nils-herrmann/clean_scirepeval huggingface dataset:
Unifying probabilistic and variational estimation

A maximum a posteriori (MAP) estimator using a Markov or a maximum entropy random field model for a prior distribution may be viewed as a minimizer of a variational problem.Using notions from robust statistics, a variational filter referred to as a Huber gradient descent flow is proposed. It is a result of optimizing a Huber functional subject to some noise constraints and takes a hybrid form of a total variation diffusion for large gradient magnitudes and of a linear diffusion for small gradient magnitudes. Using the gained insight, and as a further extension, we propose an information-theoretic gradient descent flow which is a result of minimizing a functional that is a hybrid between a negentropy variational integral and a total variation. Illustrating examples demonstrate a much improved performance of the approach in the presence of Gaussian and heavy tailed noise. In this article, we present a variational approach to MAP estimation with a more qualitative and tutorial emphasis. The key idea behind this approach is to use geometric insight in helping construct regularizing functionals and avoiding a subjective choice of a prior in MAP estimation. Using tools from robust statistics and information theory, we show that we can extend this strategy and develop two gradient descent flows for image denoising with a demonstrated performance.
Turkish lira banknotes classification using deep convolutional neural networks

While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.
A RNN based time series approach for forecasting turkish electricity load

RNN, LSTM and GRU variations have been increasing its popularity on time-series applications. Liberalization of Turkish Electricity Market empowers the necessity of better electricity consumption prediction systems. This paper presents a Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) based time series forecasting experiments on Turkish electricity load prediction. Resulting %0.71 MAPE success of our experiments yields better results than existing researches based on ARIMA and artificial neural networks on Turkish electricity load forecasting which have %2.6 and %1.8 success rate respectively.
A new method for characterization of coronary plaque composition via IVUS images

IVUS-derived virtual histology (VH) permits the assessment of atherosclerotic plaque morphology by using radiofrequency analysis of ultrasound signals. However, it requires the acquisition to be ECG-gated, which is a major limitation of VH. Indeed, its computation can only be performed once per cardiac cycle, which significantly decreases the longitudinal resolution of VH. To overcome this limitation, the introduction of an image-based plaque characterization is of great importance. Current IVUS image processing techniques do not allow adequate identification of the coronary artery plaques. This can be improved by defining appropriate features for the different kinds of plaques. In this paper, a novel feature extraction method based on Run-length algorithm is presented and used for improving the automated characterization of the plaques within the IVUS images. The proposed feature extraction method is applied to 200 IVUS images obtained from five patients. As a result an accuracy rate of 77% was achieved. Comparing this to the accuracy rates of 75% and 71% obtained using co-occurrence and local binary pattern methods respectively indicates the superior performance of the proposed feature extraction method.
Shape deformation measures for white matter fibers

Diffusion MRI(DMRI) provides us with the anatomy of the white matter fibers in the brain. Extracting shape information is a necessary step to analyze the deformation in the white matter fibers of a patient before and after brain surgery. We propose various measures with different invariance properties to explain deformations of different characteristics. The measures are presented for corticospinal tract and medial lemniscus fibers on a clinical dataset.
Graph cuts segmentation using an elliptical shape prior

We present a graph cuts-based image segmentation technique that incorporates an elliptical shape prior. Inclusion of this shape constraint restricts the solution space of the segmentation result, increasing robustness to misleading information that results from noise, weak boundaries, and clutter. We argue that combining a shape prior with a graph cuts method suggests an iterative approach that updates an intermediate result to the desired solution. We first present the details of our method and then demonstrate its effectiveness in segmenting vessels and lymph nodes from pelvic magnetic resonance images, as well as human faces.
Robust Scale-Space Filter Using Second-Order Partial Differential Equations

This paper describes a robust scale-space filter that adaptively changes the amount of flux according to the local topology of the neighborhood. In a manner similar to modeling heat or temperature flow in physics, the robust scale-space filter is derived by coupling Fick's law with a generalized continuity equation in which the source or sink is modeled via a specific heat capacity. The filter plays an essential part in two aspects. First, an evolution step size is adaptively scaled according to the local structure, enabling the proposed filter to be numerically stable. Second, the influence of outliers is reduced by adaptively compensating for the incoming flux. We show that classical diffusion methods represent special cases of the proposed filter. By analyzing the stability condition of the proposed filter, we also verify that its evolution step size in an explicit scheme is larger than that of the diffusion methods. The proposed filter also satisfies the maximum principle in the same manner as the diffusion. Our experimental results show that the proposed filter is less sensitive to the evolution step size, as well as more robust to various outliers, such as Gaussian noise, impulsive noise, or a combination of the two.
Depth Analogy: Data-Driven Approach for Single Image Depth Estimation Using Gradient Samples

