nils-herrmann/clean_scirepeval huggingface dataset:
Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems

Logging information on moving objects is crucial in video surveillance systems. Distributed multi-camera systems can provide the appearance of objects/people from different viewpoints and at different resolutions, allowing a more complete and precise logging of the information. This is achieved through consistent labeling to correlate collected information of the same person. This paper proposes a novel approach to consistent labeling also capable to fully characterize groups of people and to manage miss segmentations. The ground-plane homography and the epipolar geometry are automatically learned and exploited to warp objects' principal axes between overlapped cameras. A MAP estimator that exploits two contributions (forward and backward) is used to choose the most probable label configuration to be assigned at the handoff of a new object. Extensive experiments demonstrate the accuracy of the proposed method in detecting single and simultaneous handoffs, miss segmentations, and groups.
Covariance descriptors on moving regions for human detection in very complex outdoor scenes

The detection of humans in very complex scenes can be very challenging, due to the performance degradation of classical motion detection and tracking approaches. An alternative approach is the detection of human-like patterns over the whole image. The present paper follows this line by extending Tuzel et al.'s technique  based on covariance descriptors and LogitBoost algorithm applied over Riemannian manifolds. Our proposal represents a significant extension of it by: (a) exploiting motion information to focus the attention over areas where motion is present or was present in the recent past; (b) enriching the human classifier by additional, dedicated cascades trained on positive and negative samples taken from the specific scene; (c) using a rough estimation of the scene perspective, to reduce false detections and improve system performance. This approach is suitable in multi-camera scenarios, since the monolithic block for human-detection remains the same for the whole system, whereas the parameter tuning and set-up of the three proposed extensions (the only camera-dependent parts of the system), are automatically computed for each camera. The approach has been tested on a construction working site where complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.
Detecting Moving Shadows: Algorithms and Evaluation

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated "ground-truth" data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.
Optimized Block-Based Connected Components Labeling With Decision Trees

In this paper, we define a new paradigm for eight-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. First, we exploit and extend the decision table formalism introducing or-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Second, we propose a new scanning technique that moves on a 2 × 2 pixel grid over the image, which is optimized by the automatically generated decision tree. An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms.
Undersampled dynamic magnetic resonance imaging using kernel principal component analysis

Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the kernel principal component analysis (KPCA) to exploit the sparseness of the dynamic image sequence in the feature space. Specifically, we apply KPCA to represent the temporal profiles of each spatial location and reconstruct the images through a modified pre-image problem. The underlying optimization algorithm is based on variable splitting and fixed-point iteration method. Simulation results show that the proposed method outperforms conventional CS method in terms of aliasing artifact reduction and kinetic information preservation.
Improving image reconstructions for simultaneous multi-slice readout-segmented diffusion MRI data with phase errors

Readout-segmented echo planar imaging (RS-EPI) combined with controlled aliasing simultaneous multi-slice (SMS) acquisition improves spatial resolution of diffusion-weighted images (DWIs) with a scan time that is reduced by a factor proportional to the number of simultaneous slices. Split slice-GRAPPA (SSG) is a commonly used method to de-alias SMS DWIs using kernels trained from baseline b=0 images. When applying SSG to datasets acquired from a RS-EPI sequence, we found that SSG kernels trained from baselines do not de-alias DWIs effectively due to baseline phase errors. To overcome this issue, in this work we propose an iterative approach, termed iterative Split slice-GRAPPA (I-SSG), to train improved kernels using estimated DWIs rather than only the baseline images. Our results from two stroke patients show that the proposed I-SSG algorithm produces consistently better reconstructions in the presence of baseline phase errors. The proposed I-SSG algorithm yields over 50% improvement over the SSG method in Fractional anisotropy (FA) and Mean Diffusion (MD) estimations for slice reduction factors of up to R = 4.
Undersampled dynamic magnetic resonance imaging using patch-based spatiotemporal dictionaries

