nils-herrmann/clean_scirepeval huggingface dataset:
ASIC design for robust signal and image processing

Maximum a posteriori (MAP) estimation using a robust signal model is capable of extracting a signal from additive noise, preserving edges in the signal estimate. A digital network can compute the estimate numerically using the gradient descent algorithm, or an analog network can be designed which provides the estimate as a steady-state output. An ASIC designed to perform robust signal processing has been manufactured, using the Huber-Markov random field (HMRF) signal model. This robust model is dependent upon two tunable parameters, which may be adjusted for a particular signal due to the design of novel analog circuit blocks.<<ETX>>
Coupled Hidden Markov Models for Robust EO/IR Target Tracking

Augmenting electro-optical (EO) based target tracking systems with infrared (IR) modality has been shown to be effective in increasing the accuracy rate of the tracking system. A key issue in designing such a multimodal tracking system is how to combine information observed from different sensor types in a systematic way to obtain desirable performance. In this paper, we present an investigation into integrating EO and IR sensors within hidden Markov model (HMM) based frameworks. We propose to use a coupled hidden Markov model (CHMM) to improve upon the existing fusion schemes. Another contribution is that we propose to use a robust t-distribution based subspace representation in the CHMM to model appearance changes of the target. Numerical experiments demonstrate that the proposed CHMM tracking system has improved performance over other integration schemes for situations where the target object is corrupted by noise or occlusion.
Reducing the computational complexity of a MAP post-processing algorithm for video sequences

Maximum a posteriori (MAP) filtering using the Huber-Markov (1981) random field (HMRF) image model has been shown in the past to be an effective method of reducing compression artifacts in images. Unfortunately, this MAP formulation requires iterative techniques for the solution of a constrained optimization problem. In the past, these iterative techniques have been computationally intensive, making the filter infeasible in situations where it is desired to filter images (or video frames) quickly. This paper introduces two methods for reducing the computational requirements of the constrained optimization, as well as theoretical and experimental justifications for using them.
Motion blur kernel estimation using noisy inertial data

In the case of motion blur due to unknown motion, most of the existing image deblurring algorithms rely on good initial estimate of the kernel or latent image obtained through blind deconvolution and only consider 3-dimensional camera motions. To overcome these problems, Joshi  presented a novel blur kernel estimation and image deblurring approach by integrating 6-dimensional inertial sensors with a camera. However, the drift in the estimated camera motion path introduced by inertial measurement noise was not well handled in Joshi's work. In this paper, we propose an alternating optimization scheme to move the drifted camera motion path to the correct position. The camera pose space in the projective motion blur model is replaced by a motion path set to compensate for path drift. Experiments are performed on synthetic and real images to show the effectiveness of our approach.
Simultaneous multi-frame MAP super-resolution video enhancement using spatio-temporal priors

A simultaneous multi-frame super-resolution video reconstruction procedure, utilizing spatio-temporal smoothness constraints and motion estimator confidence parameters is proposed. The ill-posed inverse problem of reconstructing super-resolved imagery from the low resolution, degraded observations is formulated as a statistical inference problem and a Bayesian, maximum a-posteriori (MAP) approach is utilized for its approximate solution. The inclusion of motion estimator confidence parameters and temporal constraints result in higher quality super-resolution reconstructions with improved robustness to motion estimation errors.
Multiple object tracking with kernel particle filter

A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.
H.264/SVC Multiple Description Coded Video Transmission over MIMO System with Power Control Based Antenna Selection

Improvements in transmitted video quality are achievable by utilizing multiple-input multiple-output (MIMO) systems for transmission of multiple description coded (MDC) video. For a particular MDC scheme, the improvements heavily depend on the selection of the underlying MIMO system. This paper proposes using a MIMO system with power control based antenna selection. Simulations show that the proposed system significantly outperforms the existing MDC/MIMO combinations in the literature and extends the range of channel signal-to-noise ratio (SNR) values for which video of given quality can be transmitted. Quality improvements result from MIMO power control which allows transmissions over available MIMO antennas only for sufficiently high channel gains (effectively preventing transmissions that result in video packet loss) and guarantees equal channel performance in terms of the bit error rate (BER) for all the balanced descriptions of equal importance.
Image Based Localization Based on Feature Scale Consistency in BOF Vector

Image search engines commonly employ the Bag Of Features (BOF) method to represent each database image with a feature vector and retrieve the best candidate using a measure of similarity to a query image vector. The BOF vector, which specifies the occurrence frequency of features, is used with Soft Assignment (SA) to find the most similar candidates which are further analyzed using geometric information to determine the final location. In this paper, we propose a new method where partial geometric information captured in the scales of keypoints associated to feature descriptors is directly used in the feature vector entries, unlike the conventional BOF method which uses the frequency of features. The proposed method, referred to as Bag Of Scale-Indexed Features (BOSIF), is implemented with an algorithm devised to avoid the increased use of memory. A procedure for evaluating scale consistency between query and dataset images is also proposed. Experimental results demonstrate that BOSIF outperforms SA-based feature-indexed BOF method and has performance comparable to state-of-the-art approaches in terms of Recall and especially for the first retrieved images by removing false matches caused by quantization error.
Power-efficient RMA SAR imaging using pipelined Non-uniform Fast Fourier Transform

Range Migration Algorithm (RMA) is one of the most practiced frequency domain based algorithms for Synthetic Aperture Radar (SAR) image reconstruction. While computationally more intensive than Polar Format Algorithm (PFA), it has superior image reconstruction for large squint angles. Due to the real-time nature of SAR imaging-related tasks, it is desired to have faster hardware implementations of reconstruction algorithms. In this paper we study FPGA-based implementation of the RMA algorithm using a novel pipelined Non-uniform FFT (NuFFT) module that optimizes its operation by exploiting the order in which samples are presented. We demonstrate that we can achieve processing speed of over 600 Msps (Mega Samples per Second) for the implemented RMA algorithm using the FPGA.
Estimation of oxygen tension in retinal capillaries from phosphorescence lifetime images

Investigating the effect of retinal oxygenation abnormalities in the development of common eye diseases requires accurate assessment of oxygen tension in retinal vasculatures. Estimation of oxygen tension in retinal capillaries using phosphorescence lifetime imaging is addressed in this paper. Separation from tissue and oxygen tension estimation is a more challenging task for capillaries when compared with large retinal vessels due to the finer structure and noise predominance in capillaries. An automated segmentation procedure is applied using the EM algorithm and noise contamination is reduced using a regularization method unlike previous approaches to capillary analysis where segmentation was done manually and noise contamination was ignored. We demonstrate the effectiveness of the proposed method by applying it to the retinal image data acquired from rat eyes, and we show that the oxygen tension estimate of retinal capillaries falls in the physiologically expected range.
On Achievable Distortion Exponents for a Gaussian Source Transmitted Over Parallel Gaussian Channels With Correlated Fading and Asymmetric SNRs

This paper considers the end-to-end mean squared error distortion in reconstructing a memoryless proper-complex Gaussian source transmitted over a set of parallel block-fading Gaussian noise channels, where the fading gains are modeled as correlated Rayleigh distributed random variables with different average powers, thus resulting in asymmetric average received signal noise ratios (SNRs). The distortion exponent (i.e., how fast the average distortion decays to zero as the average received SNR increases) of several coding strategies based on separate source and channel coding is characterized. The definition of distortion exponent commonly used in the literature for SNR-symmetric channels is generalized to the case of SNR-asymmetric channels. It is shown that fading correlation degrades the achievable mean squared error distortion, but does not affect the distortion exponent in the analyzed achievable schemes. The logarithm of the determinant of the fading correlation matrix is found to be a proxy for measuring the performance degradation due to correlation as compared with the case of independent fading. The proposed framework allows one to study any number of correlated parallel channels, contrary to the most of the literature that restricts attention to two channels only, with the same received SNR and with independent fading. In particular, a scheme based on the multiple description coding with more than two descriptions is analyzed; it is shown that determining the distortion exponent in this setting reduces to solving a linear program, which can be done numerically very efficiently. The proposed methodology relies on combining the ideas from linear innovation sequences and properties of determinant of sub-matrices. Interestingly, it is found that SNR-asymmetry is beneficial for multiple description coding when the total average received SNR in decibel is held constant. Even in the SNR-symmetric case, asymmetry in the compression rates is shown to lead to a larger distortion exponent than symmetric rates.
Density propagation for tracking initialization with multiple cues [human motion visual tracking]

