nils-herrmann/clean_scirepeval huggingface dataset:
A surface-constrained volumetric alignment method for image atlasing

In this paper, we propose a prototype system capable of incorporating 3D shape information with conventional TPS-based (thin-plate-spline) volumetric registration method for image atlasing. Our method consists of two phases. The former phase registers and warps the 3D mesh surface models describing the tissue shape boundary of the input image volumes, and the latter aims to align the input image volumes with the aid of the boundary constraints suggested by the former. The proposed volumetric registration method is driven and constrained by the pre-registered 3D mesh surface model. Experiments show that using our framework for volumetric image registration and warping obtains a performance comparable to or better than a well-known benchmark method.
A Sampling-Based Gem Algorithm with Classification for Texture Synthesis

Research on texture synthesis has made substantial progress in recent years, and many patch-based sampling algorithms now produce quality results in an acceptable computation time. However, when such algorithms are applied, whether they provide good results for specific textures, and why they do so, are questions that have yet to be answered. In this article, we deal specifically with the second question by modeling the synthesis problem as one of learning from incomplete data, and propose an algorithm that is a generalization of patch-work approach. Through this algorithm, we demonstrate that the solution of patch-based sampling approaches is an approximation of finding the maximum-likelihood optimum by the generalized expectation and maximization (GEM) algorithm
Error concealment protection for loss resilient bitplane-coded video communications

In this paper, we propose an error control scheme for video communications over lossy channels. The proposed algorithm uses error concealment protection (ECP) approach to coordinate with multiple encoded streams which is capable of handling error concealment to achieve robust transmissions. Unlike previous methods, our algorithm focuses on joint optimizing multiple substreams distortion with concealment over error prone channels. The algorithm minimizes the expected rate-distortion function to achieve the optimal FEC result. In the experiments, we demonstrate the effectiveness of our method by using a 3-D SPIHT algorithm. Simulation results show that the proposed protection strategy achieves about 2 dB higher peak signal-to-noise ratio compared to conventional method
3D thin-plate spline registration for Drosophila brain surface model

With the progress of model averaging algorithms, scientists in the field of brain research have an increasing demand for methods capable to register and warp source data to the pre-registered standard atlas. We here propose a thin-plate spline (TPS) based surface registration method to facilitate the registration and warping process of Drosophila brain data. Our contributions are twofold. First, the proposed method performs TPS-based registration in the parameterization domain, and hence it no longer needs a rigid transformation to globally align and scale the input models. Second, the obtained well-registered surface model can act as boundary constraints for further volumetric registration schemes. Experiments show that the proposed method is effective. For models with a 750-voxel-long bounding box diagonal, the average surface-to-surface distance is reduced to about 0.1-voxel-long after registration.
Analysis of singularities from modulus maxima of complex wavelets

Complex-valued wavelets are normally used to measure instantaneous frequencies, while real wavelets are normally used to detect singularities. We prove that the wavelet modulus maxima with a complex-valued wavelet can detect and characterize singularities. This is an extension of the previous wavelet work of Mallat and Hwang on modulus maxima using a real wavelet. With this extension, we can simultaneously detect instantaneous frequencies and singularities from the wavelet modulus maxima of a complex-valued wavelet. Some results of singularity detection with the modulus maxima from a real wavelet and an analytic complex-valued wavelet are compared. We also demonstrate that singularity detection methods can be employed to detect the corners of a planar object.
Singularity detection and processing with wavelets

The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images.
A Proximal Method for Dictionary Updating in Sparse Representations

In this paper, we propose a new dictionary updating method for sparse dictionary learning. Our method imposes the ℓ0 norm constraint on coefficients as well as a proximity regularization on the distance of dictionary modifications in the dictionary updating process. We show that the derived dictionary updating rule is a generalization of the K-SVD method. We study the convergence and the complexity of the proposed method. We also compare its performance with that of other methods.
Complex-valued wavelet transform applications in planar shape prototype generation and recognition

The prototype generation has been widely used in industrial design, medical imaging, computer animation, pattern recognition, and computer vision. We propose a new hierarchical tree based algorithm for generating a prototype contour from a class of similar planar solid objects. Our algorithm uses the wavelet moduli of a complex-valued wavelet to extract the contour features. Features of different objects are organized as a binary tree. The root of a subtree corresponds to the prototype contour for the contours of its children. We show that one can update this tree efficiently by only modifying the local subcontours. An experiment of our method to prototype generation and pattern recognition is demonstrated.
Matching pursuits low bit rate video coding with codebooks adaptation