Dynamic magnetic resonance imaging (dMRI) requires high spatial and temporal resolutions, which is challenging due to the low imaging speed. To reduce the imaging time, a patch-based spatiotemporal dictionary learning (DL) model is proposed for compressed-sensing reconstruction of dynamic images from undersampled data. Specifically, the dynamic image sequence is divided into overlapping patches along both the spatial and temporal directions. These patches are expected to be sparsely represented over a set of temporal-dependent spatiotemporal dictionaries. The images are then reconstructed from the undersampled data in (k,t) space under such sparseness constraints, where the dictionaries are learned iteratively. Alternating optimization is applied to solve the problem. Simulation results show that the proposed method is capable of preserving details in both spatial and temporal directions.
Accelerating sensitivity encoding using Compressed Sensing

The combination of Compressed Sensing (CS) and SENSitivity Encoding (SENSE) for improving MRI acquisition speed and robustness has recently drawn great attentions. However, in the direct combination, the encoding matrix which represents the Fourier transform of channel-specific sensitivity modulation is not guaranteed to be a good CS matrix. In this paper, we propose a different approach that applies CS and SENSE sequentially. The method first uses CS to reconstruct a set of aliased images in each coil, and then applies the basic SENSE on these images to reconstruct the final image. The total reduction factor can achieve the product of the factors of each individual method. The experimental results show that overall performance of our proposed method is superior to the direct combination method with the same reduction factor.
A kernel-based compressed sensing approach to dynamic MRI from highly undersampled data

Compressed sensing (CS) has been used in dynamic MRI to reduce the data acquisition time. Several sparsifying transforms have been investigated to sparsify the dynamic image sequence. Most existing works have studied linear transformations only. In this paper, we proposed a novel kernel-based compressed sensing approach to dynamic MRI. The method represents the image sequence sparsely and adaptively using nonlinear transformations. Such nonlinearity is implemented using the kernel method, which maps the acquired undersampled k-space data onto a high dimensional feature space, then reconstructs the image sequence in the corresponding feature space using the conventional compressed sensing, and finally convert the image sequence back into the original space. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic ASL-based perfusion MRI over the state-of-the-art method where linear transform is used.
Parallel MRI Using Phased Array Coils

Parallel MRI using phased array coils can be viewed as an application of the multichannel sampling theory. Specifically, in the case of uniform 1-D undersampling, Papoulis' classical reconstruction formulas correspond well to the existing parallel MRI reconstruction algorithms, and a number of practical issues can be analyzed in this context. However, parallel MRI also presents several unique signal processing problems, whose solutions can help maximize the potential of parallel MRI for fast imaging. While existing parallel MRI methods were developed independently of the multichannel sampling theory, making such a connection may help develop more optimal methods for parallel MRI data acquisition and image reconstruction.
Joint estimation of image and coil sensitivities in parallel MRI

Parallel magnetic resonance imaging (MRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various dynamic imaging applications. However, there are still a number of image reconstruction issues that have not been fully addressed, thereby limiting the level of speed enhancement achievable with the technology. This paper considers the inaccuracy of coil sensitivities in conventional reconstruction methods such as SENSE, and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative algorithm. Experimental results demonstrate the effectiveness of the proposed method especially when large acceleration factors are used
An Efficient Non-Iterative Reconstruction Algorithm for Parallel MRI with Arbitrary K-Space Trajectories

Parallel imaging using multiple receiver coils has emerged as an effective tool to reduce imaging time in various MRI applications. Although several different image reconstruction methods have been developed and demonstrated to be successful for Cartesian k-space trajectories, there is a lack of efficient reconstruction methods for arbitrary trajectories. In this paper, we formulate the reconstruction problem in k-space and propose a novel image reconstruction method that is fast and effective for arbitrary trajectories. To obtain the desired image, the method reconstructs the Nyquist-sampled k-space data of the image on a uniform Cartesian grid from the undersampled multichannel k-space data on an arbitrary grid, followed by inverse Fourier transform. We demonstrate the effectiveness of the proposed fast algorithm using simulations. In particular, we compare the proposed method with the existing iterative method and show that the former is able to achieve similar image quality to the latter but with reduced computational complexity
Parallel generalized series MRI: algorithm and application to cancer imaging

