pwc-archive/papers-with-abstracts huggingface dataset:
An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings
Authors: Georgios Karantaidis, Constantine Kotropoulos
Date: 2021-10-02 00:00:00
Proceeding: Journal of Imaging 2021 10
Tasks: ENF (Electric Network Frequency) Extraction, ENF (Electric Network Frequency) Extraction from Video, Superpixels

Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.
State and Input Constrained Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems
Authors: Tochukwu Elijah Ogri, Muzaffar Qureshi, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2024-06-27 00:00:00
Tasks: Model-based Reinforcement Learning, State Estimation

In this paper, a novel online, output-feedback, critic-only, model-based reinforcement learning framework is developed for safety-critical control systems operating in complex environments. The developed framework ensures system stability and safety, regardless of the lack of full-state measurement, while learning and implementing an optimal controller. The approach leverages linear matrix inequality-based observer design method to efficiently search for observer gains for effective state estimation. Then, approximate dynamic programming is used to develop an approximate controller that uses simulated experiences to guarantee the safety and stability of the closed-loop system. Safety is enforced by adding a recentered robust Lyapunov-like barrier function to the cost function that effectively enforces safety constraints, even in the presence of uncertainty in the state. Lyapunov-based stability analysis is used to guarantee uniform ultimate boundedness of the trajectories of the closed-loop system and ensure safety. Simulation studies are performed to demonstrate the effectiveness of the developed method through two real-world safety-critical scenarios, ensuring that the state trajectories of a given system remain in a given set and obstacle avoidance.
The use of Electric Network Frequency presence in video material for time estimation
Authors: Guus Frijters, Zeno J. M. H. Geradts
Date: 2022-02-24 00:00:00
Proceeding: Journal of forensic sciences 2022 2
Tasks: ENF (Electric Network Frequency) Extraction, ENF (Electric Network Frequency) Extraction from Video

In this research, the possibility of estimating the time a video was recorded at through electric network frequency is explored by examining various light sources in differentiating circumstances. This research focuses on videos made with smartphones. The smartphone cameras make use of an integrated complementary metal oxide semiconductor sensor. The filmed videos are analyzed using software, which employs a small electric network frequency (ENF) database to determine the time of recording of a video made in experimental circumstances. This research shows that in ideal circumstances, it is possible to determine the time stamp of a video recording made with a smartphone. However, it becomes clear that different light sources greatly influence the outcome. The best results are achieved with Halogen and Incandescent light sources, both of which also seem promising in less ideal circumstances. LED sources do work in ideal circumstances and, however, do not show much success in lesser circumstances. This research further demonstrates that there is potential in using ENF to determine a time stamp of recorded videos and provides validation on prior research on this topic. It proves usable in ideal circumstances with the presence of a clear light source on a white wall. With additional research, it has potential to become a feasible method to use for forensic settings in circumstances that are less ideal.
Electric Network Frequency Detection Using Least Absolute Deviations
Authors: Christos Korgialas, Constantine Kotropoulos
Date: 2023-05-05 00:00:00
Proceeding: ICASSP 2023 5
Tasks: ENF (Electric Network Frequency) Detection

Electric Network Frequency (ENF) is a fingerprint in multi-media forensics applications. ENF is a weak signal that is difficult to be detected. This difficulty stems from the existence of colored wide-sense stationary Gaussian noise in ENF as well as due to many unknown random parameters. However, several ENF detectors have been proposed, motivating the related research. In this paper, a novel Least Absolute Deviations-based ENF detector is proposed that is coined as LAD-Likelihood Ratio Test (LAD-LRT). The performance of the LAD-LRT detector is thoroughly analyzed concerning test statistic distribution and threshold selection. The aim is to develop a detector that detects ENF more accurately in short-length recordings than the state-of-the-art Least-Squares (LS)-LRT and naive-LRT detectors. Thorough evaluation using benchmark audio recordings demonstrate the effectiveness of the proposed detector.
Anomaly detection with a variational autoencoder for Arabic mispronunciation detection
Authors: Meriem Lounis, Bilal Dendani, Halima Bahi
Date: 2024-06-25 00:00:00
Proceeding: International Journal of Speech Technology 2024 6
Tasks: Anomaly Detection

Computer-assisted language learning (CALL) systems increasingly arouse a significant interest and establish a presence
in automated foreign language learning. They enhance traditional learning methods by providing access to various accents
and spoken language styles through websites, mobile applications, and social media. Herein, mispronunciation detection
is a key component mainly addressed as a classification problem. Meanwhile, deep learning (DL) advances have promoted
these systems by training deep neural networks (DNN) to classify a pronunciation as correct or incorrect. However, the
effectiveness of the DL models is hindered by many shortcomings, such as the scarcity of labeled data. To address this issue,
the paper assumes an anomaly detection-based mispronunciation detection approach. It utilizes a variational autoencoder
(VAE) relying on a density-based method to model the “normal data.” The VAE is a generative model trained in a self supervised
way to learn the distribution of the correct pronunciations, standing for “normal data,” and is expected to detect
mispronunciations, standing for “abnormal data” during the test stage. Our proposition was evaluated in the context of Arabic
pronunciation learning through the ASMDD Arabic dataset. The obtained results are promising, with an accuracy of about
98%. The proposed VAE outperformed the standard autoencoder as well as the state-of-the-art convolution neural networks
used for Arabic mispronunciation detection.
Research on Image Processing and Vectorization Storage Based on Garage Electronic Maps
Authors: Nan Dou, Qi Shi, Zhigang Lian
Date: 2024-06-02 00:00:00

