nils-herrmann/clean_scirepeval huggingface dataset:
Best view selection with geometric feature based face recognition

Nowadays, an important problem in multi-camera systems is how to select the camera with the best frontal view of a person in order to visualize to an observer. Therefore, we present a minimum score based criterion for best view selection, based on face recognition with geometric features. In this approach, faces are represented with Curve Edge Maps (CEMs), which are collections of polynomial curves with a convex region. Face recognition is performed by matching face CEMs driven by histograms of intensities and histograms of relative positions. The resulting face recognition scores are employed as quality-of-view measures. They indicate whether or not persons are seen by cameras in frontal view. Experiments show that the method is robust and efficient when selecting the best view in a multi-camera system. Furthermore, our method outperforms view selection based on face detection only.
Robust plane-based calibration for linear cameras

A linear, or 1D, camera is a type of camera that sweeps a linear sensor array over the scene, rather than capturing the scene using a single impression on a 2D sensor array. They are often used in satellite imagery, industrial inspection, or hyperspectral imaging. In satellite imaging calibration is often done through a collection of ground points for which the 3D locations are known. In other applications, e.g. hyperspectral imaging, such known points are not available and annotating many different points is onerous. Hence we will use a checkerboard for calibration. The state-of-the-art method for linear camera calibration with a checkerboard becomes unstable when the checkerboards are parallel to the image plane. Our proposed method1 yields more accurate camera calibrations without suffering from this shortcoming.
Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
A Distributed Coding-Based Extension of a Mono-View to a Multi-View Video System

Multi-view video systems provide 3D information about the captured scene. This 3D information can be useful for many emerging applications, e.g. 3D TV or virtual reality. However, many current video systems consist only of one camera and consequently do not capture the 3D content of a scene. In this paper, we therefore present an efficient, flexible and low-complexity method for extending an existing mono video system to a 3D system. The main idea is to develop a coding framework that starts from a single camera and that can be flexibly extended by low-complexity cameras to capture 3D video data. These cameras do not perform any motion or disparity estimation, but still good coding efficiency is achieved by relying on distributed video (DV) coding principles, i.e. jointly decoding of the independently encoded frames of the multi-view cameras. If we compare our coding results with the results for low-complexity DV coding of a single video, then higher efficiency is achieved since not only the motion between the frames of the video but also disparity between different views of the array of cameras is exploited at the decoder.
Improving Random Forest With Ensemble of Features and Semisupervised Feature Extraction

In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.
Wavelet Domain Image Denoising for Non-Stationary Noise and Signal-Dependent Noise

We develop a low-complexity overcomplete wavelet domain method for denoising digital images corrupted with non-stationary white additive Gaussian noise. The noise level for each pixel is estimated from a local window around that pixel. We use a shrinkage function that adapts itself to the noise level and to the spatially changing statistics of the image. Experiments show that this noise model has good results for different non-stationary noise sources. Finally, we extend our method for denoising images corrupted with signal-dependent noise.
A computational efficient external energy for active contour segmentation using edge propagation

Active contours or snakes are widely used for segmentation and tracking. We propose a new active contour model, which converges reliably even when the initialization is far from the object of interest. The proposed segmentation technique uses an external energy function where the energy slowly decreases in the vicinity of an edge. This new energy function is calculated using an efficient dual scan line algorithm. The proposed energy function is tested on computational speed, its effect on the convergence speed of the active contour and the segmentation result. The proposed method gets similar segmentation results as the gradient vector flow active contours, but the energy function needs much less time to calculate.
Compass: a joint framework for Parallel Imaging and Compressive Sensing in MRI

Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction techniques that have recently been applied to increase MRI performance. In this paper we demonstrate that a combined analysis of the pMRI and CS problems leads to a conceptually simple, yet effective technique that outperforms independent approaches to both reconstruction problems. We argue that the proposed technique is also naturally resilient to noise, due to its relation to the MAP image denoising formulation. A modified Basis Pursuit (BP) formulation of the CS-MRI problem allows it to handle the pMRI problem at the same time. We also present an exact solution to this BP problem, using the split Bregman technique, with discrete shearlet transform (DST) regularization. The DST is an excellent choice for natural image applications, due to its optimal sparsity property. Results show that this Compressive Parallel Sensing (COMPASS) reconstruction algorithm outperforms more traditional MRI reconstruction algorithms in both pMRI and CS experiments.
A low resolution multi-camera system for person tracking

The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30×30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras.
Adaptively subsampled image coding with warped polynomials

This paper presents an adaptive image coding method which uses an image dependent orthogonal transform. The method is a generalization of a one-dimensional coding scheme which represents signals as linear combinations of signal-dependent time-warped (TW) orthogonal polynomials. This paper briefly summarizes the theory of time-warped signal coding; next, it describes the new image compression method. Basically, this method transforms all of the image rows and columns with different, but not completely independent bases. By adapting the bases to the image, high quality coding is achieved even when retaining only a small number of transform coefficients. Also, the overhead involved in coding the bases is very small. This paper shows that at net bit rates of about 0.3 bpp, images compressed by the new method are sharper and less distorted by ringing effects than those produced by JPEG or the full-image DCT. Block distortion, which is an important problem in JPEG at 0.3 bpp cannot occur in the new method, since it transforms the full image instead of blocks.<<ETX>>
Evaluating color difference measures in images

The most well known and widely used method for comparing two homogeneous color samples is the CIEDE2000 color difference formula because of its strong agreement with human perception. However, the formula is unreliable when applied over images and its spatial extensions have shown little improvement compared with the original formula. Hence, researchers have proposed many methods intending to measure color differences (CDs) in natural scene color images. However, these existing methods have not yet been rigorously compared. Therefore, in this work we review and evaluate CD measures with the purpose of answering the question to what extent do state-of-the-art CD measures agree with human perception of CDs in images? To answer the question, we have reviewed and evaluated eight state-of-the-art CD measures on a public image quality database. We found that the CIEDE2000, its spatial extension and the just noticeable CD measure perform well in computing CDs in images distorted by black level shift and color quantization algorithms (correlation higher than 0.8). However, none of the tested CD measures perform well on identifying CDs for the variety of color related distortions tested in this work, e.g., most of the tested CD measures showed a correlation lower than 0.65.
A hybrid fusion based frontal-lateral collaborative pedestrian detection and tracking

In the current context of autonomous or assisted driving, reliable pedestrian detection plays one of the key roles in performing situation awareness tasks. In this paper we propose a new method for pedestrian tracking based on hybrid sensor fusion. Pedestrian detection and tracking based on a single feature type cannot always achieve the desired performance, especially in low visibility conditions. To improve the pedestrian detection rate, in this paper we propose a method for high-level decision fusion based on the detection outputs from multiple cameras. Low-level processing entails the capture, calibration and registration steps. The high-level processing consists of pedestrian detection, tracking of the detected pedestrians, and fusing the tracking outputs into a common multi-camera view. By relying on parallel tracks we are able to detect objects and follow them more efficiently without losing track. Moreover, each target receives specific signature which serves as an identification of the individual target. When the target pedestrian from any of the side views enters the area of the front looking camera, the identity of its track is handed over to the corresponding front view tracker. The proposed strategy improves early detection of pedestrians, which is critical for driving safety.
A wavelet-based image denoising technique using spatial priors

We propose a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov random field model. An iterative updating technique known as iterated conditional modes (ICM) is applied to estimate the binary masks containing the positions of those wavelet coefficients that represent the useful signal in each subband. For each wavelet coefficient a shrinkage factor is determined, depending on its magnitude and on the local spatial neighbourhood in the estimated mask. We derive analytically a closed form expression for this shrinkage factor.
Motion compensated de-interlacing for both real time video and still images

