nils-herrmann/clean_scirepeval huggingface dataset:
A video object segmentation algorithm based on the feature learning and shape tracking

This paper proposes a video object segmentation algorithm based on the conditional random field (CRF) framework. A foreground object in the first frame is segmented by training the CRF on user interaction, i.e., by using user scribbles corresponding to foreground and background respectively for CRF training. The data term of the energy function in this CRF framework is designed as a function of the score of texture-color classifier trained by AdaBoost. From the second frame, a weighted data term that encodes the shape of the object is added to this energy function. The boundary pixels of the current frame are predicted by the optical flow, and a smaller cost is given to a pixel closer to the boundary and vice versa. Also, a confidence of optical flow is defined, and a larger weight is given to the data term when the confident is high. As a result, the data term related with the shape becomes important when the motion estimation is reliable, and conversely the color-texture term becomes important otherwise. Experimental results show that the proposed data term keeps the boundary correctly in most cases and provides comparable result when compared to a state-of-the-art method.
Feature-based image registration algorithm for image stitching applications on mobile devices

In this paper, we propose a new feature-based image registration algorithm considering the mobile devices with limited computing power. The algorithm is consisted of two steps: first we reject severe outliers in putative correspondences by considering a similarity transform as a motion model between two views. This approach can find most of the inliers very efficiently, but some non-severe outliers still remain due to its coarse approximation. Therefore, in the second step, we apply an optimization method based on the conjugate gradient algorithm to refine a geometric relation between two views and a set of inliers simultaneously. The proposed method is implemented on a mobile device and experimental results have shown that our method is very efficient and yields good registration performance.
Age and gender classification using wide convolutional neural network and Gabor filter

Age and gender classification has received more attention recently owing to its important role in user-friendly intelligent systems. In this paper, we propose a convolutional neural network (CNN) based architecture for joint age-gender classification, where we use the Gabor filter responses as the input. The weighting of Gabor-filter responses is learned through back-propagation in an end-to-end architecture. The architecture is trained to label the input images into 8 ranges of age and 2 types of gender. Our approach shows improved accuracy in both age and gender classification compared to the state-of-the-art methodologies. We also observe that increasing the width of neural network would increase the accuracy of the overall system.
An IHS fusion algorithm based on the correlation of panchromatic and intensity images

A trade-off parameter approach was recently introduced for the reduction of spectral distortion inherent in the conventional intensity-hue-saturation (IHS) based image fusion algorithm. The parameter controls the contributions of spectral and spatial resolution in order to reduce the spectral distortion while keeping the spatial resolution. In this paper, we attempt to improve the trade-off parameter approach by developing an area dependent piecewise linear control model, whereas the existing method determines the global parameter by trial and error. Block based correlation of panchromatic and intensity images is used to acquire an optimized tradeoff parameters to control the spatial and spectral resolution of the image. Experimental results show that the proposed method provides satisfying results both objectively and subjectively.
A fast 4×4 DCT algorithm for the recursive 2-D DCT

The authors present an efficient algorithm for the computation of the 4*4 discrete cosine transform (DCT). The algorithm is based on the decomposition of the 4*4 DCT into four 4-point 1-D DCTs. Thus, only 1-D transformations and some additions are required. It is shown that the proposed algorithm requires only 16 multiplications, which is half the number needed for the conventional row-column method. Since the 2/sup m/*2/sup m/ DCT can be computed using the 4*4 DCT recursively for any m, the proposed algorithm leads to a fast algorithm for the computation of the 2-D DCT.
Occlusion detection and stereo matching in a stochastic method

This paper proposes a stochastic approach to estimate the occlusion and disparity fields of stereoscopic images. The fields are estimated by the Bayesian maximum a posteriori (MAP) framework and Markov random field (MRF) models. The occlusion field model is based on the stochastic observation that the probability distribution in MAP estimator is relatively unstable and uniform at occluded regions. The occlusion is explicitly modelled as MRF, and is estimated in an energy optimization method called stochastic diffusion. The detected occluded region is compensated for the re-estimated disparity field in the same stochastic diffusion and the dynamic programming approach. Experimental results show good occlusion detection and disparity estimation. These results show the novel stochastic approach is suitable for occlusion detection and disparity estimation.
Fast approximate inverse filtering for reducing motion blur in liquid crystal display

