nils-herrmann/clean_scirepeval huggingface dataset:
Learning-Based Hierarchical Graph for Unsupervised Matting and Foreground Estimation

Automatically extracting foreground objects from a natural image remains a challenging task. This paper presents a learning-based hierarchical graph for unsupervised matting. The proposed hierarchical framework progressively condenses image data from pixels into cells, from cells into components, and finally from components into matting layers. First, in the proposed framework, a graph-based contraction process is proposed to condense image pixels into cells in order to reduce the computational loads in the subsequent processes. Cells are further mapped into matting components using spectral clustering over a learning based graph. The graph affinity is efficiently learnt from image patches of different resolutions and the inclusion of multiscale information can effectively improve the performance of spectral clustering. In the final stage of the hierarchical scheme, we propose a multilayer foreground estimation process to assemble matting components into a set of matting layers. Unlike conventional approaches, which typically address binary foreground/background partitioning, the proposed method provides a set of multilayer interpretations for unsupervised matting. Experimental results show that the proposed approach can generate more consistent and accurate results as compared with state-of-the-art techniques.
A flexible architecture for feature-based image editing

This paper presents a scheme for feature-based image editing. In the proposed method, an image is decomposed into a skeleton component and a residual component. The skeleton component is obtained by performing iterative linear interpolation based on high-curvature points, which are extracted from the image surface using a Hessian tensor. The residual component is obtained by subtracting the skeleton component from the original image. By manipulating the skeleton component and the residual component, several applications, such as adaptive contrast enhancement, sharpness enhancement, and shape editing, can be achieved. Experimental results are provided to verify the versatility and flexibility of this proposed architecture.
A patch-based contraction process for the improvement of image matting

In this paper, we propose a patch-based contraction process for the improvement of image matting. Given an input image and a trimap, the proposed contraction process quickly refines the trimap by reclassifying the alpha values of the undetermined pixels into “foreground”, “background”, or “undetermined”, based on the feature similarity of the undetermined pixels with respect to the foreground/background regions. The refinement of trimap causes the shrinkage of undetermined regions and may reveal more foreground/background information across isolated regions. Based on the refined trimap, the image matting algorithm can better estimate the alpha values of the image. Experimental results show that the proposed patch-based process provides an effective and efficient way to obtain a refined trimap and the use of the trimap refinement improves the performance of image matting.
Unsupervised hierarchical image segmentation based on Bayesian sequential partitioning

In this paper, we present an unsupervised hierarchical image segmentation algorithm based on a split-and-merge scheme. In the split phase, we propose an efficient partition algorithm, called Just-Noticeable-Difference Bayesian Sequential Partitioning (JND-BSP), to partition image pixels into a few regions, within which the color variations are perceived to be smoothly changing without apparent color differences. In the merge phase, we propose a simple but effective merging criterion to sequentially construct a hierarchical structure that represents the relative similarity among these partitioned regions. Instead of generating a segmentation result with a fixed number of segments, the new algorithm produces an entire hierarchical representation of the given image in a single run. This hierarchical representation is informative and can be very useful for subsequent processing, like object recognition and scene analysis. To demonstrate the effectiveness and efficiency of our method, we compare our new segmentation algorithm with several existing algorithms. Experiment results show that our new algorithm can not only offers a more flexible way to segment images but also provides segmented results close to human's visual perception.
Hierarchical Image Segmentation Based on Iterative Contraction and Merging

In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.
A systolic architecture for elliptic curve cryptosystems

This article presents a new systolic architecture for the main operation in an elliptic curve cryptosystem over the finite field GF(2/sup m/). This proposed architecture is actually a finite field multiplier and is used to implement the addition operation in the elliptic curve cryptosystem. We apply a partitioning scheme and parallelize the main operation in a straightforward systolic architecture to speed up the operation and then apply merging and re-timing schemes in the partitioned architecture to future improve the performance of this architecture. We compare this architecture with some previously proposed systolic architectures for the finite field arithmetic. The comparison shows that our architecture offers the lowest hardware complexity. This architectures can be easily adopted to build a low-complexity elliptic curve cryptosystem.
Semantic segmentation based on iterative contraction and merging

