nils-herrmann/clean_scirepeval huggingface dataset:
A two-term penalty function for inverse problems with sparsity constrains

Inverse problems with sparsity constrains, such Basis Pursuit denoising (BPDN) and Convolutional BPDN (CBPDN), usually use the ℓ1-norm as the penalty function; however such choice leads to a solution that is biased towards zero. Recently, several works have proposed and assessed the properties of other non-standard penalty functions (most of them non-convex), which avoid the above mentioned drawback and at the same time are intended to induce sparsity more strongly than the ℓ1-norm. In this paper we propose a two-term penalty function consisting of a synthesis between the ¿i-norm and the penalty function associated with the Non-Negative Garrote (NNG) thresholding rule. Although the proposed two-term penalty function is non-convex, the total cost function for the BPDN / CBPDN problems is still convex. The performance of the proposed two-term penalty function is compared with other reported choices for practical denoising, deconvolution and convolutional sparse coding (CSC) problems within the BPDN / CBPDN frameworks. Our experimental results show that the proposed two-term penalty function is particularly effective (better reconstruction with sparser solutions) for the CSC problem while attaining competitive performance for the denoising and deconvolution problems.
Catadioptric omnidirectional image inpainting via a multi-scale approach and image unwrapping

Omnidirectional catadioptric sensors are widely used in different applications as they allow the observation of a 360 field of view instantaneously. Their specific architecture necessarily induces carrying devices which obstruct the field of view of the sensor. This drawback can be a major obstacle to their use. This paper outlines a methodoly to inpaint catadioptric omnidirectional images, that means to remove and repair a part of the image. This method combines a multi-scale image inpainting (MII) algorithm with unwrapping techniques as the MII can not be applied in the omnidirectional image directly.
M-mode echocardiography image and video segmentation based on am-fm demodulation techniques

A novel methodology to automatically segment all the walls present in M-mode echocardiography images in order to determine the ventricular chamber dimensions and wall thickness is proposed using amplitude-modulation frequency modulation (AM-FM) demodulation techniques. Preliminary results show method's capability to segment the left ventricular posterior wall and the left ventricular side of the interventricular septum. using these results three measurements are possible: left ventricular posterior wall (LVPW) thickness, left ventricular dimension (LVDD) at end-diastole and left ventricular dimension (LVSD) at end-systole.
Translational and rotational jitter invariant incremental principal component pursuit for video background modeling

While Principal Component Pursuit (PCP) is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including a high computational cost, a batch operating mode, and sensitivity to camera jitter. In this paper we propose a novel fully incremental PCP algorithm for video background modeling that is robust to translational and rotational jitter. It processes one frame at a time, obtaining similar results to standard batch PCP algorithms, while being able to deal with translational and rotational jitter. It also has extremely low memory footprint, and a computational complexity that allows almost real-time processing.
An incremental principal component pursuit algorithm via projections onto the ℓ1 ball

Video background modeling, used to detect moving objects in digital videos, is a ubiquitous pre-processing step in computer vision applications. Principal Component Pursuit (PCP) PCP is among the leading methods for this problem. In this paper we proposed a new convex formulation for PCP, substituting the standard ℓ1 regularization with a projection onto the ℓ1-ball. This formulation offers an advantage over the known incremental PCP methods in practical parameter selection and ghosting suppression, while retaining the ability to be implemented in a fully incremental fashion, keeping all the desired properties related to such PCP methods (low memory footprint, adaptation to changes in the background, computational complexity that allows online processing).
Omnidirectional catadioptric image unwrapping via total variation regularization

A catadioptric sensor is composed of a hyperbolic mirror coupled with a camera. The registered images with such sensor have a field of view of 360 degrees; this property is very useful in robotics since it increases a robot's performance for navigation and localization. However, due to the significant distortions on the omnidirectional image, classical image processing can not be directly applied onto the image. The standard techniques to unwrap catadioptric images involve a projection step follow by an interpolation step. In this paper we propose to unwrap omnidirectional images via Total Variation (TV) regularization by casting the geometric projection as the forward operator. This has two advantages: if the acquired images are noisy (due to dust onto the mirror or wireless transmission errors), the noise can be effectively remove while the unwrapping step is performed; moreover if the mirror or camera's lenses have any type of aberrations they could be included as part of the forward operator and then compare the standard unwrapping method with our proposed (TV based) method and show the superior reconstruction quality of the proposed method when the acquired images are corrupted with salt-and-pepper noise.
Simple Recurrent Networks Learn Context-Free and Context-Sensitive Languages by Counting