MRI is a relatively slow imaging technique. Although imaging speeds have increased dramatically over the last three decades, many clinical and research applications, ranging from contrast-enhanced dynamic imaging of breast tumors to cardiac imaging, require still faster imaging methods. To address this problem, this paper presents a novel algorithm to integrate generalized series (OS) imaging with parallel imaging using multiple receiver coils. This algorithm takes advantage of both the conventional parallel data acquisition scheme and the GS model-based reduced-scan imaging method to achieve higher spatiotemporal resolution in dynamic imaging. The algorithm has been validated using both simulated and experimental data from dynamic contrast-enhanced MRI experiments, which produced excellent results. We expect the algorithm to be useful for a number of dynamic imaging applications, especially contrast-enhanced imaging of tumors.
Improved spiral sense reconstruction using a multiscale wavelet model

SENSE has been widely accepted and extensively studied in the community of parallel MRI. Although many regularization approaches have been developed to address the ill-conditioning problem for Cartesian SENSE, fewer efforts have been made to address this problem when the sampling trajectory is non-Cartesian. For non-Cartesian SENSE using the iterative conjugate gradient method, ill- conditioning can degrade not only the signal-to-noise ratio, but also the convergence behavior. This paper proposes a regularization technique for non-Cartesian SENSE using a multiscale wavelet model. The technique models the desired image as a random field whose wavelet transform coefficients obey a generalized Gaussian distribution. The effectiveness of the proposed method has been validated by in vivo experiments.
A model-based method with joint sparsity constraint for direct diffusion tensor estimation

Diffusion tensor imaging (DTI) has been widely used for nondestructive characterization of microstructures of myocardium or brain connectivity. It requires repeated acquisition with different diffusion gradients. The long acquisition time greatly limits the clinical application of DTI. In this paper, a novel method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images in direct estimation of the diffusion tensor from highly undersampled k-space data. Experimental results demonstrate that the proposed method is able to estimate the diffusion tensors more accurately than the existing method when a high net reduction factor is used.
Enhancing Bayesian PET image reconstruction using neural networks

The performance of smoothness-enforced Bayesian PET image reconstruction is strongly affected by the weight on regularization. Compromises need to be made between variance and spatial resolution. In this work, we propose to use an artificial neural network (ANN) to fuse the image versions reconstructed from a maximum a posteriori (MAP) algorithm with different regularizing weights for quantitative improvement. Using the BrainWeb phantoms, we simulated PET data at different count levels for different subjects with and without lesions. We designed an ANN and trained it using MAP reconstructions with different regularization parameters for one normal subject at a specific count level. Reconstructed images from the simulations with lesions, of other count levels, and of other subjects were fused using the trained ANN. In all of the testing experiments, the designed ANN fusion keeps the noise level as low as what the MAP algorithm achieves at heavy regularization while significantly reduces the bias or improves the lesion contrast. We conclude that the proposed ANN fusion method removes the need for tuning the regularization of the MAP algorithm.
JOINT ESTIMATION OF IMAGE AND COIL SENSITIVITIES IN PARALLEL SPIRAL MRI

Spiral MRI has received increasing attention due to its reduced T 2*-decay and robustness against bulk physiologic motion. In parallel imaging, spiral trajectories are especially of great interest due to their inherent self-calibration capabilities, which is especially useful for dynamic imaging applications such as fMRI and cardiac imaging. The existing self-calibration techniques for spiral use the k-space center data that are sampled densely in the accelerated acquisition for coil sensitivity estimation. There exists a trade-off in choosing the radius of the center data: it must be sufficiently large to contain all major spatial frequencies of coil sensitivity, but not too large to cause significant aliasing artifacts due to undersampling below Nyquist rate as the trajectory moves away from the center k-space. To address this tradeoff, we generalize the JSENSE approach, which has demonstrated success in Cartesian case, to spiral trajectory. Specifically, the method jointly estimates the coil sensitivities and reconstructs the desired image through cross validations so that the sensitivities are estimated from the full data recovered by SENSE instead of the center k-space data only, thereby increasing high frequency information without introducing aliasing artifacts. We use experimental results to show the proposed method improves sensitivities, which leads to a more accurate SENSE reconstruction
Simultaneous image reconstruction and sensitivity estimation in parallel MRI using blind compressed sensing