The interlaced video scan format suffers from major flaws such as visual artifacts and unsuitability for devices such as LCD displays, video printers, computers which require or prefer the progressive scan format. De-interlacing algorithms convert a video signal from the interlaced scan format to the progressive scan format. Next to simple spatial interpolation, two categories of de-interlacing techniques are available: motion adaptive and motion compensating methods. The latter have more potential to produce better results but are also more complex since they require accurate subpixel motion estimation. This paper presents a motion compensating de-interlacing technique based on the Irani and Peleg (1991) superresolution algorithm. Appropriate weighting terms are introduced to take into account the interlaced scanning format. Two operational modes are derived: a single iteration mode for real time video de-interlacing, and a multiple iteration mode for enhanced still image generation.
Efficient approximate foreground detection for low-resource devices

A broad range of very powerful foreground detection methods exist because this is an essential step in many computer vision algorithms. However, because of memory and computational constraints, simple static background subtraction is very often the technique that is used in practice on a platform with limited resources such as a smart camera. In this paper we propose to apply more powerful techniques on a reduced scan line version of the captured image to construct an approximation of the actual foreground without overburdening the smart camera. We show that the performance of static background subtraction quickly drops outside of a controlled laboratory environment, and that this is not the case for the proposed method because of its ability to update its background model. Furthermore we provide a comparison with foreground detection on a subsampled version of the captured image. We show that with the proposed foreground approximation higher true positive rates can be achieved.
2D mapping of strongly deformed cell nuclei based on contour warping

The dynamics of genome regions are associated to the functional or dysfunctional behaviour of the human cell. In order to study these dynamics it is necessary to remove perturbations coming from movement and deformation of the nucleus, i.e. the container holding the genome. In literature models have been proposed to cope with the transformations corresponding to nuclear dynamics of healthy cells. However for pathological cells, the nucleus deforms in an apparently random way, making the use of such models a non trivial task. In this paper we propose a mapping of the cell nucleus which is based on the matching of the nuclear contours. The proposed method does not put constraints on the possible shapes nor on the possible deformations, making this method suited for the analysis of pathological nuclei.
Image scrambling without bandwidth expansion

Image-scrambling schemes are designed to render the image content unintelligible. Wyner has proposed an elegant one-dimensional (1-D) scrambling scheme without bandwidth expansion, making use of the discrete prolate spheroidal sequences (DPSS). The DPSS are optimal regarding their energy concentration in a given frequency subband. In this paper, we propose the two-dimensional (2-D) extension and application of this algorithm. We discuss new possibilities introduced by the 2-D approach. We also include experimental results.
Optimization of Packetization Masks for Image Coding Based on an Objective Cost Function for Desired Packet Spreading

In image communication over lossy packet networks (e.g., cell phone communication), packet loss errors lead to damaged images. Damaged images can be repaired with passive error concealment methods, which use neighboring coefficient or pixel values to estimate the missing ones. Neighboring image data should, thus, be spread over different packets. This paper presents a novel robust packetization method for the transmission of image content in lossy packet networks. We first define novel criteria for a good packetization. Based on these properties, we propose a cost function for packetization masks. We then use stochastic optimization to calculate optimal packetization masks. We test our packetization technique on both wavelet coding and DCT coding. Compared to other packetization techniques, we are able to achieve the same or better mean quality of the reconstructed images but with less fluctuation in quality, which is important for the viewer experience. In this way, we significantly increase the worst case quality, especially for high packet loss rates. This leads to visually more pleasing images in case of a passive reconstruction.
Convolutional neural network pruning to accelerate membrane segmentation in electron microscopy

Biological membranes are one of the most basic structures and regions of interest in cell biology. In the study of membranes, segment extraction is a well-known and difficult problem because of impeding noise, directional and thickness variability, etc. Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks. However, because of the massive amount of features that have to be extracted while propagating forward, the practical usability diminishes, even with state-of-the-art GPU's. A significant part of these network features typically contains redundancy through correlation and sparsity. In this work, we propose a pruning method for convolutional neural networks that ensures the training loss increase is minimized. We show that the pruned networks, after retraining, are more efficient in terms of time and memory, without significantly affecting the network accuracy. This way, we manage to obtain real-time membrane segmentation performance, for our specific electron microscopy setup.
Optimized feature fusion of LiDAR and hyperspectral data for tree species mapping in closed forest canopies