The state-of-the-art models for semantic image segmentation usually contain a convolutional neural network (CNN) and a conditional random field (CRF). As a predictor, existing CNN techniques can generate a dense prediction result but may generate obvious boundary errors at the same time. As a refinement model, CRF improves the CNN outcomes by forcing the consistency of local labels. However, the use of CRF may cause fragmentation effect around object boundaries. In this paper, we propose the use of a so-called iterative contraction and merging (ICM) process to facilitate the semantic segmentation process. Guided by the high-level information from CNN, the ICM process is used as a tool to grow image segments in a bottom-up way and to produce more accurate outcomes in an iterative way. The ICM process can faithfully preserve the boundary information and maintain the consistency of local labels. Our experimental results demonstrate that the performance of the proposed approach is comparable to the state-of-the-art models but with more accurate boundaries.
SSPQ - spatial domain perceptual image codec based on subsampling and perceptual quantization

A spatial domain perceptual image codec based on subsampling and perceptual quantization (SSPQ) guided by the just-noticeable distortion (JND) profile is proposed. SSPQ integrates perceptual image coding and progressive transmission in one framework. The input image is first subsampled by a factor of two in both dimensions and the subsampled image is compressed without loss. The subsampled image provides a basis for both predicting the input pixels by interpolation and estimating the JND values for each pixel. Residual quantization thresholds are set to the estimated JND values for a perceptually tuned compression. Quantized residuals are progressively encoded by a context-based Golomb coder with run-length coding capacity. Experimental results show over 50% improvement in compression performance on average for the proposed SSPQ codec compared to the lossless JPEG-LS.
Distributed Aggregation of Spectrally Efficient Single- and Dual-Polarization Super-Channels by Optical Frequency Conversion in Fiber

Recently developed upgrade options for distributed ultra-dense frequency-division multiplexing toward large aggregation capacity are discussed. The concept, originally demonstrated for distributed coherent optical orthogonal frequency-division multiplexing, is based on optical frequency conversion in fiber. It enables precise frequency allocation of the successively multiplexed subcarriers within the super-channel without the need of absolute optical frequency control, i.e., allowing for use of free-running lasers at the individual multiplexing nodes. Follow-up experiments proved the applicability of this concept also to Nyquist wavelength-division multiplexing (WDM) with extremely small guard bands by successfully demonstrating the distributed aggregation of a spectrally efficient 400-Gb/s single-polarization zero-guard-band Nyquist-WDM super-channel using 4× 28-GBd 16-ary quadrature-amplitude modulation (16QAM) subcarriers. Recently, we further extended the concept to distributed generation of dual-polarization super-channels. This extension allowed for the distributed aggregation of a 400-Gb/s Nyquist-WDM super-channel using 4× 28-GBd polarization-division multiplexed quaternary phase-shift keying (PDM-QPSK) subcarriers. In this invited contribution, we review and compare in detail both experiments enabling 400-Gb/s aggregation capacity. Additionally, we discuss a viable option for operation under random fiber birefringence regardless of the polarization-dependent nature of the underlying optical frequency conversion process.
Impact of Brillouin Backscattering on Signal Distortions in Single-Fiber Diversity Loop Based Polarization-Insensitive FOPAs

We both experimentally and numerically investigate the impact of Brillouin backscattering in single-fiber-loop-based polarization-insensitive fiber-optical parametric amplifiers. We show that the loop configuration can cause distortions in the pump spectra due to Brillouin interactions of the counter-propagating pumps. This can result in significant distortions for amplified data signals at high gain when conventional highly nonlinear fibers are used. We thoroughly investigate the effect in experiments and derive a numerical model. This allows us to compare the results from system experiments with 28 GBd quadrature-phase shift keying signals to system simulations showing a good agreement on the system. Based on the model, we finally estimate that a large gain (∼20 dB) with a low optical signal-to-noise ratio penalty (0.15 dB at bit error rate of $1 \times {10^{ - 4}}$) for the amplified data signal should be possible in a single-fiber loop with Brillouin-suppressed highly nonlinear fibers.
Effective visual masking techniques in JPEG200

This paper introduces a very low complexity visual masking algorithm for the JPEG2000 image compression standard and evaluates its impact on the visual image quality by means of the multi-scale SSIM index. The algorithm derives suitable weighting masks indirectly from a statistical model of the wavelet data which is defined from the second moment and the average absolute amplitude of the data. If combined with an a priori rate allocation algorithm, the computation of the visual masking weights has almost no overhead at all.
Robust Super-Resolution for Mixed-Resolution Multiview Image Plus Depth Data

