nils-herrmann/clean_scirepeval huggingface dataset:
A Proof of Convergence of the MAP Turbo-Detector to the AWGN Case

In this paper, we consider a coded transmission over a frequency-selective channel. We propose to study analytically the convergence of the turbo-detector using a maximum a posteriori (MAP) equalizer and a MAP decoder. We show that the densities of the extrinsic log likelihood ratios (LLRs) exchanged during the iterations are e-symmetric and output-symmetric. Under the Gaussian approximation, this property allows to perform a one-dimensional (1-D) analysis of the turbo-detector. By deriving the analytical expressions of the extrinsic LLR distributions under the Gaussian approximation, we prove that the bit error rate (BER) performance of the turbo-detector converges to the BER performance of the coded additive white Gaussian noise (AWGN) channel at high signal to noise ratio (SNR), for any frequency-selective channel.
Operational Rate-Distortion Performance of Single-Source and Distributed Compressed Sensing

We consider correlated and distributed sources without cooperation at the encoder. For these sources, we derive the best achievable performance in the rate-distortion sense of any distributed compressed sensing scheme, under the constraint of high-rate quantization. Moreover, under this model we derive a closed-form expression of the rate gain achieved by taking into account the correlation of the sources at the receiver and a closed-form expression of the average performance of the oracle receiver for independent and joint reconstruction. Finally, we show experimentally that the exploitation of the correlation between the sources performs close to optimal and that the only penalty is due to the missing knowledge of the sparsity support as in (non distributed) compressed sensing. Even if the derivation is performed in the large system regime, where signal and system parameters tend to infinity, numerical results show that the equations match simulations for parameter values of practical interest.
Maximizing the spectral efficiency of coded CDMA under successive decoding

We investigate the spectral efficiency achievable by random synchronous code-division multiple access (CDMA) with quaternary phase-shift keying (QPSK) modulation and binary error-control codes, in the large system limit where the number of users, the spreading factor, and the code block length go to infinity. For given codes, we maximize spectral efficiency assuming a minimum mean-square error (MMSE) successive stripping decoder for the cases of equal rate and equal power users. In both cases, the maximization of spectral efficiency can be formulated as a linear program and admits a simple closed-form solution that can be readily interpreted in terms of power and rate control. We provide examples of the proposed optimization methods based on off-the-shelf low-density parity-check (LDPC) codes and we investigate by simulation the performance of practical systems with finite code block length.
Chromaticity diffusion

A novel approach for color image denoising is proposed. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. We present the underlying theory, a number of examples, and comparison with the current literature.
A variational model for disocclusion

In this paper we study a variational approach for filling-in regions of missing data in 2D and 3D digital images. Applications of this technique include the restoration of old photographs and removal of superimposed text like dates, subtitles, or publicity, or the zooming of images. The approach presented here, initially introduced in [C. Ballester et al., 2001], is based on a joint interpolation of the image gray-levels and gradient/isophotes directions, smoothly extending the isophote lines into the holes of missing data. The process underlying this approach can be considered as an interpretation of the Gestaltist's principle of good continuation. After discussing the model, we present the numerical algorithm used to minimize it and display some numerical experiments.
Stereoscopic image inpainting using scene geometry

In this paper we propose an algorithm for stereoscopic image inpainting, given the inpainting mask in both images. We also assume that depth map is known in one of the images of the stereo pair, taken as reference. This image is clustered in homogeneous color regions using a mean-shift procedure. In each clustered region, depths are fitted by planes and then extended into the mask. Then we inpaint the visible parts of each extended region using a modified exemplar-based inpainting algorithm. Finally, we extend the algorithm to stereoscopic image inpainting. We display some experiments showing the performance of the proposed algorithm.
An axiomatic approach to image interpolation

We discuss possible algorithms for interpolating data given in a set of curves and/or points in the plane. We propose a set of basic assumptions to be satisfied by the interpolation algorithms which lead to a set of models in terms of possibly degenerate elliptic partial differential equations. The absolute minimal Lipschitz extension model (AMLE) is singled out and studied in more detail. We show experiments suggesting a possible application, the restoration of images with poor dynamic range.
Junction detection and filtering: a morphological approach

We discuss the physical generation process of images as a combination of basic operations: occlusions, transparencies and contrast changes. These operations generate the essential singularities which we call junctions. We deduce a mathematical and computational model for image analysis according to which the "atoms" of the image must be "pieces of level lines joining junctions", fitting the phenomenological description of Gaetano Kanizsa (1990). A parameter free junction detection algorithm is proposed for the computation of the previously defined "atoms". Then we propose an adequate modification of the morphological filtering algorithms so that they smooth the "atoms" without altering the junctions. Finally, we give some experiments on real and synthetic images.
Relative depth from monocularoptical flow

We present a method to compute the relative depth of moving objects in video sequences. The method relies on the fact that the boundary between two moving objects follows the movement of the object which is closest to the camera. Thus, the input of the method is a segmentation (to know the boundaries of objects) and an optical flow (to know the movement of the objects). The output of the method is a relative ordering of the neighboring segments. In fact, this output only provides a cue of the desired relative ordering, just like T-junctions typically provide a cue of the relative ordering of the objects around them. These cues can be used later as heuristics or as starting points for higher-level algorithms for image and video-processing.
An axiomatic approach to image interpolation.