We propose a codebook adaptation algorithm for matching pursuit low bit rate video compression. The matching pursuit dictionary is a set of basis functions. In practice, a function is approximated as a vector. According to the incoming data, the basis functions are modified similar to the minimum-point-finding algorithm in stochastic regression. Although matching pursuit low bit rate compressions have been studied intensively, bases adaptation in the dictionary is a new approach. We demonstrate the performance of our adaptation method on MPEG-4 video sequences.
Combined Error Concealment and Error Correction in Rate-Distortion Analysis for Multiple Substream Transmissions

We propose a new framework for multiple scalable bitstream video communications over lossy channels. The major feature of the framework is that the encoder estimates the effects of postprocessing concealment and includes those effects in the rate-distortion analysis. Based on the framework, we develop a rate-distortion optimization algorithm to generate multiple scalable bitstreams. The algorithm maximizes the expected peak signal-to-noise ratio by optimally assigning forward error control codes and transmission schemes in a constrained bandwidth. The framework is a general approach motivated by previous methods that perform concealment in the decoder, as in our special case. Simulations show that the proposed approach can be implemented efficiently and that it outperforms previous methods by more than 2 dB
Analysis on multiresolution mosaic images

Image mosaicing is the act of combining two or more images and is used in many applications in computer vision, image processing, and computer graphics. It aims to combine images such that no obstructive boundaries exist around overlapped regions and to create a mosaic image that exhibits as little distortion as possible from the original images. In the proposed technique, the to-be-combined images are first projected into wavelet subspaces. The images projected into the same wavelet space are then blended. Our blending function is derived from an energy minimization model which balances the smoothness around the overlapped region and the fidelity of the blended image to the original images. Experiment results and subjective comparison with other methods are given.
Estimating particulate matter using COTS cameras

Particulate pollution has become increasingly critical and threatening for human health. Although a number of approaches have been attempted for particulate pollution monitoring, these approaches are either expensive, unscalable, or requiring deployment of yet-another sensing infrastructure. In this study, by combining the advanced image dehazing and support vector machine techniques, we propose a novel particulate matter sensing approach using commercially off-the-shelf cameras. Using a Raspberry Pi-based testbed, we conducted a half-year measurement and conduct a comprehensive analysis of our approach. We show that our approach is effective, and the 80%-th estimation error is below 20 and 30 μg/m3 for PM2.5 and PM10 estimation, respectively. Moreover, the proposed approach can be easily applied to the existing camera surveillance infrastructure, as long as the photos contain both long-range and near-view objects.
An asymmetric watermarking method for copyright protection utilizing dual bases

We present an asymmetric watermarking method for copyright protection that uses different matrix operations to embed and extract a watermark. It allows for the public release of all information, except the secret key. We investigate the conditions for a high detection probability, a low false positive probability, and the possibility of unauthorized users successfully hacking into our system. The robustness of our method is demonstrated by the simulation of various attacks.
Adaptive Signal Decomposition Based on Local Narrow Band Signals

We propose an operator-based method of adaptive signal decomposition, whereby a local narrow band signal is defined in the null space of a singular local linear operator. Based on the definition and the algorithm, we propose two types of local narrow band signals and two singular .operator estimation methods for adaptive signal decomposition. We show that our approach can solve a special case of Huang 's empirical-mode decomposition algorithm. For signals that cannot be resolved by our method or the empirical-mode decomposition algorithm, we propose a hybrid approach. Conceptually, the approach applies the empirical-mode decomposition algorithm, followed by our algorithms. Our experiments show that the proposed hybrid approach can solve a wide range of complex signals effectively.
Light field upsampling by joint bilateral filtering on epipolar plane images