Increasing spatial resolution and thus improving the image quality is a key issue in the mixed-resolution multiview image and video processing domain. Given adjacent camera perspectives with various spatial resolutions and their corresponding depth information, the high-frequency part of a high-resolution view can be used for increasing the image quality of a neighboring low-resolution camera perspective. However, a reasonable projection of high-frequency information onto the image plane of a neighboring low-resolution view typically requires pixel-wise error-free depth data for the high-resolution reference image. Starting from this, a novel image super-resolution approach is proposed that is robust against both inaccurate depth acquisition and nonperfect calibration of spatially low-resolution depth sensors. The algorithm is based on displacement-compensated high-frequency synthesis and aims at correcting the projection errors introduced by inaccurate depth information. The proposed approach is further effectively extended by a signal extrapolation technique. For a wide range of proper scenarios, the proposed framework achieves substantial objective and visual gains compared with the considered reference approaches. The improvement of quality is shown for both simulated and self-recorded experimental data.
Networked Experiments and Scientific Resource Sharing in Cooperative Knowledge Spaces

Cooperative knowledge spaces create new potentials for the experimental fields in natural sciences and engineering because they enhance the accessibility of experimental setups through virtual laboratories and remote technology, opening them for collaborative and distributed usage. A concept for extending existing virtual knowledge spaces for the means of the technological disciplines (ViCToR-Spaces- Virtual Cooperation in Teaching and Research for Mathematics, Natural Sciences and Engineering) is presented. The integration of networked virtual laboratories and remote experiments (NanoLab Approach), as well as an approach to community-driven content sharing and content development within virtual knowledge spaces (NanoWiki) are described.
Instant Mixed Reality Lighting from Casual Scanning

We present a method for recovering both incident lighting and surface materials from casually scanned geometry. By casual, we mean a rapid and potentially noisy scanning procedure of unmodified and uninstrumented scenes with a commodity RGB-D sensor. In other words, unlike reconstruction procedures which require careful preparations in a laboratory environment, our method works with input that can be obtained by consumer users. To ensure a robust procedure, we segment the reconstructed geometry into surfaces with homogeneous material properties and compute the radiance transfer on these segments. With this input, we solve the inverse rendering problem of factorization into lighting and material properties using an iterative optimization in spherical harmonics form. This allows us to account for self-shadowing and recover specular properties. The resulting data can be used to generate a wide range of mixed reality applications, including the rendering of synthetic objects with matching lighting into a given scene, but also re-rendering the scene (or a part of it) with new lighting. We show the robustness of our approach with real and synthetic examples under a variety of lighting conditions and compare them with ground truth data.
Simulations in undergraduate electrodynamics: Virtual laboratory experiments on the wave equation and their deployment

Experiments play a vital role in undergraduate engineering education: They allow students to learn the foundations of engineering in practical hands-on courses. However, lack of funding and increasing costs for equipment makes it harder and harder to supply a complete pool of experiments for large student classes. The EU funded “Library of Labs” project aims to counterbalance this development by creating a EU wide network of remotely controlled experiments and virtual laboratories. Remote experiments are here real experiments remotely controlled over a network, virtual laboratories simulation environments using the component metaphor of a real laboratories. In this paper, we introduce such a virtual laboratory developed at the University of Stuttgart; the aim here is to help students, here participating in the undergraduate physics course for engineers, understanding abstract phenomena by visualizing the underlying mathematics. We demonstrate this in a particular use-case, the wave equation and phenomena related to it, as they are discussed in undergraduate physics, and show how to implement this as a simulation in the virtual laboratory. In cooperation with the physics department a deployment plan for this experiment and related experiments has been created for the lecture “Physics for Engineering” which shall also be presented and discussed.
Generation and Coherent Reception of 107-GBd Optical Nyquist BPSK, QPSK, and 16QAM

We present a 107-GBd coherent optical system for the generation and reception of m-ary phase-shift keyed (PSK) and quadrature-amplitude modulated (QAM) signals, including Nyquist pulse shaping. It is based on phase-stable optical time-division multiplex (OTDM) of two tributaries, which are modulated at half the target symbol rate, optical Nyquist filtering, and a broadband digital coherent receiver. The system is analyzed in back-to-back experiments with binary PSK (BPSK) modulation, quadrature-PSK (QPSK) modulation, as well as with 16-ary QAM. Compared to all-ETDM implementations, the transmitter is based on independent electro-optical modulation of two time-division multiplex tributaries and subsequent optical interleaving after modulation. Although this scheme implies increased hardware complexity, it enables lower penalties with respect to theory compared to all-ETDM experiments presented so far. This is due to the reduced hardware speed requirements. The measured penalties at bit-error ratios of 1 × 10-3 are 0.5, 1.5, and 6 dB for single-polarization BPSK, QPSK, and 16QAM, respectively. Furthermore, the employed phase-stable optical multiplexer allows use of standard receiver DSP at the broadband digital coherent receiver without need for special treatment of the individual OTDM tributaries.
Kerr Nonlinearity Mitigation: Mid-Link Spectral Inversion Versus Digital Backpropagation in 5×28-GBd PDM 16-QAM Signal Transmission