It has been shown that if a recurrent neural network (RNN) learns to process a regular language, one can extract a finite-state machine (FSM) by treating regions of phase-space as FSM states. However, it has also been shown that one can construct an RNN to implement Turing machines by using RNN dynamics as counters. But how does a network learn languages that require counting? Rodriguez, Wiles, and Elman (1999) showed that a simple recurrent network (SRN) can learn to process a simple context-free language (CFL) by counting up and down. This article extends that to show a range of language tasks in which an SRN develops solutions that not only count but also copy and store counting information. In one case, the network stores information like an explicit storage mechanism. In other cases, the network stores information more indirectly in trajectories that are sensitive to slight displacements that depend on context. In this sense, an SRN can learn analog computation as a set of interdependent counters. This demonstrates how SRNs may be an alternative psychological model of language or sequence processing.
Optical motion tracking in earthquake-simulation shake table testing: Preliminary results

Sensors such accelerometers and displacement transducers are generally used in earthquake-simulation shake table testing to measure the induced motions. In particular the Anti-seismic Structure Laboratory at the Pontificia Universidad Cato¿lica del Peru¿ (PUCP) uses LVDT (linear variable differential transformer) sensors, which can achieve accurate measurements. However there are limitations in the number of measuring points; moreover, the required instrumentation is demanding and destructive tests can not be measured with such devices. We present the preliminary results of an optical motion tracking system to measure the induced motions for shake table testing at the PUCP's Anti-seismic Structure Laboratory.
Efficient Minimization Method for a Generalized Total Variation Functional

Replacing the lscr2 data fidelity term of the standard total variation (TV) functional with an lscr1 data fidelity term has been found to offer a number of theoretical and practical benefits. Efficient algorithms for minimizing this lscr1-TV functional have only recently begun to be developed, the fastest of which exploit graph representations, and are restricted to the denoising problem. We describe an alternative approach that minimizes a generalized TV functional, including both lscr2-TV and lscr1-TV as special cases, and is capable of solving more general inverse problems than denoising (e.g., deconvolution). This algorithm is competitive with the graph-based methods in the denoising case, and is the fastest algorithm of which we are aware for general inverse problems involving a nontrivial forward linear operator.
2D amplitude-modulation frequency-modulation - based method for motion estimation

We present a new approach to compute the motion estimation in digital videos using 2D amplitude-modulation frequency-modulation (AM-FM) methods. The optical flow vectors are computed using an iteratively reweighted norm for total variation (IRN-TV) algorithm. We compare the proposed method using synthetic videos versus a previous three-dimensional AM-FM based method and available motion estimation methods available in free and commercial software. The proposed approach produces dense estimates that are more accurate than competitive methods.
Multiplicative updates algorithm to minimize the generalized total variation functional with a non-negativity constraint

We propose an efficient algorithm to solve the generalized Total Variation (TV) functional with a non-negativity constraint. This algorithm, which does not involve the solution of a linear system, but rather multiplicative updates only, can be used to solve the denoising and deconvolution problems. The derivation of our method is straightforward once the generalized TV functional is cast as a Non-negative Quadratic Programming (NQP) problem. The proposed algorithm offers a fair computational performance to solve the ℓ2-TV and ℓ1-TV denoising and deconvolution problems and it is the fastest algorithm of which we are aware for general inverse problems involving a nontrivial forward linear operator and a non-negativity constraint.
An Iteratively Reweighted Norm Algorithm for Total Variation Regularization

Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard lscr2 data fidelity term with an lscr1 norm. We propose a simple but very flexible and computationally efficient method, the iteratively reweighted norm algorithm, for minimizing a generalized TV functional which includes both the lscr2-TV and and lscr2-TV problems.
Real time AM-FM analysis of ultrasound video

In this paper, we present a real-time amplitude-modulation frequency-modulation (AM-FM) analysis system, and apply the system to M-mode ultrasound video. The estimated frequency-modulation (FM) process is shown to capture the cardiac wall deformation, while the amplitude modulation process is shown to follow changes in cardiac wall material. Results from segmenting the septum and left-ventricle walls are also presented.
Robust Multiscale AM-FM Demodulation of Digital Images

