nils-herrmann/clean_scirepeval huggingface dataset:
Sign language recognition using real-sense

Sign Language Recognition (SLR) targets on facilitating the communication between deaf-mute people and ordinary people. This task is very challenging due to the complexity and large variations in hand postures. Some methods require user wear sensor gloves which can detect the position and angle of finger articulations. Others use RGB-D camera like Kinect to track hands and rely on complex algorithms to segment hands from background. However, all these methods have its own disadvantages. Sensor-based methods are not natural as the user must wear cumbersome instruments while camera-based methods have to design extra algorithms to track and segment hands from complex background. To address these problems, we propose a novel method for SLR which involves the use of the Real-Sense. It is a camera device which can detect and track the location of hands in a natural way. More powerful, it provides the 3D coordinates of finger joints in real time. We build a deep neural network (DNN) based on Real-Sense to recognize different signs. The DNN takes the 3D coordinates of finger joints as input directly without using any handcrafted features. The reason is that DNN, as a deep model, is capable of learning suitable features for recognition from raw data. In experiment, to demonstrate the effectiveness of Real-Sense, we collect two datasets by Real-Sense and Kinect respectively, then build DNNs based on each dataset for recognition. To validate the powerfulness of DNN, we compare the performance of DNN and support vector machine (SVM) on the same dataset.
Subpixel Image Quality Assessment Syncretizing Local Subpixel and Global Pixel Features.

The subpixel rendering technology increases the apparent resolution of an LCD/OLED screen by exploiting the physical property that a pixel is composed of RGB individually addressable subpixels. Due to the intrinsic intercoordination between apparent luminance resolution and color fringing artifact, a common method of subpixel image assessment is subjective evaluation. In this paper, we propose a unified subpixel image quality assessment metric called subpixel image assessment (SPA), which syncretizes local subpixel and global pixel features. Specifically, comprehensive subjective studies are conducted to acquire data of user preferences. Accordingly, a collection of low-level features is designed under extensive perceptual validation, capturing subpixel and pixel features, which reflect local details and global distance from the original image. With the features and their measurements as the basis, the SPA is obtained, which leads to a good representation of the subpixel image characteristics. The experimental results justify the effectiveness and the superiority of the SPA. The SPA is also successfully adopted in a variety of applications, including content adaptive sampling and metric-guided image compression.
Source-channel secrecy for Shannon cipher system

Recently, a secrecy measure based on list-reconstruction has been proposed [1], in which a wiretapper is allowed to produce a list of 2mRL reconstruction sequences and secrecy is measured by the minimum distortion over the entire list. In this paper, we show that this list secrecy problem is equivalent to one with secrecy measured by a new quantity lossy-equivocation, which is proven to be the minimum optimistic 1-achievable source coding rate of the source with the wirtapped signal as two-sided information, and also can be seen as an extension of conventional equivocation to lossy case. Upon this (or list) secrecy measure, we study source-channel secrecy problem in the discrete memoryless Shannon cipher system with noisy wiretap channel. Two inner bounds and an outer bound on the achievable region of secret key rate, list rate, wiretapper distortion, and distortion of legitimate user are given. The inner bounds are derived by using an uncoded scheme and a (operationly) separate scheme, respectively. Thanks to the equivalence between lossy-equivocation secrecy and list secrecy, the information spectrum method is leveraged to prove the outer bound. As special cases, the admissible region for the cases of degraded wiretap channel or lossless communication for legitimate user has been characterized completely. For both these two cases, separate scheme is proven to be optimal. Interestingly, however, separation indeed suffers performance loss for other certain cases. Besides, we also extend our results to characterize the achievable region for Gaussian communication case. As a side product optimistic lossy source coding has also been addressed.
No-reference image quality assessment based on global and local content perception

Existing no-reference image quality assessment (NR-IQA) methods mainly focus on designing the low-level features related to image degradation. However, the evaluation of image quality is the human visual perception of image content, involving the integrated analysis of global high-level semantics and local low-level characteristics. From this perspective, we propose a NR-IQA framework based on global and local content perception. We adopt the deep convolutional neural network (DCNN) to extract the semantic feature implied in global image content. The perception of visual quality associated with local content utilizes the visual attention and filtering mechanisms of human visual system. The overall image quality is estimated by combining the semantic and local characteristic features generated from the perceptions. Experimental results on the LIVE IQA database demonstrate that our method is superior to the state-of-the-art NR-IQA algorithms and competitive to the popular full-reference IQA methods. Further experiments on the TID2008 dataset show that the proposed approach is robust for various kinds of distortion types.
Video error concealment via total variation regularized matrix completion

In this paper, we propose a novel video error concealment method to restore the visual degradation, caused by packet loss in video delivery over unreliable channels. For a video sequence, we exploit its inherent temporal-spatially correlated property, i.e., temporal continuity and spatial smoothness, from a global view point. We then formulate it into a total variation regularized matrix completion model. Compared with the error concealment methods implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.
Noise reduction for hyperspectral images based on structural sparse and low-rank matrix decomposition

In this paper, a noise reduction approach for hyperspectral images (HSIs) is presented. Due to the assorted noise sources of HSIs, it seems difficult to describe the noise in a concise manner. Commonly, noise reduction algorithms are dedicated to a certain kind of noise, such as random or striping noise. Most of them in addition have somewhat idealized hypotheses. For example, the random noise is white or signal-independent, or the observed scene is spatially homogeneous or quasi-homogeneous. Thus a practically efficient and universal denoising method is preferred. Thanks to the low-rank characteristic of HSI signal, and the structural sparsity of HSI noise, we draw inspiration from low-rank matrix decomposition and the emerging mixed norm, to propose a method dealing with various patterns of noise simultaneously. Both simulated and real data experiments show the effectiveness of the proposed approach.
Detection of Blotch and Scratch in Video Based on Video Decomposition

In old video restoration, automatic detection of common defects, e.g., scratches and blotches, has always been emphasized. While prior thoughts mainly focus on detecting blotches and linear, vertical scratches separately, this paper contributes to a more generalized and challenging issue: simultaneous detection of blotches and complex scratches in video, with much less knowledge of them. We investigate the characteristics of blotches and scratches in space and time domain, and propose a novel detection method based on two main steps: cartoon-texture decomposition in the space domain and content-defect separation in the time domain. We then formulate it into convex optimization problems and develop corresponding algorithms. The experiment results demonstrate that the proposed method is of high detection accuracy, verifying the effectiveness of our detection via a video decomposition method.
Error Resilient Mode Decision in Scalable Video Coding

Error resilient macroblock mode decision has been extensively investigated in the literature for single-layer video coding, for which error resilient mode decision is also called as intra refresh. In this paper, we present a loss-aware rate-distortion optimized macroblock mode decision algorithm for scalable video coding, wherein more macroblock coding modes than intra and inter are involved. Thanks to the good performance, the proposed method has been adopted into the joint scalable video model by the joint video team.
Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation

In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT. The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256*256*144 image).
Feature Based Nonrigid Brain MR Image Registration With Symmetric Alpha Stable Filters