We experimentally investigate Kerr nonlinearity mitigation of a 28-GBd polarization-multiplexed 16-QAM signal in a five-channel 50-GHz spaced wavelength-division multiplexing (WDM) system. Optical phase conjugation (OPC) employing the mid-link spectral inversion technique is implemented by using a dual-pump polarization-independent fiber-optic parametric amplifier and compared to digital backpropagation (DBP) compensation over up to 800-km in a dispersion-managed link. In the single-channel case, the use of the DBP algorithm outperformed the OPC with a Q-factor improvement of 0.9 dB after 800-km transmission. However, signal transmission was not possible with DBP in the WDM scenario over the same link length while it was enabled by the OPC with a maximum Q-factor of 8.6 dB.
High-Capacity Directly Modulated Optical Transmitter for 2-μm Spectral Region

The 2-μm wave band is emerging as a potential new window for optical telecommunications with several distinct advantages over the traditional 1.55 μm region. First of all, the hollow-core photonic band gap fiber (HC-PBGF) is an emerging transmission fiber candidate with ultra-low nonlinearity and lowest latency (0.3% slower than light propagating in vacuum) that has its minimum loss within the 2-μm wavelength band. Second, the thulium-doped fiber amplifier that operates in this spectral region provides significantly more bandwidth than the erbium-doped fiber amplifier. In this paper, we demonstrate a single-channel 2-μm transmitter capable of delivering >52 Gbit/s data signals, which is twice the capacity previously demonstrated. To achieve this, we employ discrete multitone modulation via direct current modulation of a Fabry-Perot semiconductor laser. The 4.4-GHz modulation bandwidth of the laser is enhanced by optical injection locking, providing up to 11 GHz modulation bandwidth. Transmission over 500-m and 3.8-km samples of HC-PBGF is demonstrated.
Implementing a receiver for terrestrial digital video broadcasting in software on an application-specific DSP

Terrestrial digital video broadcasting (DVB-T) is currently being introduced in many European countries and planned to supplement or replace current analogue broadcasting schemes in a large part of the world. It is also considered as an additional downlink medium for third generation UMTS mobile telephones, where a special variant, DVB-H, is under development. Current DVB-T receivers still are built upon dedicated application specific integrated circuits (ASIC). However, designing ASIC is a tedious and expensive task. We show that it is possible to implement a DVB-T receiver in software on an application-specific digital signal processor (AS-DSP). We analyze the computational requirements of a DVB-T receiver and investigate its potential for parallelization. Further, we present our AS-DSP, the M5-DSP, which is based on a novel architecture and design methodology, and report on implementing the core algorithms of a DVB-T receiver on it.
Rate Allocation in a Two Quantizer Coding System

The issue of backwards compatible image and video coding gained some attention in both MPEG and JPEG, let it be as extension for HEVC, let it be as the JPEG XT standardization initiative of the SC29WG1 committee. The coding systems work all on the principle of a base layer, perating in the low-dynamic range regime, using a one-mapped version of the HDR material as input, and an extension layer invisible to legacy applications. The extension layer allows implementations conforming to the full standard to reconstruct the original image in the high-dynamic range regime. What is also common to all approaches is the rate-allocation problem: How can one split the rate between base and extension layer to ensure optimal coding? In this work, an explicit answer is derived for a simplified model of a two-layer compression system in the high bit-rate approximation. For a HDR to LDR tone mapping that approximates the well-known sRGB non-linearity of gamma = 2.4 and a Laplacian probability density function, explicit results in the form of the Lambert-W-function are derived. The theoretical results are then verified in experiments using a JPEG XT demo implementation.
A Quantitative Analysis of a Virtual Programming Lab

We implemented a survey with one learning group using the web-based tools and a control group working with a traditional setup based on editor and compiler. In a recent publication, we described the design and implementation of a web-based programming lab (ViPLab) targeted at undergraduate Engineering and Mathematics courses. This work provides a quantitative analysis of the user feedback, experience and learning success. The survey shows that web-based installations are as efficient as classical tools, while Windows users prefer the web-based chain over the editor/compiler installation on Linux. This justifies the use of web-based installations in programming beginner courses, if the learning target focuses on programming and not a particular tool chain.