Inferring scene depth from a single monocular image is a highly ill-posed problem in computer vision. This paper presents a new gradient-domain approach, called depth analogy, that makes use of analogy as a means for synthesizing a target depth field, when a collection of RGB-D image pairs is given as training data. Specifically, the proposed method employs a non-parametric learning process that creates an analogous depth field by sampling reliable depth gradients using visual correspondence established on training image pairs. Unlike existing data-driven approaches that directly select depth values from training data, our framework transfers depth gradients as reconstruction cues, which are then integrated by the Poisson reconstruction. The performance of most conventional approaches relies heavily on the training RGB-D data used in the process, and such a dependency severely degenerates the quality of reconstructed depth maps when the desired depth distribution of an input image is quite different from that of the training data, e.g., outdoor versus indoor scenes. Our key observation is that using depth gradients in the reconstruction is less sensitive to scene characteristics, providing better cues for depth recovery. Thus, our gradient-domain approach can support a great variety of training range datasets that involve substantial appearance and geometric variations. The experimental results demonstrate that our (depth) gradient-domain approach outperforms existing data-driven approaches directly working on depth domain, even when only uncorrelated training datasets are available.
Cost Aggregation and Occlusion Handling With WLS in Stereo Matching

This paper presents a novel method for cost aggregation and occlusion handling for stereo matching. In order to estimate optimal cost, given a per-pixel difference image as observed data, we define an energy function and solve the minimization problem by solving the iterative equation with the numerical method. We improve performance and increase the convergence rate by using several acceleration techniques such as the Gauss-Seidel method, the multiscale approach, and adaptive interpolation. The proposed method is computationally efficient since it does not use color segmentation or any global optimization techniques. For occlusion handling, which has not been performed effectively by any conventional cost aggregation approaches, we combine the occlusion problem with the proposed minimization scheme. Asymmetric information is used so that few additional computational loads are necessary. Experimental results show that performance is comparable to that of many state-of-the-art methods. The proposed method is in fact the most successful among all cost aggregation methods based on standard stereo test beds.
SPM-BP: Sped-Up PatchMatch Belief Propagation for Continuous MRFs

Markov random fields are widely used to model many computer vision problems that can be cast in an energy minimization framework composed of unary and pairwise potentials. While computationally tractable discrete optimizers such as Graph Cuts and belief propagation (BP) exist for multi-label discrete problems, they still face prohibitively high computational challenges when the labels reside in a huge or very densely sampled space. Integrating key ideas from PatchMatch of effective particle propagation and resampling, PatchMatch belief propagation (PMBP) has been demonstrated to have good performance in addressing continuous labeling problems and runs orders of magnitude faster than Particle BP (PBP). However, the quality of the PMBP solution is tightly coupled with the local window size, over which the raw data cost is aggregated to mitigate ambiguity in the data constraint. This dependency heavily influences the overall complexity, increasing linearly with the window size. This paper proposes a novel algorithm called sped-up PMBP (SPM-BP) to tackle this critical computational bottleneck and speeds up PMBP by 50-100 times. The crux of SPM-BP is on unifying efficient filter-based cost aggregation and message passing with PatchMatch-based particle generation in a highly effective way. Though simple in its formulation, SPM-BP achieves superior performance for sub-pixel accurate stereo and optical-flow on benchmark datasets when compared with more complex and task-specific approaches.
Disparity search range estimation: Enforcing temporal consistency

This paper presents a new approach for estimating the disparity search range in stereo video that enforces temporal consistency. Reliable search range estimation is very important since an incorrect estimate causes most stereo matching methods to get trapped in local minima or produce unstable results over time. In this work, the search range is estimated based on a disparity histogram that is generated with sparse feature matching algorithms such as SURF. To achieve more stable results over time, we further propose to enforce temporal consistency by calculating a weighted sum of temporally-neighboring histograms, where the weights are determined by the similarity of depth distribution between frames. Experimental results show that this proposed method yields accurate disparity search ranges for several challenging stereo videos and is robust to various forms of noise, scene complexity and camera configurations.
Cost aggregation with anisotropic diffusion in feature space for hybrid stereo matching