Due to the trade-off between spatial and angular resolution, the effective spatial resolution of a light field image is usually less than one percent of the number of pixels on the photo sensor. In this paper, we propose a prototype algorithm to upsample a light field image. Because the boundary edges of 3D objects would result in lines on epipolar plane images (EPIs), the main idea of our method is to preserve these line structures while upsampling so that the image enlargement can still have sharp boundary edges. The kernel of the proposed algorithm is an iterative joint-bilateral filtering process. Experiments show that the upsampled image derived by our method is still refocusable and has better visual quality than those derived by other methods. Finally, the main contribution of this method is that it decomposes a 4D light field space L(u, v, s, t) upsampling problem into a series of 1D, parameter-free upsampling subproblems that can be solved fast in u-s and v-t EPI domains.
Sparse representation of a blur kernel for out-of-focus blind image restoration

Blind image restoration is a non-convex problem which involves restoration of images from an unknown blur kernel. The factors affecting the performance of this restoration are how much prior information about an image and a blur kernel are provided and what algorithm is used to perform the restoration task. Prior information on images is often employed to restore the sharpness of the edges of an image. However, no consensus is present regarding what prior information to use in restoring from a blur kernel due to complex image blurring processes. In this paper, we propose modelling of a blur kernel as a sparse linear combinations of basic 2-D patterns. Our approach has a competitive edge over the existing blur kernel modelling methods because our method has the flexibility to customize the dictionary design, which makes it well-adaptive to a variety of applications. As a demonstration, we construct a dictionary formed by basic patterns derived from the Kronecker product of Gaussian sequences. We also compare our results with those derived by other state-of-the-art methods, in terms of improvement SNR (ISNR).
Colored multi-neuron image processing for segmenting and tracing neural circuits

Recently developed were the Brainbow and Flybow techniques that can image and visualize a large number of neurons simultaneously; however, scientists still lack adequate tools to process this kind of colored multi-neuron image volumes. Due to dozens of colorized neuron fibers spreading densely in a very intricate structure, it is difficult to trace them by existing algorithms designed for single-neuron images. We proposed a framework to formulate and solve this issue, and the experimental results show that our method can successfully extract independent neurons from Flybow images. Consequently, the proposed procedure contributes to neuroscience by increasing the efficiency of collecting neuron information from Flybow images.
Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation

The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. The approach can be formulated as an optimization problem whose optimal solution can be derived analytically. However, the following issues must still be resolved: estimating the robustness of the operator's parameters and the Lagrangian multipliers, and determining how much of the information in the null space of the operator should be retained in the residual signal. To address these problems, we propose a novel optimization formula for operator-based signal separation and show that the parameters of the problem can be estimated adaptively. We demonstrate the effectiveness of the proposed method by processing several signals, including real-life signals.
Estimation of fractional Brownian motion embedded in a noisy environment using nonorthogonal wavelets

We show that nonorthogonal wavelets can characterize the fractional Brownian motion (fBm) that is in white noise. We demonstrate the point that discriminating the parameter of fBm from that of noise is equivalent to discriminating the composite singularity formed by superimposing a peak singularity on a Dirac singularity. We characterize the composite singularity by formalizing this problem as a nonlinear optimization problem. This yields our parameter estimation algorithm. For fractal signal estimation, Wiener filtering is explicitly formulated as a function of the signal and noise parameters and the wavelets. We show that the estimated signal is a 1/f process. Comparative studies through numerical simulations of our methods with those of Wornell and Oppenheim (1992) are presented.
Optimal Bit-allocation for Wavelet-based Scalable Video Coding

We investigate the wavelet-based scalable video coding problem and present a solution that takes account of each user's preferred resolution. Based on the preference, we formulate the bit allocation problem of wavelet-based scalable video coding. We propose three methods to solve the problem. The first is an efficient Lagrangian-based method that solves the upper bound of the problem optimally, and the second is a less efficient dynamic programming method that solves the problem optimally. Both methods require knowledge of the user's preference. For the case where the user's preference is unknown, we solve the problem by a min-max approach. Our objective is to find the bit allocation solution that maximizes the worst possible performance. We show that the worst performance occurs when all users subscribe to the same spatial, temporal, and quality resolutions. Thus, the min-max solution is exactly the same as the traditional bit allocation method for a non-scalable wavelet codec. We conduct several experiments on the 2D+t MCTF-EZBC wavelet codec with respect to various subscribers' preferences. The results demonstrate that knowing the users' preferences improves the coding performance of the scalable video codec significantly.
An Examplar-Based Approach for Texture Compaction Synthesis and Retrieval

