MRF-based methods as well as classical methods disregarding reliability information. Optimal (in the Maximum A-Posteriori probability sense) estimates of the 3D and confidence images are obtained by minimizing the energy functional by using simulated annealing.
A real-time vision system for crowding monitoring

In this paper, the approach used to estimate the number of people for planning purposes in DIMUS (ESPRIT project P-5345) is described. Crowd estimation is based on the image-processing and inference phases applied to the acquired data. Images come from a set of visual b/w camera oriented towards a zone to be monitored. Some significant features extracted from each acquired image are related to the number of people present in the monitored scene using the nonlinear models obtained by means of dynamic programming in an off-line training phase. The present approach, employing previously obtained estimates, improves the accuracy of estimation, with respect to an evaluation based only on present available data, and can predict crowding values without using new data, between two successive acquisitions. Results obtained after an extended test phase in a station of Genova's underground are reported.<<ETX>>
Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications.
A voting-based approach for fast object recognition in underwater acoustic images

This paper describes a voting-based approach for the fast automatic recognition of man-made objects and related attitude estimation in underwater acoustic images generated by forward-looking sonars or acoustic cameras. In general, the continuous analysis of sequences of images is a very heavy task for human operators and this is due to the poor quality of acoustic images. Hence, algorithms able to recognize an object on the basis of a priori knowledge of the model and to estimate its attitude with reference to a global coordinate system are very useful to facilitate underwater operations like object manipulation or vehicle navigation. The proposed method is capable of recognizing objects and estimating their two-dimensional attitude by using information coming from boundary segments and their angular relations. It is based on a simple voting approach 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 very 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 average estimation error and computational load.
Piecewise single view Photometric Stereo with multi-view constraints

This paper presents a novel Photometric Stereo approach for static views that recasts the problem into a piecewise formulation. The proposed algorithm, called Piecewise Photometric Stereo (PPS), entails several advantages in respect to previous global approaches. It is intrinsically more efficient, since reconstructing the surface in patches is computationally faster than reconstructing the global surface. Each patch has been associated an individual photometric model rather than a single global model as used in classical approaches. In this way, the piecewise formulation may grasp more complex lighting effects. Finally, the global metric properties of the shape is preserved using the multi-view constraints. In this pipeline, structure from motion is exploited to define such set of constraints and to compose a 3D mesh representing the metric structure of the object. Real results with ground truth show the positive performance of our algorithm compared with a classical global approach for Photometric Stereo.
Hybrid approach for accurate echo detection in the formation of acoustic images

This paper proposes a hybrid approach aimed at precise echo detection for improving short-range underwater images generated by beam-forming process. The final goal is the construction of the scene model as accurately as possible to be used by an autonomous vehicle to navigate in the environment. Our approach is essentially a voting method. It consists in the integration of two methods based on the estimation of the angle of arrival for each time instant and the estimation of the time of arrival for each fixed direction. Each sampled time instant of each beam signal contributes with an opportune quantity on a voting space. At the end of the process, maxima recovery in the voting space and post-processing methods are applied to recover the precise silhouette of the scene. Angular-scan sonar data were simulated in order to verify the validity of the idea. Results are reported in order to compare our method with commonly used echo estimation methods, showing advantages and drawbacks of our approach.
2D shape recognition by hidden Markov models

In computer vision, two-dimensional shape classification is a complex and well-studied topic, often basic for three-dimensional object recognition. Object contours are a widely chosen feature for representing objects, useful in many respects for classification problems. We address the use of hidden Markov models (HMM) for shape analysis, based on chain code representation of object contours. HMM represent a widespread approach to the modeling of sequences, and are largely used for many applications, but unfortunately are poorly considered in the literature concerning shape analysis, and in any case, without reference to noise or occlusion sensitivity. The HMM approach to shape modeling is tested, probing good invariance of this method in terms of noise, occlusions, and object scaling.
A Re-evaluation of Pedestrian Detection on Riemannian Manifolds

Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al.  can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-the art results.
3D mosaicing for environment reconstruction

This paper proposes a technique for the 3D reconstruction of an underwater environment from multiple range views. The final target of the work lies in improving the understanding of a human operator guiding an underwater remotely operated vehicle (ROV) equipped with an acoustic camera, which provides a sequence of 3D images in real time. Since the field of view is narrow we devise a technique for the reconstruction of relevant information of the image sequence up to building a mosaic of the surrounding scene. Due to the very noisy nature of the data and the low range resolution, smoothing, segmentation, registration, and fusion problems have been tackled. Examples on real images are presented to show the promising performances of the algorithm.
Exploiting Gaussian mixture importance for person re-identification

Person re-identification (ReID) stands for the task of determining the co-occurrence of individuals across a network of cameras with disjoint viewfields. The relevant literature documents a plausible number of contributions so far. KISS metric learning is an effective ReID method. However, as reported in the existing works, KISS metric learning is sensitive to the feature dimensionality and can not capture the multi modes in the dataset. To this end, we propose in this paper a Gaussian Mixture Importance Estimation (GMIE) approach for ReID, which exploits the Gaussian Mixture Models (GMMs) to estimate the observed commonalities of similar and dissimilar person pairs in the feature space. Experiments on three benchmark datasets reveal that our method offers the property of maintaining its efficiency on high-dimensional features. Moreover, the proposed GMIE scores plausible ReID rates as compared to other works.
Vehicle recognition and tracking from road image sequences

In this paper, synergies between the recognition and tracking processes for autonomous vehicle driving are studied. Object recognition is periodically performed to focus attention on specific parts of the visual signal and to assign them symbolic meanings. Tracking is used to maintain correspondences between objects identified at successive recognition instants as well as to provide further features (e.g., spatio-temporal trajectories) on which to base object-pose estimation. Results obtained by using complex road scenes are reported, which demonstrate the validity of the approach in terms of robustness, accuracy, and time responses.