This paper presents an approximate deblur filter for reducing motion blur that appears on liquid crystal displays due to their slow response time and hold-type property. The filter coefficients are derived from the regularized inverse of blur model, which are in the form of concatenation of two or more window functions (low pass filters) separated by the amount of motion. Hence, it is shown that the motion deblur filter for the current frame can be approximated as a weighted sum of two or more low pass filtered consecutive frames, which needs very simple computations. More specifically, each frame is filtered with a 2D lowpass filter, where its size and shape are controlled according to the magnitude and direction of motion vector. Then the output is simply obtained by subtracting previous frame from the current frame with some control weights. Experimental results show that the proposed method provides comparable performance to the existing frequency domain methods, while requiring much lower computational complexity.
Image registration by using a descriptor for repetitive patterns

This paper proposes a new feature-based image registration method based on the description of feature clusters. This method can find larger number of correspondences than the conventional methods using singleton feature descriptors, which often fail in repetitive patterns. The reason for the failure of conventional methods in a repeating pattern is due to the existence of too many similar features, which in turn gives geometrically inconsistent matching or do not survive ratio test. Hence the proposed method follows the strategy that first separate the similar features from the repetitive patterns from the others. Then the similar features in a pattern are grouped into a set that is described by a support vector descriptor in terms of the cluster's center and radius. Once the same pattern in different images are matched, the geometric cue is added to find many geometrically consistent correspondences of the features. In the experiments, it has been demonstrated that the larger number of geometrically consistent correspondences from the repetitive pattern give more accurate registration, and thus more pleasing results in image stitching and panoramic image generation.
Low-Power Filtering Via Minimum Power Soft Error Cancellation

In this paper, an energy-efficient estimation and detection problem is formulated for low-power digital filtering. Building on the soft digital signal processing technique proposed by Hegde and Shanbhag, which combines algorithmic noise tolerance and voltage scaling to reduce power, the proposed minimum power soft error cancellation (MP-SEC) technique detects, estimates, and corrects transient errors that arise from voltage overscaling. These timing violation-induced errors, called soft errors, can be detected and corrected by exploiting the correlation structure induced by the filtering operation being protected, together with a reduced-precision replica of the protected operation. By exploiting a spacing property of soft errors in certain architectures, MP-SEC can achieve up to 30% power savings with no signal-to-noise ratio (SNR) loss and up to 55% power savings with less than 1-dB SNR loss, according to the logic-level simulations performed for an example 25-tap frequency-selective filter.
Adaptive line enhancement by using an IIR lattice notch filter

Two simple methods for retrieving a single sinusoid corrupted with noise are proposed. They are based on the lattice form realization of an adaptive infinite-impulse-response (IIR) notch filter. The IIR filter is a cascade of second-order all-pole and all-zero filters, and the coefficients of the finite-impulse-response (FIR) section are adapted. The proposed algorithms keep the poles of the filter inside the unit circle. The computer simulation results show that the algorithms have considerable potential in adaptive notch filter applications, especially when the input signal-to-noise ratio is low.
Convolutional neural networks and training strategies for skin detection

This paper presents two convolutional neural networks (CNN) and their training strategies for skin detection. The first CNN, consisting of 20 convolution layers with 3 × 3 filters, is a kind of VGG network. The second is composed of 20 networkin-network (NiN) layers which can be considered a modification of Inception structure. When training these networks for human skin detection, we consider patch-based and whole-image-based training. The first method focuses on local features such as skin color and texture, and the second on the human-related shape features as well as color and texture. Experiments show that the proposed CNNs yield better performance than the conventional methods and also than the existing deep-learning based method. Also, it is found that the NiN structure generally shows higher accuracy than the VGG-based structure. The experiments also show that the whole-image-based training that learns the shape features yields better accuracy than the patch-based learning that focuses on local color and texture only.
Sparsity pattern recovery using FRI methods