A texture representation should corroborate various functions of a texture. In this paper, we present a novel approach that incorporates texture features for retrieval in an examplar-based texture compaction and synthesis algorithm. The original texture is compacted and compressed in the encoder to obtain a thumbnail texture, which the decoder then synthesizes to obtain a perceptually high quality texture. We propose using a probabilistic framework based on the generalized EM algorithm to analyze the solutions of the approach. Our experiment results show that a high quality synthesized texture can be generated in the decoder from a compressed thumbnail texture. The number of bits in the compressed thumbnail is 400 times lower than that in the original texture and 50 times lower than that needed to compress the original texture using JPEG2000. We also show that, in terms of retrieval and synthesization, our compressed and compacted textures perform better than compressed cropped textures and compressed compacted textures derived by the patchwork algorithm.
Planar-shape prototype generation using a tree-based random greedy algorithm

A prototype is representative of a set of similar objects. This paper proposes an approach that formulates the problem of prototype generation as finding the mean from a given set of objects, where the prototype solution must satisfy certain constraints. These constraints describe the important perceptual features of the sample shapes that the proposed prototype must retain. The contour prototype generated from a set of planar objects was used as an example of the approach, and the corners were used as the perceptual features to be preserved in the proposed prototype shape. However, finding a prototype solution for more than two contours is computationally intractable. A tree-based approach is therefore proposed in which an efficient greedy random algorithm is used to obtain a good approximation of the proposed prototype and analyze the expected complexity of the algorithm. The proposed prototype-generation process for hand-drawn patterns is described and discussed in this paper.
Optimal Multiresolution Blending of Confocal Microscope Images

Typical mosaicing schemes assume that to-be-combined images are equally informative; thus, the images are processed in a similar manner. However, the new imaging technique for confocal fluorescence images has revealed a problem when two asymmetrically informative biological images are stitched during microscope image mosaicing. The latter process is widely used in biological studies to generate a higher resolution image by combining multiple images taken at different times and angles. To resolve the earlier problem, we propose a multiresolution optimization approach that evaluates the blending coefficients based on the relative importance of the overlapping regions of the to-be-combined image pair. The blending coefficients are the optimal solution obtained by a quadratic programming algorithm with constraints that are enforced by the biological requirements. We demonstrate the efficacy of the proposed approach on several confocal microscope fluorescence images and compare the results with those derived by other methods.
Robust speech recognition features based on temporal trajectory filtering of frequency band spectrum

The paper presents the use of a variety of filters in the temporal trajectories of the frequency band spectrum to extract speech recognition features for environmental robustness. Three kinds of filters for emphasizing the statistically important parts of speech are proposed. First, a bank of RASTA-like band-pass filters to fit the statistical peaks of the modulation frequency band spectrum of speech are used. Secondly, a three-channel octave band-filter band with a smoothed rectangular window spline is applied. Thirdly, a data-driven filter is developed. Experimental results show that significant improvements for speech recognition using the proposed feature extraction approach under noisy environments can be achieved.
Adaptive Integral Operators for Signal Separation

The operator-based signal separation approach uses an adaptive operator to separate a signal into a set of additive subcomponents. In this paper, we show that differential operators and their initial and boundary values can be exploited to derive corresponding integral operators. Although the differential operators and the integral operators have the same null space, the latter are more robust to noisy signals. Moreover, after expanding the kernels of Frequency Modulated (FM) signals via eigen-decomposition, the operator-based approach with the integral operator can be regarded as the matched filter approach that uses eigen-functions as the matched filters. We then incorporate the integral operator into the Null Space Pursuit (NSP) algorithm to estimate the kernel and extract the subcomponent of a signal. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art approaches in separating multiple-component synthesized signals and real-life signals.
Operator based multicomponent AM-FM signal separation approach

The operator-based signal separation approach, which formulates the signal separation as an optimization problem, uses an adaptive operator to separate a signal into additive subcomponents. Furthermore, it is possible to design different operators to fit different signal models. In this paper, we propose a new kind of differential operator to separate multicomponent AM-FM signals. We then use the estimated operators to calculate each sub-component's envelope and instantaneous frequency. To demonstrate the efficacy of the proposed method, we compare the decomposition and AM-FM demodulation results of several signals, including real-life signals.