A new feature based nonrigid image registration method for magnetic resonance (MR) brain images is presented in this paper. Each image voxel is represented by a rotation invariant feature vector, which is computed by passing the input image volumes through a new bank of symmetric alpha stable (S?S) filters. There are three main contributions presented in this paper. First, this work is motivated by the fact that the frequency spectrums of the brain MR images often exhibit non-Gaussian heavy-tail behavior which cannot be satisfactorily modeled by the conventional Gabor filters. To this end, we propose the use of S?S filters to model such behavior and show that the Gabor filter is a special case of the S?S filter. Second, the maximum response orientation (MRO) selection criterion is designed to extract rotation invariant features for registration tasks. The MRO selection criterion also significantly reduces the number of dimensions of feature vectors and therefore lowers the computation time. Third, in case the segmentations of the input image volumes are available, the Fisher's separation criterion (FSC) is introduced such that the discriminating power of different feature types can be directly compared with each other before performing the registration process. Using FSC, weights can also be assigned automatically to different voxels in the brain MR images. The weight of each voxel determined by FSC reflects how distinctive and salient the voxel is. Using the most distinctive and salient voxels at the initial stage to drive the registration can reduce the risk of being trapped in the local optimum during image registration process. The larger the weight, the more important the voxel. With the extracted feature vectors and the associated weights, the proposed method registers the source and the target images in a hierarchical multiresolution manner. The proposed method has been intensively evaluated on both simulated and real 3-D datasets obtained from BrainWeb and Internet Brain Segmentation Repository (IBSR), respectively, and compared with HAMMER, an extended version of HAMMER based on local histograms (LHF), FFD, Demons, and the Gabor filter based registration method. It is shown that the proposed method achieves the highest registration accuracy among the five widely used image registration methods.
Efficient Implementation for Spherical Flux Computation and Its Application to Vascular Segmentation

Spherical flux is the flux inside a spherical region, and it is very useful in the analysis of tubular structures in magnetic resonance angiography and computed tomographic angiography. The conventional approach is to estimate the spherical flux in the spatial domain. Its running time depends on the sphere radius quadratically, which leads to very slow spherical flux computation when the sphere size is large. This paper proposes a more efficient implementation for spherical flux computation in the Fourier domain. Our implementation is based on the reformulation of the spherical flux calculation using the divergence theorem, spherical step function, and the convolution operation. With this reformulation, most of the calculations are performed in the Fourier domain. We show how to select the frequency subband so that the computation accuracy can be maintained. It is experimentally demonstrated that, using the synthetic and clinical phase contrast magnetic resonance angiographic volumes, our implementation is more computationally efficient than the conventional spatial implementation. The accuracies of our implementation and that of the conventional spatial implementation are comparable. Finally, the proposed implementation can definitely benefit the computation of the multiscale spherical flux with a set of radii because, unlike the conventional spatial implementation, the time complexity of the proposed implementation does not depend on the sphere radius.
Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns

In this paper, we propose a new feature extraction method, which is robust against rotation and histogram equalization for texture classification. To this end, we introduce the concept of advanced local binary patterns (ALBP), which reflects the local dominant structural characteristics of different kinds of textures. In addition, to extract the global spatial distribution feature of the ALBP patterns, we incooperate ALBP with the aura matrix measure as the second layer to analyze texture images. The proposed method has three novel contributions, (a) The proposed ALBP approach captures the most essential local structure characteristics of texture images (i.e. edges, corners); (b) the proposed method extracts global information by using Aura matrix measure based on the spatial distribution information of the dominant patterns produced by ALBP; and (c) the proposed method is robust to rotation and histogram equalization. The proposed approach has been compared with other widely used texture classification techniques and evaluated by applying classification tests to randomly rotated and histogram equalized images in two different texture databases: Brodatz and CUReT. The experimental results show that the classification accuracy of the proposed method exceeds the ones obtained by other image features.
Non-rigid image registration by using graph-cuts with mutual information

Non-rigid image registration plays an important role in medical image analysis. Recently, Tang and Chung proposed to model the non-rigid medical image registration problem as an energy minimization framework. The optimization was done by using the graph-cuts algorithm via alpha-expansions. However, the dissimilarity measure used in the energy function of this graph-cuts based method was restricted to the sum of absolute differences (SAD) and the sum of squared differences (SSD). In this paper, to utilize an advanced dissimilarity measure, such as mutual information (MI), we adopt an approximation of MI to the graph-cuts based method. Exploiting the mutual information is valuable as it can capture complex statistical relationships between the intensities of the image pair without a priori knowledge of those relationships. We have compared the proposed method against the original graph-cuts based methods, and two state-of-the-art approaches. The experimental results demonstrate that the proposed method can achieve lower registration errors.
Vascular segmentation of phase contrast magnetic resonance angiograms based on statistical mixture modeling and local phase coherence