This study deals with data fusion of hyperspectral and LiDAR sensors for tree species mapping in complex, closed forest canopies in Belgium. In particular, seven tree species were mapped: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. The added value of LiDAR height profile data on tree species mapping was assessed. Sensor data were fused in the PCA domain, while optimal feature combination was derived from the best classification performance (in terms of Kappa and producer's accuracy) based on 5-fold cross-validation. Besides, varying training set sizes were tested (resp. 10%, 30% and 50% number of samples per tree species class). Feature fusion of PCA-transformed HS and LiDAR data was most effective for small sample set sizes reaching a Kappa accuracy improvement of 10.51%.
Robust non-linear filtering for video processing

Noise removal techniques, such as the K-nearest neighbour filter and the /spl alpha/-trimmed mean filter, are known to be very robust in still image noise removal, but they have not been exploited in video processing. We investigate their 3D-extension for use in video sequence noise removal. We also determine the optimal balance between temporal and spatial window size, the optimal values of the other parameters and finally we investigate the artefacts introduced by the filters. The results show that the new video K-nearest neighbour filter outperforms the video version of the /spl alpha/-trimmed mean and the state-of-the-art rational filter by G. Ramponi from both a PSNR and a visual quality point of view.
Depth-guided patch-based disocclusion filling for view synthesis via Markov random field modelling

In this paper, we propose a novel patch-based disocclusion filling method for view synthesis from video-plus-depth data. The proposed method treats disocclusion filling as a global optimization problem, where global (spatial) consistency among the patches is enforced via a Markov random field (MRF) model. The main idea of our method is to exploit disocclusion properties to limit and guide the search for candidate patches (labels) and to minimize efficiently the resulting MRF energy. In particular, we propose to constrain the label selection to local background regions in order to ensure that the disocclusions are filled with background information. Background is determined based on the locally estimated hard threshold on the depth values. The efficient minimization approach represents an extension of our previous method for general inpainting, where we propose to visit the MRF nodes from the background to the foreground disocclusion border and discard unnecessary labels. In this way, the number of labels is further reduced and the propagation of background information is additionally enforced. Finally, efficient inference is performed to obtain the final inpainting result. The proposed disocclusion filling method represents one step of the complete view synthesis framework that we also introduce in this paper. Experimental results show improvement of the proposed approach over related state-of-the-art methods both for small and big disocclusions.
A realistic volume conductor model of the neonatal head: Methods, challenges and applications

Developing a realistic volume conductor head model is an important step towards a non-invasive investigation of neuro-electrical activity in the brain. For adults, different volume conductor head models have been designed and successfully used for electroencephalography (EEG) source analysis. However, creating appropriate neonatal volume conductor head model for EEG source analysis is a challenging task mainly due to insufficient knowledge of head tissue conductivities and complex anatomy of the developing newborn brain. In this work, we present a pipeline for modeling a realistic volume conductor model of the neonatal head, where we address the modeling challenges and propose our solutions. We also discuss the use of our realistic volume conductor head model for neonatal EEG source analysis.
Centralized Collaborative Sparse Unmixing for Hyperspectral Images

Spectral unmixing is very important in hyperspectral image analysis and processing, which aims at identifying the constituent spectra (i.e., endmembers) and estimating their fractional abundances from the mixed pixels. In recent years, sparse unmixing has received considerable interest. However, the acquired hyperspectral images are generally degraded by the noise, making sparse unmixing not faithful enough. To address this issue, this paper proposes a novel framework to couple sparse hyperspectral unmixing and abundance estimation error reduction together. Specifically, with the definition of abundance estimation error, a centralized constraint is incorporated into the collaborative sparse unmixing framework by exploiting the nonlocal redundancy of abundance map. This way we suppress the abundance estimation error, and improve the unmixing accuracy. Meanwhile, the alternating direction method of multipliers is introduced to solve the underlying constrained model. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of our proposed algorithm.