In this paper, we introduce new multiscale AM-FM demodulation algorithms that provide significant improvements in accuracy over previously reported approaches. The improvements are due to the use of new filterbanks based on separable filters supported in just two quadrants. The QEA, robust-QEA and Vakman methods are improved with this new filterbanks. A number of 2-D AM-FM examples are presented, where we observe significant accuracy improvements. For Lena, the mean-square-error for the AM-FM harmonic reconstruction is reduced by 88.31%. Similarly, for a AM-FM synthetic example of sinusoidal phase and Gaussian amplitude, the mean-square-error is reduced by: (i) 70.86% for the reconstruction, (ii) 99.66% for the instantaneous amplitude and (iii) 96.52% for the sinusoidal instantaneous frequency component.
Practical hand tracking solution by alternating the use of a priori information

Hand tracking is the automatic detection and displaying of the user hands captured by a camera throughout a video. This paper proposes a practical solution to two main hand tracking problems: the mutual occlusion between hands and the recovery from a lost tracking. This is done by alternating the use of a priori information about past hand locations according to the presence of a problematic situation. We propose a deterministic solution capable of working at different video resolutions without the need of any early learning stage or hard-coded initial calibration. Thus, it is practical in the sense of its implementation and use. Our experimental results, which are compared to the state of the art, shows that our solution is computationally efficient and has a good performance in different lighting and motion situations, both in terms of speed and precision error.
AN ℓ1-TV algorithm for deconvolution with salt and pepper noise

There has recently been considerable interest in applying Total Variation regularization with an ℓ1 data fidelity term to the denoising of images subject to salt and pepper noise, but the extension of this formulation to more general problems, such as deconvolution, has received little attention. We consider this problem, comparing the performance of ℓ1-TV deconvolution, computed via our Iteratively Reweighted Norm algorithm, with an alternative variational approach based on Mumford-Shah regularization. The ℓ1-TV deconvolution method is found to have a significant advantage in reconstruction quality, with comparable computational cost.
A regularized optimization approach for AM-FM reconstructions

The AM-FM Dominant and Channelized Component Analysis (DCA and CCA respectively) , consist of applying a filter bank to the Hilbert-tranformed image, and then proceeding with the AM-FM demodulation of each band-pass filtered image. Whereas AM-FM reconstructions based on the CCA use a reasonably small number of locally coherent components, those based on the DCA only use one component: the estimates from the channel with the maximum amplitude estimate. Both types of reconstructions are known to produce noticeable visual artifacts. We propose a method, based on a regularized optimization of the estimates from the CCA, which attains a small number of locally coherent components and simultaneously enforces a piecewise smooth constrain for the amplitude functions. Moreover, this method offers high quality reconstructions when compared to standard CCA and DCA reconstructions and state of the art techniques .
MOOCEP: Towards a Method for Building Massive Open Online Courses for Elderly People

Nowadays, the elderly people population use Internet in order to perform different activities, such as entertainment, reading news, or learning a variety of topics. However, applications and websites do not take into account specific requirements of elderly people. Therefore, it is necessary to have methodologies, processes, and tools, which consider andragogical techniques and strategies that enable the creation of appropriate software artifacts that improve the interaction between elderly people and those artifacts. This paper proposes a method to support the creation of Massive Open Online Courses (MOOCs), which takes into account andragogical strategies to improve learning objects when they are used by elderly people. In order to demonstrate the feasibility of this proposal, it is presented the creation of a MOOC which follows each one of the steps included in the method, thus obtaining, a MOOC oriented to this target.
An online background subtraction algorithm using a contiguously weighted linear regression model

In this paper, we propose a fast online background subtraction algorithm detecting a contiguous foreground. The proposed algorithm consists of a background model and a foreground model. The background model is a regression based low rank model. It seeks a low rank background subspace and represents the background as the linear combination of the basis spanning the subspace. The foreground model promotes the contiguity in the foreground detection. It encourages the foreground to be detected as whole regions rather than separated pixels. We formulate the background and foreground model into a contiguously weighted linear regression problem. This problem can be solved efficiently and it achieves an online scheme. The experimental comparison with most recent algorithms on the benchmark dataset demonstrates the high effectiveness of the proposed algorithm.
Computationally inexpensive parallel parking supervisor based on video processing

Parallel parking, in general, is a moderate difficulty maneuver. Moreover, for inexperienced drivers, it can be a stressful situation that can lead to mistakes such as being far from the sidewalk or damage another vehicle resulting in traffic tickets that range from simple parking violation to crash-related violations. In this paper we propose a computationally effective approach to perform a collision-free parallel parking. The method will calculate the minimum parking space needed and then the minimum possible path for the parallel parking. This method is computationally inexpensive in comparison with the state of art. Moreover, it could be used by any car because the parameters needed to perform all computations are taken from published datasheets.
Fast omni-image unwarping using pano-mapping pointers array