The problem of finding the sparse representation of a signal has attracted a lot of attention over the past years. In particular, uniqueness conditions and reconstruction algorithms have been established by relaxing a non-convex optimisation problem. The finite rate of innovation (FRI) theory is an alternative approach that solves the sparsity problem using algebraic methods based around Prony's algorithm. Recent extensions to this framework have shown that it is possible to recover sparse representations beyond the uniqueness limits, that is, finding all the possible sparse representations that fit the observation for the case of signals which are sparse in the union of Fourier and canonical bases. In this paper, we show the application of such methods to the case of the union of DCT and Haar basis. We present an extension that takes advantage of the even symmetry of the cosine functions to build an algorithm that can operate over the observed vector and in a dual domain. We also analyse the case of the union of frames. Simulation results confirm the validity of this new approach and show that it outperforms state of the art algorithms in a number scenarios.
An investigation into aliasing in images recaptured from an LCD monitor using a digital camera

With current technology, high quality recaptured images can be created from soft displays, such as an LCD monitor, using a digital still camera and professional image editing software. The task of verifying the ownership and past history of an image is, consequently, more difficult. One approach to detecting an image that has been recaptured from an LCD monitor is to search for the presence of aliasing due to the sampling of the monitor pixel grid. To validate this approach, an investigation into the aliasing introduced in a digitally recaptured image is conducted. An anti-forensic method for recapturing images that are free from aliasing is developed using a model of the image acquisition process. This is supported by a simulation of the acquisition process and illustrated with examples of recaptured images that are free from aliasing.
Model order selection for sampling FRI signals

Recently it has been shown that specific classes of non-bandlimited signals known as signals with finite rate of innovation (FRI) can be perfectly reconstructed by using appropriate sampling kernels and reconstruction schemes. The knowledge of the model order (i.e. the rate of innovation) is essential for correct reconstruction. In view of this, we devise an algorithm which can robustly identify the rate of innovation prior to the signal reconstruction in different noise levels and this extends the current scheme to a universal one that works with signals with unknown rate of innovation and using arbitrary kernels. We use the ‘guaranteed performance’ criterion to assess the performance and show a success rate close to 100% for SNR up to 10dB.
Multiview image compression using a layer-based representation

We propose a novel compression method for multiview images. The algorithm exploits the layer-based representation, which partitions the data set into planar layers characterized by a constant depth value. For efficient compression, the partitioned data is decorrelated using the separable three-dimensional wavelet transform across the viewpoint and spatial dimensions. The transform is modified to efficiently deal with occlusions and disparity variations for different depths. The generated transform coefficients are entropy coded. Experimental results show that our coding method is capable of outperforming the state-of-the-art algorithms, like H.264/AVC, for different data sets.
Distributed Coding of Shifts using the DFT Phase

In this paper we consider the problem of image encoding with side information at the decoder, where the side information is an integer shifted version of the image at the encoder. The encoder is asked to send the shift of its own image with respect to the side information which is only available at the decoder. We propose a solution based on the encoding of the phase sign of the DFT coefficients, taken at exponentially spaced positions. We first introduce the method under ideal hypothesis, i.e. noiseless conditions without border effects, giving a theoretical foundation to the technique. Then, we consider the more realistic case of noisy images with border effects, showing the effectiveness of the proposed method.
Solving inverse source problems for linear PDEs using sparse sensor measurements

Many physical phenomena across several applications can be described by partial differential equations (PDEs). In these applications, sensors collect sparse samples of the resulting phenomena with the aim of detecting its cause/source, using some intelligent data analysis tools on the samples. These problems are commonly referred to as inverse source problems. This work presents a novel framework for solving such inverse source problem for linear PDEs by drawing from certain recent results in modern sampling theory. Under the new framework, we study the well-known diffusion PDE and present numerical results that highlight the validity and robustness of the approach.
Distributed signal processing and communications: on the interaction of sources and channels

Distributed ways of communicating, processing, and sensing are replacing more traditional centralized architectures. An early example of this revolution in distributed communications is appearing in the form of sensor networks, which are densely distributed networks of embedded signal sensors, controls and processors. These nodes could be simple signal sensors, but could also be cameras and microphones. In this distributed scenario, there are several interesting topics to investigate that span from traditional signal processing problems (i.e. sampling, compression, detection) to communication and information theory (i.e. transmission protocols, capacity bounds for ad-hoc networks). This paper reviews some recent results on the topic of source representations and distributed source coding and transmission.
E-spline sampling for precise and robust line-edge extraction