In this paper, we present a cost aggregation using anisotropic diffusion on a feature space for hybrid stereo matching. Stereo matching can be classified into two categories: feature-based and area-based approaches. Feature-based approaches generate accurate but sparse disparity maps. On the other hand, area-based approaches generate dense but unreliable disparity maps, especially at depth discontinuities and homogeneous regions. We hence propose a stereo matching algorithm having advantages of both approaches. We study how to design a correspondence algorithm without modeling any depth cues except disparity. A procedure of depth perception is modeled via anisotropic diffusion on the feature space in terms of coherence. Based on the assumption that similar local feature space has similar disparity, we define the feature space and its similarity and then introduce feature confidences into the proposed model. Experimental results show that the performance of the proposed method is comparable to that of the state-of-the-art methods.
Gaze correction for 3D tele-immersive communication system

The lack of eye contact between participants in a tele-conferencing makes nonverbal communication unnatural and ineffective. A lot of research has focused on correcting the user gaze for a natural communication. Most of prior solutions require expensive and bulky hardware, or incorporate a complicated algorithm causing inefficiency and deployment. In this paper, we propose an effective and efficient gaze correction solution for a 3D tele-conferencing system in a single color/depth camera set-up. A raw depth map is first refined using the corresponding color image. Then, both color and depth data of the participant are accurately segmented. A novel view is synthesized in the location of the display screen which coincides with the user gaze. Stereoscopic views, i.e. virtual left and right images, can also be generated for 3D immersive conferencing, and are displayed in a 3D monitor with 3D virtual background scenes. Finally, to handle large hole regions that often occur in the view synthesized with a single color camera, we propose a simple yet robust hole filling technique that works in real-time. This novel inpainting method can effectively reconstruct missing parts of the synthesized image under various challenging situations. Our proposed system works in real-time on a single core CPU without requiring dedicated hardware, including data acquisition, post-processing, rendering, and so on.
Stereoscopic image quality compendium

Stereoscopic viewing of 3D content brings with it a variety of complex perceptual quality issues. For the percept of depth to be convincing, consistent, and comfortable, many parameters throughout the imaging pipeline must to be matched correctly. However, tradeoffs are inevitable for practical reasons, which may lead to various distortions. This paper reviews the issues from an engineering perspective, addressing in particular depth issues, multiview issues, display issues, viewer-specific issues, as well as possible measurements for the overall 3D quality of experience (QoE).
Spatial and temporal up-conversion technique for depth video

This paper proposes a novel framework for up-conversion of depth video resolution both in spatial and in time domain. Time-of-Flight (TOF) sensors are widely used in computer vision fields. Although TOF sensors provide depth video in real time, there are some problems in a sense that it provides a low resolution and a low frame-rate depth video. We propose a cheaper solution that enhances depth video obtained by TOF sensor by combining it with CCD camera. The proposed method provides high quality video as a cheaper solution for low resolution, and low frame-rate depth video. It is useful when depth video is used in various applications such as 3DTV, free-view TV, teleconference system. High-quality depth video can be obtained by motion compensated frame interpolation (MCFI) and extended Joint Bilateral Upsampling (JBU). Experimental results show that depth video obtained by the proposed method has satisfactory quality.
3D Scene Reconstruction System with Hand-Held Stereo Cameras

3D scene modeling is a challenging problem and has been one of the most important research topic for many years. In this paper, we describe the 3D scene reconstruction system that creates 3D models with multiple stereo image pairs acquired by hand-held device. Our algorithm consists of the following two steps, which is depth reconstruction and model registration. In the first part, we obtain the depth map with stereo matching and camera geometry in each view. The algorithm is based on adaptive window methods in hierarchical frameworks. In the second part, we use SIFT feature to estimate the camera motion. LMedS algorithm reduces the effect of outliers in this process. Experimental results show that the proposed algorithm provides accurate disparity map in various types of images, and the 3D model of real world's scene.
Stereoscopic Video Generation Method using Motion Analysis