The paper presents an automatic initialization procedure for visual tracking of human motion. Instead of relying merely on low-level image features to give a single estimate of the initial human posture, the system seeks to find a set of samples that carries multiple hypotheses of the pose. By accumulating different image cues in the first 3-15 consecutive frames and combining dynamic information regarding human motion, the system builds a human body model for the person to be tracked from a video sequence and produces a sample set as an estimate of the posterior distribution of the initial posture. The sample set provides a good starting point for tracking with sequential Monte Carlo methods.
InvArch: A hardware eficient architecture for Matrix Inversion

This paper proposes an efficient architecture (InvArch) for computing matrix inversion using Gauss-Jordan Elimination method. The proposed architecture exploits parallelism through pipelined floating-point computational units and reduces the number of floating-point multiplication units required compared with the existing pipelined implementations. The reduction in multiplication units results in over 80% reduction in hardware for floating point computation units. The architecture performs in-place inversion and provides scalability across the rows and columns. Hardware efficiency is achieved by reaping benefit from regularity in computation and better utilization of pipelined computational resources. Multiple rows are normalized within an iteration of Gauss-Jordan algorithm that allows reduction in number of floating-point multiplication units in the elimination step. In addition to implementing the architecture, an analytical performance model is also developed for InvArch and some related works. InvArch achieves performance comparable to reference architectures in terms of clock cycles and throughput while using significantly less hardware resources.
Accurate localization in dense urban area using Google street view images

Accurate information about the location and orientation of a camera in mobile devices is central to the utilization of location-based services (LBS). Most of such mobile devices rely on GPS data but this data is subject to inaccuracy due to imperfections in the quality of the signal provided by satellites. This shortcoming has spurred the research into improving the accuracy of localization. Since mobile devices have a camera, a major thrust of this research has been directed at acquiring the local scene and applying image retrieval techniques by querying a GPS-tagged image database to find the best match for the acquired scene. The techniques are however computationally demanding. To overcome the high complexity of those techniques, we investigated the use of inertial sensors as an aid in image-retrieval-based approach. Armed with information of media other than images, such as data from the GPS module along with orientation sensors such as accelerometer and gyro, we sought to limit the number of candidate images that should be considered for finding the best match. Specifically, data from the orientation sensors (heading) along with Dilution of Precision (DOP) from GPS are used to find the angle of view and the estimate of location. We present analysis of the reduction in the image set size for the search as well as simulations to demonstrate the effectiveness in a fast implementation with acceptable location error.
Improved image authentication using closed-form compensation and spread-spectrum watermarking

This paper presents an image authentication scheme based on compensated watermarking employing a Lagrangian-based closed-form solution to compensate for signature perturbation due to the embedding operation. The proposed scheme uses a spread-spectrum based watermarking technique and a blind detector, thus making it attractive for applications that may not have the original image available at the time of authentication. Existing compensated signature embedding frameworks use an iterative mechanism to reach a desired compensation. The iterative approach is time consuming and less effective than the closed-form approach proposed in this paper, which performs an accurate compensation in one step while meeting the minimum distortion criteria of image least mean square distortion to guarantee image fidelity. Simulation results are presented to show the proposed scheme's efficiency and accuracy.
Robust audio watermarking using frequency-selective spread spectrum

A novel audio watermarking scheme based on frequency-selective spread spectrum (FSSS) technique is presented. Unlike most of the existing spread spectrum (SS) watermarking schemes that use the entire audible frequency range for watermark embedding, the proposed scheme randomly selects subband(s) signal(s) of the host audio signal for watermark embedding. The proposed FSSS scheme provides a natural mechanism to exploit the band-dependent frequency-masking characteristics of the human auditory system to ensure the fidelity of the host audio signal and the robustness of the embedded information. Key attributes of the proposed scheme include reduced host interference in watermark detection, better fidelity, secure embedding and improved multiple watermark embedding capability. To detect the embedded watermark, two blind watermark detection methods are examined, one based on normalised correlation and the other based on estimation correlation. Extensive simulation results are presented to analyse the performance of the proposed scheme for various signal manipulations and standard benchmark attacks. A comparison with the existing full-band SS-based schemes is also provided to show the improved performance of the proposed scheme.
Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition

Computer-aided detection and enhancement of microcalcifications in mammogram images are considered. The mammogram image is first decomposed into subimages using a 'subband' decomposition filter bank which uses nonlinear filters. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. All regions with high positive skewness and kurtosis are marked as a regions of interest. Next, an outlier labeling method is used to find the locations of microcalcifications in these regions. An enhanced mammogram image is also obtained by emphasizing the microcalcification locations. Linear and nonlinear subband decomposition structures are compared in terms of their effectiveness in finding microcalcificated regions and their computational complexity. Simulation studies based on real mammogram images are presented.
Memory Optimized Re-gridding for Non-uniform Fast Fourier Transform on FPGAs

The Non-Uniform Discrete Fourier Transform (NuDFT) is a generalization of the DFT for data that may not be regularly sampled in spatial or temporal dimensions. This flexibility allows for benefits in situation where sensor placement cannot be guaranteed to be regular or where prior knowledge of the informational content could allow for better sampling patterns than a regular one. NuDFT is used in applications such as Synthetic Aperture Radar (SAR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). The NuDFT definition is shown in equation 2. Here the sample locations are points si in the set S. Each point, si has a complex value consisting of location or frequency components six and siy. The location or frequency components are, of course, not restriced to a discrete sampling grid.
Priority-aware transfer of SVC encoded video over MIMO communications system

A cross-layer method is proposed for optimizing, controlling and improving the quality of video transmission over wireless networks using scalable video coding (SVC) and multiple-input multiple-output (MIMO) transmission with channel state feedback (CSI). Multiple video sub-streams are created by a content-based partitioning and sorting of the enhancement layers produced with the SVC extension of H.264/AVC. Unlike in existing methods, the prioritized bit-streams are transmitted by actively performing power adjustment and antenna selection using a bit-stream prioritization matrix to modify the power allocation procedure of a recently proposed MIMO scheme. The power allocation strategy results in different bit error rate (BER) experienced by the bit-streams. Simulation results show an improved performance compared with power allocation that equalizes BER over the different channels.
A new class of two-channel biorthogonal filter banks and wavelet bases

Proposes a novel framework for a new class of two-channel biorthogonal filter banks. The framework covers two useful subclasses: i) causal stable IIR filter banks. ii) linear phase FIR filter banks. There exists a very efficient structurally perfect reconstruction implementation for such a class. Filter banks of high frequency selectivity can be achieved by using the proposed framework with low complexity. The properties of such a class are discussed in detail. The design of the analysis/synthesis systems reduces to the design of a single transfer function. Very simple design methods are given both for FIR and IIR cases. Zeros of arbitrary multiplicity at aliasing frequency can be easily imposed, for the purpose of generating wavelets with regularity property. In the IIR case, two new classes of IIR maximally flat filters different from Butterworth filters are introduced. The filter coefficients are given in closed form. The wavelet bases corresponding to the biorthogonal systems are generated. the authors also provide a novel mapping of the proposed 1-D framework into 2-D. The mapping preserves the following: i) perfect reconstruction; ii) stability in the IIR case; iii) linear phase in the FIR case; iv) zeros at aliasing frequency; v) frequency characteristic of the filters.
Accurate Image Based Localization by Applying SFM and Coordinate System Registration

Finding accurate positions of mobile devices based on visual information involves searching for query-matching images in a very large dataset, typically containing millions of images. Although the main problem is designing a reliable image retrieval engine, accurate localization also depends on a good fusion algorithm between the GPS data (geo-tags) of each query-matching image and the query image. This paper proposes a new method for reliable estimation of the actual query camera position (geo-tag) by applying structure from motion (SFM) with bundle adjustment for sparse 3D camera position reconstruction, and a linear rigid transformation between two different 3D Cartesian coordinate systems. The experimental results on more than 170 query images show the proposed algorithm returns accurate results for a high percentage of the samples. The error range of the estimated query geo-tag is compared with other related research and indicates an average error less than 5 meters that improves on some of the published works.
A vision based system for traffic lights recognition

In this article a traffic light recognition with status detection system is introduced. The system is evaluated from a generic point of view but the applications range from Intelligent Transportation System (ITS) to visual impaired and color vision deficiencies aid to safely cross streets. The algorithm is based on color segmentation in HSV color space. After that candidates reduction is performed using a pipeline approach to speed up the algorithm. Resulting candidates are input to feature extraction and support vector machine is then applied. For the training of the Support Vector Machine a database with images collected in Chicago is used. Unlike other works the purpose is to evaluate different performance according to the feature extraction. In particular HOG, HAAR and LBP features are compared. The purpose is also to create a database to be used from other researchers. The result is accurate and reliable provided that good quality images are input to the system.