Omni-cameras are becoming ubiquitous in several applications that require a wide field of view (such as 3D reconstruction, video surveillance, robot vision, etc.) and thus the need to reduce computational time during omni-image un-warping. In this paper a computationally efficient alternative referred as pano-mapping pointers array (PMPA) is proposed. First, a buffer is created to save the omni-image. Then, a PMPA is created, where each entry points to a specific place in the buffer depending on the interpolation desired (nearest neighbors or bilinear interpolation). Finally, using the PMPA we unwarp the omni-image. The PMPA is created once for an omni-camera, interpolation method and panoramic image resolution. Experiments on a standard computer demonstrate that the proposed method is about 5.8 (nearest neighbors) and 2.1 (bilinear interpolation) times faster than the classic pano-mapping table method and about 1.7 (nearest neighbors) times faster than the one-eighth pano-mapping table method.
Fast principal component pursuit via alternating minimization

We propose a simple alternating minimization algorithm for solving a minor variation on the original Principal Component Pursuit (PCP) functional. In computational experiments in the video background modeling problem, the proposed algorithm is able to deliver a consistent sparse approximation even after the first outer loop, (taking approximately 12 seconds for a 640 × 480 × 400 color test video) which is approximately an order of magnitude faster than Inexact ALM to construct a sparse component of the same quality.
Moving object detection in videos using principal component pursuit and convolutional neural networks

Object recognition in videos is one of the main challenges in computer vision. Several methods have been proposed to achieve this task, such as background subtraction, temporal differencing, optical flow among others. Since the introduction of Convolutional Neural Networks (CNN) for object detection in the Imagenet Large Scale Visual Recognition Competition (ILSVRC), its use for image detection and classification has increased, becoming the state-of-the-art in object detection and classification. In this paper we propose to use Robust PCA (RPCA, a.k.a. Principal Component Pursuit, PCP), as a video background modeling pre-processing step, before using the Faster R-CNN model, in order to improve the overall performance of detection and classification of, specifically, the moving objects. Furthermore, we present extensive computational results that were carried out in three different platforms: A high-end server with a Tesla K40m GPU, a desktop with a Tesla K10m GPU and the embedded system Jetson TK1. Our classification results attain competitive or superior performance (F-measure) with respect to the state-of-the-art, while at the same time, reducing the classification time in all architectures by a factor raging between 4% and 25%.
Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Video background modeling is an important preprocessing stage for various applications and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras. We present a novel, fully incremental PCP algorithm, named incPCP-PTI, that is able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low rank plus additive incremental matrix method capable of handling these scenarios. Results on synthetic videos and CDNET2014 videos show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video.
Bidimensional median filter for parallel computing architectures

The median filter is a non-linear filter used for removal of salt and pepper noise from images. Each pixel of the image is replaced by the median of its surrounding elements, the median value is calculated by sorting the data. The complexity of the sorting algorithms used on the median filters are O(n2) or O(n), depending on the kernel size. Those algorithms were formulated for scalar single processor computers, with few of them successfully adapted and implemented for computer with a parallel architecture. In this paper we present a novel sorting algorithm, with O(n) computational complexity and a highly parallelizable structure, based on the Complementary Cumulative Distribution Function. Furthermore, a 2D median filter based on our proposed sorting algorithm can achieve O(1) complexity. We have implemented our proposed algorithm in two parallel architectures: SIMD Intel and CUDA, which have a throughput of 12.8 and 35 ~ 57 megapixels per second respectively.
Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy

We develop new multiscale amplitude-modulation frequency-modulation (AM-FM) demodulation methods for image processing. The approach is based on three basic ideas: (i) AM-FM demodulation using a new multiscale filterbank, (ii) new, accurate methods for instantaneous frequency (IF) estimation, and (iii) multiscale least squares AM-FM reconstructions. In particular, we introduce a variable-spacing local linear phase (VS-LLP) method for improved instantaneous frequency (IF) estimation and compare it to an extended quasilocal method and the quasi-eigen function approximation (QEA). It turns out that the new VS-LLP method is a generalization of the QEA method where we choose the best integer spacing between the samples to adapt as a function of frequency. We also introduce a new quasi-local method (QLM) for IF and IA estimation and discuss some of its advantages and limitations. The new IF estimation methods lead to significantly improved estimates. We present different multiscale decompositions to show that the proposed methods can be used to reconstruct and analyze general images.