We propose a line-edge extraction algorithm using E-spline functions as a sampling kernel. Our method is capable of extracting line-edge parameters, including amplitude, orientation, and offset, not only at sub-pixel level but also exactly provided noiseless pixel values. Even in noisy scenario, simulation results show that the proposed method outperforms a similar one based around B-spline functions with gains in standard deviation of 1.86dB for the orientation and 9.64dB for the offset when SNR is 10dB. We also show by simulations that our method extracts line-edges more precisely than the Hough transform.
Sampling piecewise smooth signals and its application to image up-sampling

In this paper we consider the problem of sampling piecewise smooth signals. The classical sampling theory is not able to sample them efficiently since the signals are neither bandlimited nor live in a shift-invariant subspace. We propose a sampling scheme which still use the set of samples from a classical sampling set-up with an arbitrary acquisition device but achieves accurate reconstruction by making use of the non-linear approximate FRI reconstruction method in addition to the classical linear reconstruction. Specifically, we see the class of piecewise smooth signal as the sum of a piecewise polynomial, which is a signal that is fully specified by finite number of parameters and can be recovered with FRI non-linear methods, and a globally smooth term, which can be accurately recovered by linear reconstruction. We also show that our proposed scheme based on combining linear and non-linear reconstruction methods can be employed for resolution enhancement of discrete-time piece-wise smooth signals and images.
The modulated E-spline with multiple subbands and its application to sampling wavelet-sparse signals

The theory of Finite Rate of Innovation (FRI) can be applied to sampling and reconstructing certain classes of parametric signals. The objective of this paper is to have a sub-Nyquist sampling scheme for continuous-time wavelet-sparse signals within the general framework of FRI theory. Though the signal has a parametric representation in the wavelet basis, it is not possible to recover the signal merely from its low-pass samples, which makes the problem different from the conventional FRI settings. The need for the Fourier coefficients at frequencies widely spread over the spectrum puts challenges on the design of the sampling kernel. This paper presents a new family of sampling kernels that are able to stably reproduce exponentials over a wide range of frequencies and gives numerical examples on applying the new kernel to sampling wavelet-sparse signals.
Sparse sampling of structured information and its application to compression

It has been shown recently that it is possible to sample classes of non-bandlimited signals which we call signals with Finite Rate of Innovation (FRI). Perfect reconstruction is possible based on a set of suitable measurements and this provides a sharp result on the sampling and reconstruction of sparse continuous-time signals. In this paper, we first review the basic theory and results on sampling signals with finite rate of innovation. We then discuss variations of the above framework to handle noise and model mismatch. Finally, we present some results on compression of piecewise smooth signals based on the FRI framework.
Video jitter analysis for automatic bootleg detection

This paper presents a novel technique for the automatic detection of recaptured videos with applications to video forensics. The proposed technique uses scene jitter as a cue for classification: when recapturing planar surfaces approximately parallel to the imaging plane, any added motion due to jitter will result in approximately uniform high-frequency 2D motion fields. The inter-frame motion trajectories are retrieved with feature tracking techniques, while local and global feature motion are decoupled through a 2-level wavelet decomposition. A normalised cross-correlation matrix is then populated with the similarities between the high-frequency components of the tracked features' trajectories. The correlation distribution is then compared with trained models for classification. Experiments with original and recaptured standard datasets show the validity of the proposed technique.
ProSparse extension: Prony's based sparse pattern recovery with extended dictionaries

ProSparse is a Prony's based method that solves the sparse representation problem of signals in the union of Fourier and canonical bases. By exploiting the structure of the dictionary, ProSparse is able to reconstruct sparse signals beyond the recovery bound of Basis Pursuit. We generalize this framework for a broader class of dictionaries which are still formed from the union of two bases. The proposed algorithm achieves perfect reconstruction over a lower sparsity level than Basis Pursuit in noiseless cases. In the presence of noise, we extend the ProSparse Denoise algorithm to the generalized dictionaries by considering their intrinsic structure. The original ProSparse can be viewed as a special case of our proposed algorithm. From simulation results, our approach outperforms state-of-the-art algorithms.
FRESH—FRI-Based Single-Image Super-Resolution Algorithm

In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels.