nils-herrmann/clean_scirepeval huggingface dataset:
Underground imaging based on edge-preserving regularization

Develops approaches for imaging weak-contrast buried objects using data from a ground penetrating radar array. An approximate physical model relating the collected data to the underground objects is developed. This model uses ray optics to represent the air/soil interface, and a Born approximation to model the weak contrast backscattering from buried objects. In order to address both modeling errors and ill-posedness, the proposed image reconstruction algorithms use regularization based on a total variation norm with orientation preference. The algorithms are tested on data generated by nonlinear finite difference time domain electromagnetic simulations.
Bayesian restoration of noisy images with the EM algorithm

In this paper, we demonstrate that a window-based Gaussian mixture model can be applied in the development of a robust nonlinear filter for image restoration. Via the EM algorithm, we utilize ML estimation of the spatially-varying model parameters to achieve the desired noise suppression and detail preservation. We demonstrate that this approach is a powerful tool which gives us information about the local statistics of noisy images. We demonstrate that the estimated local statistics can be efficiently utilized for outlier detection and edge detection. The advantage of our algorithm is that it can simultaneously suppress additive Gaussian and impulsive noise, while preserving fine details and edges.
Multiresolution statistical analysis and assimilation of large ocean data sets

A significant problem in oceanographic remote sensing is the dense gridding or smoothing of sparsely sampled altimetric data. The smoothing of altimetric measurements has an application much broader than just the regular production of elevation maps for oceanographers, however. In particular, the ability to estimate ocean circulation patterns from altimetric data can serve as an important measure for the verification of ocean acoustic tomographic results. The authors present a multiscale technique capable of extremely efficient interpolation of altimetric data: about 256000 estimates and estimation error variances are computed in one minute on a Sun Sparc-10. They also demonstrate how similar techniques may be used to directly estimate the surface gradient and biases in the geoid-model error.
A curve evolution approach for image segmentation using adaptive flows

In this paper, we develop a new active contour model for image segmentation using adaptive flows. This active contour model can be derived from minimizing a limiting form of the Mumford-Shah functional, where the segmented image is assumed to consist of piecewise constant regions. This paper is an extension of an active contour model developed by Chan-Vese. The segmentation method proposed in this paper adaptively estimates mean intensities for each separated region and uses a single curve to capture multiple regions with different intensities. The class of imagery that our new active model can handle is greater than the bimodal images. In particular, our method segments images with an arbitrary number of intensity levels and separated regions while avoiding the complexity of solving a full Mumford-Shah problem. The adaptive flow developed in this paper is easily formulated and solved using level set methods. We illustrate the performance of our segmentation methods on images generated by different modalities.
Detection and localization of obscured objects from near-field sensor array data: physical models and statistical processing

The problem of near field object detection from array-based electromagnetic induction data is addressed. A forward scattering model based on the Born approximation is employed. Using insight obtained from this model, a clutter model in the form of a state space system is developed which describes the correlation of the noise both across the sensing array and from one position of the array to the next as the measurement device proceeds down track. A multiple-model detection scheme based on the whitening properties of the Kalman filter is employed to perform the actual object detection. This approach is applied to the detection and localization of buried objects. Examples are provided for land mines buried directly in front and off to one side of the array.
Fast angiographic OCT imaging using sparse representations over learned dictionaries

Recent developments in optical coherence tomography (OCT) have enabled wide-field and high-resolution angiographic imaging. However, generation of vascular contrast inherently requires long imaging times. In this work, we demonstrate a reconstruction technique based on sparse and redundant representations over trained dictionaries that can be used to reduce acquisition times and accurately reconstruct angiographic OCT projection images with full vascular detail from a smaller number of B-scans than conventionally required. Our technique for fast angiographic imaging through reconstruction (FAIR), shows excellent reconstruction quality while using only half of the number of B-scans and graceful quality degradation with further undersampling.
Low-dose X-ray CT reconstruction based on joint sinogram smoothing and learned dictionary-based representation

In this paper we propose two novel image reconstruction methods for low-dose X-ray CT data. Both approaches are based on anisotropic sinogram smoothing coupled with sparse local image representation with respect to a learned over complete dictionary. The redundant dictionary is learned from normal-dose CT training images and encodes artifact-free image behavior. The methods differ in the details of how the redundant dictionary information is included. Efficient solution approaches to the new formulations are provided. Comparative results on simulated low-dose imagery are given. Our approach is new in how it applies learning-based dictionary techniques to low-dose CT reconstruction, in its use of high quality training data in dictionary generation, and in its incorporation of anisotropic sinogram constraints together with the dictionary-based representation.
Accurate fingertip detection from binocular mask images

Accurate fingertip detection is important for hand-based human computer interaction. Different from prior methods based on depth image, in this paper we propose a novel scheme for accurate fingertip detection from binocular mask images without explicitly computing the depth map. To demonstrate our proposed scheme, we build a new hand dataset containing synthetic and real binocular images. A deep convolutional neural network (CNN) is utilized as a baseline method to demonstrate the proposed scheme. The mask images are extracted from binocular images and fed into the CNN to predict the 3D positions of fingertips and palm center. Experiments show that this method achieves the mean error of 8.30mm on synthetic data and 4.64mm on real data, which is promosing for accurate 3D interaction. The proposed scheme runs at 130fps on a CPU and is promising for real-time applications.
Defect detection of printing images on cans based on SSIM and chromatism

In the production of digital printing equipment, it is a challenge to detect the printed surface defects of cylindrical surface like cans. This paper presents an automatic inspection program based on computer vision. We build a set of printing detection hardware system including the automatic image acquisition, high precision image registration. Then we propose a geometric defect detection based on SSIM and color defect detection algorithm based on CIEDE2000 color difference formula. The experimental results show that the system proposed by us has good effectiveness and robustness, and can realize digital detection and feedback adjustment.
Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition

Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction. In this paper, we propose a new motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. Finger motion features are extracted to describe finger movements and global motion features are utilized to represent the global movement of hand skeleton. These motion features are then fed into a bidirectional recurrent neural network (RNN) along with the skeleton sequence, which can augment the motion features for RNN and improve the classification performance. Experiments demonstrate that our proposed method is effective and outperforms start-of-the-art methods.
Multiple instance tracking based on hierarchical maximizing bag's margin boosting

In online tracking, the tracker evolves to reflect variations in object appearance and surroundings. This updating process is formulated as a supervised learning problem, thus a slight inaccuracy of the tracker will degrade the updating. Multiple Instance Learning (MIL) is used to alleviate such a problem by representing training samples in bags of image patches (or called instances). Difficulties are then passed on to the learning method to train a classifier that discovers the most accurate instance. This paper proposes a Maximizing Bag's Margin (MBM) criteria for MIL. Combined with MBM, a hierarchical boosting is proposed for updating, in which bag and instance weights are introduced to guide classifier retraining. Our approach effectively improves the updating's efficiency with less computation cost. Experiments demonstrate the benefits of our method.
Region ensemble network: Improving convolutional network for hand pose estimation

Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional methods is not so apparent. To promote the performance of directly 3D coordinate regression, we propose a tree-structured Region Ensemble Network (REN), which partitions the convolution outputs into regions and integrates the results from multiple regressors on each regions. Compared with multi-model ensemble, our model is completely end-to-end training. The experimental results demonstrate that our approach achieves the best performance among state-of-the-arts on two public datasets.
High-Resolution Image Inpainting through Multiple Deep Networks

For the operation and aerial photography of the UAV, it is important to identify the blindspots and observe the details on the ground. But limited by the camera resolution, small or fuzzy objects can not be effectively observed. Therefore, repairment of high-definition images has become one of the important problems to be solved. In recent years, the development of the deep learning method has effectively solved the loss and blurring of images, but because of the difficulties in training and the speed of calculation it can only be used with low-pixel images. Therefore, we propose a method for superimposing images first with the content and textual recovery for the defaced area. We use unsupervised learning GANs and trained VGG network to restore holes and missing areas of the image, and then enlarge it through CNN method. Our preliminary results show that high resolution image restoration speed has been greatly improved, and details become sharper than using traditional method.
Channel-adaptive error protection for scalable audio streaming over wireless Internet

Streaming high-fidelity audio over networks with both bit errors and packet erasures is becoming increasingly important due to the emerging of wireless Internet. In this paper, we propose an end-to-end architecture for scalable audio streaming over wireless Internet. Considering the characteristic of scalable audio, a novel layered product code is presented to handle bit errors and packet losses simultaneously. Specifically, unequal row channel code and unequal column code are adopted for different layers of scalable audio based on their quality impacts. Moreover, rate-distortion based bit allocation is proposed to determine each channel-coding rate and the source-coding rate so as to minimize the expected end-to-end distortion. The simulation results demonstrate the effectiveness of our proposed error protection scheme.
Enhanced Shot Change Detection using Motion Features for Soccer Video Analysis

An enhanced shot change detection algorithm for soccer video analysis is proposed in this paper. Features extracted from reliable motion vectors (MVs) are used to enhance the accuracy and efficiency of color-based method. In order to eliminate unreliable MVs, a MV filtration scheme is designed based on the principle of block-based motion search. Then two features, named as proportion of reliable MVs and centrality of reliable MVs respectively, are extracted from the filtration results in each frame. The experiments on 180 minutes' soccer video demonstrate that our algorithm achieved a considerable improvement over the widely-adopted color-based method.
Subcategory Clustering with Latent Feature Alignment and Filtering for Object Detection

For objects with large appearance variations, it has been proved that their detection performance can be effectively improved by clustering positive training instances into subcategories and learning multi-component models for the subcategories. However, it is not trivial to generate subcategories of high quality, due to the difficulty in measuring the similarity between positive instances. In this letter we propose a new weakly supervised clustering method to achieve better sub-categorization. Our method provides a more precise measurement of the similarity by aligning the positive instances through latent variables and filtering the aligned features. As a better alternative to the initialization step of the latent-SVM algorithm for the learning of the multi-component models, our method can lead to a superior performance gain for object detection. We demonstrate this on various real-world datasets.
Local matting based on sample-pair propagation and iterative refinement

This paper proposes a novel local matting algorithm based on sample-pair propagation and iterative refinement. Since sample-pairs of the foreground and background in the neighborhood are limited, they fail to fit the linear model well. We propose a sample-pair propagation scheme which propagates the confident sample-pair of each pixel to its neighbors so that they can collect more confident sample-pairs to estimate alpha values accurately. To avoid high time and space complexity of the global optimization, we convert matting into a de-noising problem and refine alpha values via fitting the linear model and smoothing the alpha matte locally and iteratively. Experimental results demonstrate that our algorithm produces more accurate results than the state-of-the-art of local matting.
Robust RGB-D SLAM in dynamic environment using faster R-CNN

Recent approaches for simultaneous localization and mapping (SLAM) have shown great success for static environment reconstruction. However, these methods suffer from performance degeneration in dynamic environment due to the unstable and ambiguous data associations. In this paper, we propose a robust and efficient method for SLAM in dynamic environment. To eliminate the influence of dynamic objects, we compute the difference between the current frame and keyframe by semantic and geometric information to detect the dynamic object. Then we pick up the reliable data association and get more accurate transform matrix. The experiments on the public dataset demonstrate that our proposed method outperforms the original method in dynamic environment.
Learning Transformation-Invariant Representations for Image Recognition With Drop Transformation Networks

This paper proposes a drop transformation networks (DTNs), a novel framework of learning transformation-invariant representations of images with good flexibility and generalization ability. Convolutional neural networks are a powerful end-to-end learning framework that can learn hierarchies of representations. Although the invariance to translation of the representations can be introduced by the approach of stacking convolutional and max-pooling layers, the approach is not effective in tackling other geometric transformations such as rotation and scale. Rotation and scale invariance are usually obtained through data augmentation, but this requires larger model size and more training time. DTN formulates transformation-invariant representations through explicitly manipulating geometric transformations within it. DTN applies multiple random transformations to its inputs but keeps only one output according to the given dropout policy. In this way, the complex dependencies of the knowledge on transformations contained in training data can be alleviated, and therefore the generalization to transformations is improved. Another advantage of DTN is the flexibility. Under the proposed framework, data augmentation can be seen as a special case. We evaluate DTN on three benchmark data sets and show that it can provide better performance with smaller number of parameters compared to state-of-the-art methods.
Error robust scalable audio streaming over wireless IP networks

Streaming high-fidelity audio over wireless Internet protocol (IP) networks is a challenging task because the networks present not only packet losses, but also residual bit errors. These losses and errors have severe adverse effect on the compressed audio bitstream. To solve this problem, this paper introduces error resilience in conjunction with error protection for scalable audio streaming over wireless networks. Specifically, error resilience is achieved by performing bitstream data partitioning and reversible variable length coding in the audio coder. Error protection is provided by layered product channel code to simultaneously handle packet losses and residual bit errors. Both the row and column codes of the product code provide unequal error protection for different layers of the audio bitstream by considering the characteristics of the scalable audio. Rate-distortion optimization is performed to determine the best source-channel coding tradeoff that minimizes the average expected end-to-end distortion. Simulation results demonstrate the effectiveness of our proposed approach.
Patient-specific ECG classification based on recurrent neural networks and clustering technique

In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification algorithm based on the recurrent neural networks (RNN) and density based clustering technique. We use RNN to learn time correlation among ECG signal points and to classify ECG beats with different heart rates. Morphology information including the present beat and the T wave of former beat is fed into RNN to learn underlying features automatically. Clustering method is employed to find representative beats as the training data. Evaluated on the MIT-BIH Arrhythmia Database, the experimental results show that proposed algorithm achieves the state-of-the-art classification performance.
Scale and rotation invariant feature-based object tracking via modified on-line boosting

Object tracking is a major technique in image processing and computer vision. In this paper, we propose a new robust feature-based tracking scheme by employing adaptive classifiers to match the detected keypoints in consecutive frames. The novelty of this paper is that the design of online boosting is combined with the invariance of local features so that the classifier-based descriptions are formed in association with the scale and rotation information. Furthermore, we introduce a sample weighting mechanism in the on-line classifier updating, for the subsequent tracking. Experimental results demonstrate the robustness and accuracy of our proposed technique.
Hand posture recognition in video using multiple cues

Hand posture conveys profound information for computer vision applications, but the articulated hand structure and restraint capture condition cast a tough obstacle on practical implementation, especially in real time video. This paper presents a framework to recognize hand postures in consecutive video frames. Mixture of Gaussian skin/non skin models is constructed for hand region detection, followed by particle filter to track hand. Then a soft-decision scheme based on extended Histogram of Orientated gradient is proposed to refine the best posture region and recognize it from pre-defined posture set. Experimental result shows promising performance under various capture conditions.
Kernel based articulated object tracking with scale adaptation and model update

Kernel based object tracking (KBOT) is one of the most popular and effective techniques for tracking task. However the constancy of the target model and unsound scale adaptation method are two main limitations. In this paper, we present a kernel based approach incorporated with scale estimation and target model update for articulated object tracking task. After predicating the object center with scale fixed KBOT, we extend scale selection theory to estimate the local optimal object scale. Once the object scale has been estimated, a kernel density estimation based strategy is developed to update the target model. Experimental results show that our approach is superior to traditional KBOT in the following two aspects: 1) it is less affected by the object scale change; 2) it is less prone to appearance variation.
Day markings detection in natural photo with morphological method

Day marking is one of valuable information for many applications such as image retrieval and indexing. In this paper, a novel automatic scheme is presented for day marking detection from color photographs. First, taking advantages of the stroke thickness, gray-scale morphologic method is specially proposed to generate the bounding box in R-component domain, where the day texts are located and the background influence can be reduced. Then binarization module is set to favor character recognition, where some post-processing is taken to remove white-dot noise and fill the holes in the strokes. Finally the binarized image should be segmented and fed to OCR module so that the characters of the day marking can be recognized. Simulations show the effectiveness of our method.
On the Effectiveness of Opportunistic Routing over Wireless Mesh Networks

Recently, opportunistic routing has gained much popularity for the ability to exploit the broadcast nature of the wireless medium with several practical protocols been proposed over wireless mesh networks (WMNs). These protocols usually add many mechanisms to introduce a dramatic increase in the overall network throughput, such as reliability promise and rate control. However, the evaluation of these protocols did not show how much performance benefit can be expected from each mechanism. In this paper, we provide a systematic performance evaluation on the effectiveness of reliability promise and rate control mechanisms that commonly used in opportunistic routing protocols. Our study shows that: (i) batch acknowledgment used in opportunistic routing significant improves the transmission performance over traditional routing, the true gain from opportunistic routing is not as much as claimed in previous works. (ii) rate control is important to the opportunistic routing. It ensures opportunistic routing to utilize its advantage sufficiently, but also prevents the performance degradation when the network is congested. These results give us new understanding in the effectiveness of the opportunistic routing. We then analyze and uncover reasons for these results and propose some specific schemes.