Parallel magnetic resonance imaging (MRI) reconstruction problem can be formulated as a multichannel sampling problem where solutions can be sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts or degraded SNR. In the context of parallel MRI, this work investigates the blind multichannel under-sampling problem where both the channel functions and signal are reconstructed simultaneously under sparseness constraints. We propose a novel algorithm to reconstruct both the coil sensitivities and image simultaneously from randomly undersampled, multichannel k-space data. The algorithm effectively applies the concept of compressed sensing (CS) to solve an underdetermined nonlinear problem, but is different from the conventional CS in that the sensing matrix is not known exactly. The proposed algorithm is shown to improve the reconstruction accuracy of SparseSENSE and L1-SPIRiT when the same number of measurements is used.
Sparsesense: Application of compressed sensing in parallel MRI

Compressed sensing (CS) has recently drawn great attentions in the MRI research community. The most desirable property of CS in MRI application is that it allows sampling of k-space well below Nyquist sampling rate, while still being able to reconstruct the image if certain conditions are satisfied. Recent work has successfully applied CS to reduce scanning time in conventional Fourier imaging. In this paper, the application of CS to parallel imaging, a fast imaging technique, is investigated to achieve an even higher imaging speed. The sampling scheme for incoherence is discussed and reconstruction method using Begman iteration is proposed. Our experiments show that the combined method, named SparseSENSE, can achieve a reduction factor higher than the number of channels.
Toeplitz block matrices in compressed sensing and their applications in imaging

Recent work in compressed sensing theory shows that sensing matrices whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal from a small number of measurements with high probability. In most medical imaging systems, the encoding matrices cannot take that form. Instead, they are Toeplitz block matrix. Motivated by this fact, we consider Toeplitz block matrices as the sensing matrices. We show that the probability of perfect reconstruction from a smaller number of filter outputs is also high if the filter coefficients are independently and identically-distributed random variable. Their applications in medical imaging is discussed. Simulation results are also shown to validate the theorem.
Determination of Fiber Orientation in MRI Diffusion Tensor Imaging Based on Higher-Order Tensor Decomposition

High angular resolution diffusion imaging (HARDI) techniques have been used for resolving multiple fiber directions within a voxel. Using HARDI, a high-order tensor can be obtained through generalized diffusion tensor imaging (GDTI). In this paper, based on the decomposition of the high-order diffusion tensors, a mathematical technique is presented which permits accurate resolution of multiple, randomly-oriented fiber tracts within tissue. A sequence of pseudo-eigenvalues and pseudo-eigenvectors are derived from the diffusion tensor through successive application of a best least-square rank-1 tensor approximation. These pseudo-eigenvalues and pseudo-eigenvectors are used to identify the major fiber directions within an individual image voxel. Results of a numerical simulation are presented to demonstrate the technique.
k-t CSPI: A dynamic MRI reconstruction framework for combining compressed sensing and parallel imaging

In this paper, we propose a new dynamic MR image reconstruction technique that combines the compressed sensing-based dynamic methods with parallel imaging techniques to achieve high accelerations. The method decouples the reconstruction process into two sequential steps. In the first step, a series of aliased dynamic images is reconstructed using a CS method from the highly undersampled £-space data. In the second step, the missing £-space data for the original image are reconstructed by the nonlinear GRAPPA technique. The sampling strategy for each step is thereby designed independently such that the incoherent undersampling requirement for CS and structured undersampling requirement for parallel imaging can be satisfied simultaneously. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art method.
On Tikhonov regularization for image reconstruction in parallel MRI

Parallel imaging using multiple receiver coils has emerged as an effective tool to reduce imaging time in various MRI applications. When a large number of receiver channels are used to achieve large acceleration factors, the image reconstruction problem can become very ill conditioned. This problem can be alleviated by optimizing the geometry of the coils or by mathematical regularization. Among the regularization methods, the Tikhonov scheme is most popular because of rough Gaussianity of the data noise, the easiness to incorporate prior information, as well as the existence of a closed-form solution. A central issue in implementing the Tikhonov scheme is the choice of the regularization parameter and the regularization image, which is addressed systematically in this paper. A new algorithm is also proposed for generating the regularization image and selecting the regularization parameter. Experimental results will be shown to demonstrate the performance of the algorithm.