We discuss possible algorithms for interpolating data given in a set of curves and/or points in the plane. We propose a set of basic assumptions to be satisfied by the interpolation algorithms which lead to a set of models in terms of possibly degenerate elliptic partial differential equations. The absolute minimal Lipschitz extension model (AMLE) is singled out and studied in more detail. We show experiments suggesting a possible application, the restoration of images with poor dynamic range.
Exemplar-Based Image Inpainting Using Multiscale Graph Cuts

We present a novel formulation of exemplar-based inpainting as a global energy optimization problem, written in terms of the offset map. The proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole. This formulation is adapted to obtain a global minimum using the graph cuts algorithm. To reduce the computational complexity, we propose an efficient multiscale graph cuts algorithm. To compensate the loss of information at low resolution levels, we use a feature representation computed at the original image resolution. This permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels. Our experiments show how well the proposed algorithm performs compared with other recent algorithms.
Homography estimation using one ellipse correspondence and minimal additional information

In sport scenarios like football or basketball, we often deal with central views where only the central circle and some additional primitives like the central line and the central point or a touch line are visible. In this paper we first characterize, from a mathematical point of view, the set of homographies that project a given ellipse into the unit circle, next, using some extra minimal additional information like the knowledge of the position in the image of the central line and central point or a touch line we show a method to fully determine the plane homography. We present some experiments in sport scenarios to show the ability of the proposed method to properly recover the plane homography.
A Variational Model for Histogram Transfer of Color Images

In this paper, we propose a variational formulation for histogram transfer of two or more color images. We study an energy functional composed by three terms: one tends to approach the cumulative histograms of the transformed images, the other two tend to maintain the colors and geometry of the original images. By minimizing this energy, we obtain an algorithm that balances equalization and the conservation of features of the original images. As a result, they evolve while approaching an intermediate histogram between them. This intermediate histogram does not need to be specified in advance, but it is a natural result of the model. Finally, we provide experiments showing that the proposed method compares well with the state of the art.
Virtual Camera Synthesis for Soccer Game Replays

In this paper, we present a set of tools developed during the creation of a platform that allows the automatic generation of virtual views in a live soccer game production. Observing the scene through a multi-camera system, a 3D approximation of the players is computed and used for the synthesis of virtual views. The system is suitable both for static scenes, to create bullet time effects, and for video applications, where the virtual camera moves as the game plays.
Image Editing With Spatiograms Transfer

Histogram equalization is a well-known method for image contrast enhancement. Nevertheless, as histograms do not include any information on the spatial repartition of colors, their application to local image editing problems remains limited. To cope with this lack of spatial information, spatiograms have been recently proposed for tracking purposes. A spatiogram is an image descriptor that combines a histogram with the mean and the variance of the position of each color. In this paper, we address the problem of local retouching of images by proposing a variational method for spatiogram transfer. More precisely, a reference spatiogram is used to modify the color value of a given region of interest of the processed image. Experiments on shadow removal and inpainting demonstrate the strength of the proposed approach.
Spatial String Matching for Image Classification

This paper presents a spatial string matching method to incorporate spatial information into the bag-of-words model, which represents an image as an unordered distribution of local features. Spatial constraints among neighboring features are explored in order to achieve better discrimination power for image classification. The features from neighboring points are combined together and taken as a spatial string, and then our method matches the images according to the similarity of string pairs. The categorization problem can be formulated using KNN or SVM classifier based on the spatial string matching kernel. The proposed method is able to capture spatial dependencies across the neighboring features. Experiment results show promising performance for image classification tasks.
Stereoscopic Image Inpainting: Distinct Depth Maps and Images Inpainting

In this paper we propose an algorithm for in painting of stereo images. The issue is to reconstruct the holes in a pair of stereo image as if they were the projection of a 3D scene. Hence, the reconstruction of the missing information has to produce a consistent visual perception of depth. Thus, first step of the algorithm consists in the computation and in painting of disparity maps in the given holes. The second step of the algorithm is to fill-in missing regions using the complete disparity maps in a way that avoids the creation of 3D artifacts. We present some experiments on several pairs of stereo images.
Geometric models for active contours

A geometric formulation of active contours for 2D, 3D boundary detection and motion tracking is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of minimal distance curves or minimal surfaces in a Riemannian space whose metric is derived from the image. Previous models of geometric active contours are improved allowing stable boundary detection when their gradients suffer from large variations, including gaps. Numerical experiments are also presented.
A gradient based neighborhood filter for disparity interpolation

In this work we propose a non-local gradient-based energy for interpolating incomplete disparity maps. It represents an extension of the bilateral filter adapted to reconstruct locally planar disparity maps. We assume that we have at our disposal a reference image from which similarity weights can be computed. When the spatial extend of the weights tends to zero, the proposed model can be shown to converge to an energy involving second order derivatives, explaining thus its ability to obtain higher order interpolations. The proposed energy can be minimized by solving its Euler-Lagrange equation via an iteration of second order Poisson equations. By including an edge map our model permits also to recover depth discontinuities.
Unsupervised Motion Layer Segmentation by Random Sampling and Energy Minimization