Stereoscopic video generation method can produce stereoscopic contents from conventional video filmed from monoscopic camera. We propose stereoscopic video generation method using motion-to-disparity conversion considering multiuser condition and characteristics of display device. Field of view, maximum and minimum values of disparity are calculated through initialization process in order to apply various types of 3D display. After motion estimation, we propose three cues to decide the scale factor of motion-to-disparity conversion which are magnitude of motion, camera movement and scene complexity. Subjective evaluation is performed by comparing videos captured from stereoscopic camera and generated from one view of stereoscopic video.
Fast 2D Complex Gabor Filter With Kernel Decomposition

2D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2D complex Gabor filter bank consisting of filtering outputs at multiple orientations and frequencies. Although several approaches for fast Gabor filtering have been proposed, they focus primarily on reducing the runtime for performing filtering once at specific orientation and frequency. To obtain the Gabor filter bank, the existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2D complex Gabor filter bank by reducing the computational redundancy that arises when performing filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor kernel to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the Gabor filter bank by reusing intermediate results computed at a specific orientation. By extending this idea, we also propose a fast approach for 2D localized sliding discrete Fourier transform that uses the Gaussian kernel in order to lend spatial localization ability as in the Gabor filter. Experimental results demonstrate that the proposed method runs faster than the state-of-the-art methods, while maintaining similar filtering quality.
A Stereoscopic Video Generation Method Using Stereoscopic Display Characterization and Motion Analysis

Stereoscopic video generation methods can produce stereoscopic content from conventional video filmed with monoscopic cameras. In this paper, we propose a stereoscopic video generation method using motion analysis which converts motion into disparity values and considers multi-user conditions and the characteristics of the display device. The field of view and the maximum and minimum disparity values were calculated in the stereoscopic display characterization stage and were then applied to various types of 3D displays. After motion estimation, we used three cues to decide the scale factor of motion-to-disparity conversion. These cues were the magnitude of motion, camera movements and scene complexity. A subjective evaluation showed that the proposed method generated more satisfactory video sequence.
Stereo matching with asymmetric occlusion handling in weighted least square framework

This paper presents a novel method for stereo matching with occlusion handling. In order to estimate optimal cost, we define an energy function and solve the iterative equation with the numerical method. We improve performance and convergence rate by using several acceleration techniques. The proposed method is computationally efficient since it does not use color segmentation or any global optimization techniques. For occlusion handling, which has not been performed effectively by any conventional cost aggregation approaches, we combine the occlusion problem with the proposed minimization scheme. Asymmetric information is used so that few additional computational loads are necessary. Experimental results show that performance is comparable to that of many state-of-the-art methods.
Fast Domain Decomposition for Global Image Smoothing

Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and regularization terms. At the price of high-computational cost, this global EPS approach is more robust and versatile than a local one that typically has a form of weighted averaging. In this paper, we introduce an efficient decomposition-based method for global EPS that minimizes the objective function of  ${L_{2}}$  data and (possibly non-smooth and non-convex) regularization terms in linear time. Different from previous decomposition-based methods, which require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1-D sub-problems. This enables applying fast linear time solver for weighted-least squares and - $L_{1}$  smoothing problems. An alternating direction method of multipliers algorithm is adopted to guarantee fast convergence. Our method is fully parallelizable, and its runtime is even comparable to the state-of-the-art local EPS approaches. We also propose a family of fast majorization–minimization algorithms that minimize an objective with non-convex regularization terms. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications.
Randomized texture flow estimation using visual similarity

Exploring underlying texture flows defined with orientation and scale is of a great interest on a variety of vision-related tasks. However, existing methods often fail to capture accurate flows due to over-parameterization of texture deformation or employ a costly global optimization which makes the algorithm computationally demanding. In this paper, we address this inverse problem by casting it as a randomized correspondence search along with a locally-adaptive vector field smoothing. When a small example patch is given as a reference, a randomized deformable matching is performed on the very densely quantized label space, enabling an efficient estimation of texture deformation without quality degeneration, e.g., due to quantization artifacts which often appear in the optimization-driven discrete approaches. The visual similarity with respect to the deformation parameters is directly measured with an input texture image on an appearance space. The locally-adaptive smoothing is then applied to the intermediate flow field, resulting in a good continuation of the resultant texture flow. Experimental results on both synthetic and natural images show that the proposed method improves the performance in terms of both runtime efficiency and/or visual quality, compared to the existing methods.