In this paper, we present an approach to segmenting the brain vasculature in phase contrast magnetic resonance angiography (PC-MRA). According to our prior work, we can describe the overall probability density function of a PC-MRA speed image as either a Maxwell-uniform (MU) or Maxwell-Gaussian-uniform (MGU) mixture model. An automatic mechanism based on Kullback-Leibler divergence is proposed for selecting between the MGU and MU models given a speed image volume. A coherence measure, namely local phase coherence (LPC), which incorporates information about the spatial relationships between neighboring flow vectors, is defined and shown to be more robust to noise than previously described coherence measures. A statistical measure from the speed images and the LPC measure from the phase images are combined in a probabilistic framework, based on the maximum a posteriori method and Markov random fields, to estimate the posterior probabilities of vessel and background for classification. It is shown that segmentation based on both measures gives a more accurate segmentation than using either speed or flow coherence information alone. The proposed method is tested on synthetic, flow phantom and clinical datasets. The results show that the method can segment normal vessels and vascular regions with relatively low flow rate and low signal-to-noise ratio, e.g., aneurysms and veins.
Learning-based non-rigid image registration using prior joint intensity distributions with graph-cuts

Non-rigid image registration is widely used in medical image analysis and processing. We recently proposed a novel learning-based similarity measure for non-rigid image registration. The novel similarity measure is constructed by using two Kullback-Leibler distances (KLD), which are based on the a priori knowledge of the joint intensity distribution of a pre-aligned image pair. In this paper, we propose a new formulation for the novel KLD based similarity measure such that it can be exploited in Markov random field (MRF) based non-rigid registration framework with the graph-cuts algorithm. We have compared the proposed formulation against two other similarity measures under the same MRF-based framework, and two state-of-the-art approaches. According to the experimental results, it is demonstrated that the proposed method can achieve high registration accuracy.
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3times3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use
3D Randomized Connection Network With Graph-Based Label Inference

In this paper, a novel 3D deep learning network is proposed for brain magnetic resonance image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional long-short term memory and the 3D convolution are employed as network units to capture the long-term and short-term 3D properties, respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with the other state-of-the-art methods.
Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms

We present a new multimodal image registration method based on the a priori knowledge of the class label mappings between two segmented input images. A joint class histogram between the image pairs is estimated by assigning each bin value equal to the total number of occurrences of the corresponding class label pairs. The discrepancy between the observed and expected joint class histograms should be minimized when the transformation is optimal. Kullback-Leibler distance (KLD) is used to measure the difference between these two histograms. Based on the probing experimental results on a synthetic dataset as well as a pair of precisely registered 3D clinical volumes, we show that, with the knowledge of the expected joint class histogram, our method obtained longer capture range and fewer local optimal points as compared with the conventional mutual information (MI) based registration method. We also applied the proposed method to a 2D-3D rigid registration problems between DSA and MRA volumes. Based on manually selected markers, we found that the accuracies of our method and the MI-based method are comparable. Moreover, our method is more computationally efficient than the MI-based method.
Trilateral filtering for biomedical images

Filtering is a core operation in low level computer vision. It is a preliminary process in many biomedical image processing applications. Bilateral filtering has been applied to smooth biomedical images while preserving the edges. However, to avoid oversmoothing structures of sizes comparable to the image resolutions, a narrow spatial window has to be used. This leads to the necessity of performing more iterations in the filtering process. In this paper, we propose a novel filtering technique namely trilateral filter, which can achieve edge-preserving smoothing with a narrow spatial window in only a few iterations. The experimental results have shown that our novel method provides greater noise reduction than bilateral filtering and smooths biomedical images without over-smoothing ridges and shifting the edge locations, as compared to other noise reduction methods.