In this paper we introduce an unsupervised scheme for the segmentation of motion layers in video sequences. The number of layers is automatically determined by the method. Our approach first extracts the motion models thanks to a RANSAC-based random sampling algorithm improved by the use of geodesic distance information. Then those models are assigned to pixels in the color image by minimizing an energy functional thanks to graph-cut. Our energy takes into account motion residuals, color distributions, geodesic distance as well as temporal consistency of the layers. Moreover, we define a smoothness term that enforces a patch-wise spatial coherency on areas where optical flow is reliable and a pixel-wise coherency on occluded areas. The method leads to promising results on the tested sequences.
Polyconvexification of the multi-label optical flow problem

In this paper the problem of optical flow and occlusion mask estimation is aborded. To that end, we consider a multi-label representation of the optical flow and we define an energy that models the problem. The convexification of the energy and its minimization with an iterative algorithm are studied. Our algorithm is implemented in GPU, since each pixel can be processed in parallel. From our experiments, the relation between the quality of the results obtained and computing time seems to be very promising.
Skin-based privacy filter for surveillance systems

With surveillance monitoring becoming widely available with the emergence of high resolution cameras, privacy concerns have been raised. One way to achieve privacy protection is by employing a privacy preserving device that can protect persons' identity, but still provide sufficient information to detect anomalous events if necessary. The skin region is important as a Personally Identifying Information (PII) that needs to be obscured if privacy is to be protected. Thus, this paper presents skin detection techniques to conceal privacy sensitive information. However, state-of-the-art skin detection methods suffer from various problems when deployed in a surveillance system. Accordingly, we propose a solution whereby we first find a set of candidate Regions-of-Interest (RoI) and then apply skin detection to the RoI thus found so as to locate the skin. By using this strategy, skin regions can be well delineated to allow targeted privacy filtering without covering the non-RoI parts.
A fuzzy qualitative approach for scene classification

Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem. That is, we first adopted the fuzzy quantity space to model the training data. Secondly, we present a novel weight function, w to train a fuzzy qualitative scene model in the fuzzy qualitative states. Finally, we introduce fuzzy qualitative partition to perform the scene classification. Empirical results using a standard dataset and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method.
A novel technique for solving fuzzy differential equations of fractional order using Laplace and integral transforms

In this paper, we propose a novel approach for the numerical solution of fuzzy fractional differential equations (FFDEs) under fuzzy Caputo-type derivative. More specifically, we first obtain the equivalent integral form of original problem, then the fractional integral equation is approximated using Laplace transforms. Afterwards, we can get the solution by employing any numerical method. Indeed, the proposed approach introduces an efficient and practical way to solve a wide range of fractional models under uncertainty. The most important advantage of this procedure is that the complexity of dealing with the fractional derivative is removed from the calculations, which can reduce the computational costs, considerably. Illustrative examples address the validity and appropriateness of this technique.
Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

Classification of plants based on a multi-organ approach is very challenging. Although additional data provide more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Despite promising solutions built using deep learning enable representative features to be learned for plant images, the existing approaches focus mainly on generic features for species classification, disregarding the features representing plant organs. In fact, plants are complex living organisms sustained by a number of organ systems. In our approach, we introduce a hybrid generic-organ convolutional neural network (HGO-CNN), which takes into account both organ and generic information, combining them using a new feature fusion scheme for species classification. Next, instead of using a CNN-based method to operate on one image with a single organ, we extend our approach. We propose a new framework for plant structural learning using the recurrent neural network-based method. This novel approach supports classification based on a varying number of plant views, capturing one or more organs of a plant, by optimizing the contextual dependencies between them. We also present the qualitative results of our proposed models based on feature visualization techniques and show that the outcomes of visualizations depict our hypothesis and expectation. Finally, we show that by leveraging and combining the aforementioned techniques, our best network outperforms the state of the art on the PlantClef2015 benchmark. The source code and models are available at https://github.com/cs-chan/Deep-Plant.
pLSA-based zero-shot learning

Current zero-shot learning methods relied on attributes to describe the unseen class characteristics, using the learned seen class model. However, these approaches required extensive attribute labels on each object class, and a well-defined, attributes relationship between the seen and unseen class with the aid of human knowledge. In this work, we avoid these with a novel learning process using the probabilistic Latent Semantic Analysis (pLSA). We replace the attributes with topic model and extend the representation as a mapping algorithm to object classes, so that zero-shot learning would be possible. With this, less annotated class information is required to achieve similar performance. Evaluations on three public datasets had shown the effectiveness of our proposed method.
A Scene Image is Nonmutually Exclusive—A Fuzzy Qualitative Scene Understanding

Ambiguity or uncertainty is a pervasive element of many real-world decision-making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research has proven that decision making by humans is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming that scene images are mutually exclusive; therefore, they focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are nonmutually exclusive and propose the fuzzy qualitative rank classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in terms of qualitative and quantitative measurements using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the nonmutually exclusive scene images.