For the purpose of achieving a more precise definition and data analysis of images, this study conducted a research on vectorization and rasterization storage of electronic maps, focusing on a large underground parking garage map. During the research, image processing, vectorization and rasterization storage were performed. The paper proposed a method for the vectorization classification storage of indoor two-dimensional map raster data. This method involves converting raster data into vector data and classifying elements such as parking spaces, pathways, and obstacles based on their coordinate positions with the grid indexing method, thereby facilitating efficient storage and rapid querying of indoor maps. Additionally, interpolation algorithms were employed to extract vector data and convert it into raster data. Navigation testing was conducted to validate the accuracy and reliability of the map model under this method, providing effective technical support for the digital storage and navigation of garage maps.
Towards Deep Learning for Predicting Microbial Fuel Cell Energy Output
Authors: Adam Hess-Dunlop, Harshitha Kakani, Colleen Josephson
Date: 2024-06-17 00:00:00
Tasks: Deep Learning, quantile regression

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.
An Initial Study of Human-Scale Blockage in sub-THz Radio Propagation with Application to Indoor Passive Localization
Authors: F. Paonessa, G. Virone, S. Kianoush, A. Nordio, S. Savazzi
Date: 2024-05-15 00:00:00

This paper empirically investigates the body induced electromagnetic (EM) effects, namely the human body blockage, by conducting indoor measurement campaigns in the unexplored sub-THz W-band (75-110 GHz) and G-band (170-260 GHz). The proposed analysis focuses on both the alterations of channel frequency response induced by body presence, fully or partially obstructing the line-of-sight (LoS) between transmitter and recevier, as well as on the channel impulse response (CIR) for selected movements of the target, i.e. crossing the LoS of the radio link. Modelling of large scale parameters is also presented using a phantom body object. The proposed study has applications in device-free radio localization and radio frequency (RF) sensing scenarios where the EM radiation or environmental radio signals are collected and processed to detect and locate people without requiring them to wear any electronic devices. Although preliminary, the study reveals that discrimination of the blockage micro-movements is possible, achieving higher precision compared to classical RF sensing and localization using cm-scale wavelengths (2.4-6GHz bands).
RACon: Retrieval-Augmented Simulated Character Locomotion Control
Authors: Yuxuan Mu, Shihao Zou, Kangning Yin, Zheng Tian, Li Cheng, Weinan Zhang, Jun Wang
Date: 2024-06-11 00:00:00
Tasks: Hierarchical Reinforcement Learning, Retrieval

In computer animation, driving a simulated character with lifelike motion is challenging. Current generative models, though able to generalize to diverse motions, often pose challenges to the responsiveness of end-user control. To address these issues, we introduce RACon: Retrieval-Augmented Simulated Character Locomotion Control. Our end-to-end hierarchical reinforcement learning method utilizes a retriever and a motion controller. The retriever searches motion experts from a user-specified database in a task-oriented fashion, which boosts the responsiveness to the user's control. The selected motion experts and the manipulation signal are then transferred to the controller to drive the simulated character. In addition, a retrieval-augmented discriminator is designed to stabilize the training process. Our method surpasses existing techniques in both quality and quantity in locomotion control, as demonstrated in our empirical study. Moreover, by switching extensive databases for retrieval, it can adapt to distinctive motion types at run time.
Neural Appearance Modeling From Single Images
Authors: Jay Idema, Pieter Peers
Date: 2024-06-08 00:00:00

We propose a material appearance modeling neural network for visualizing plausible, spatially-varying materials under diverse view and lighting conditions, utilizing only a single photograph of a material under co-located light and view as input for appearance estimation. Our neural architecture is composed of two network stages: a network that infers learned per-pixel neural parameters of a material from a single input photograph, and a network that renders the material utilizing these neural parameters, similar to a BRDF. We train our model on a set of 312,165 synthetic spatially-varying exemplars. Since our method infers learned neural parameters rather than analytical BRDF parameters, our method is capable of encoding anisotropic and global illumination (inter-pixel interaction) information into individual pixel parameters. We demonstrate our model's performance compared to prior work and demonstrate the feasibility of the render network as a BRDF by implementing it into the Mitsuba3 rendering engine. Finally, we briefly discuss the capability of neural parameters to encode global illumination information.
CanFields: Consolidating Diffeomorphic Flows for Non-Rigid 4D Interpolation from Arbitrary-Length Sequences
Authors: Miaowei Wang, Changjian Li, Amir Vaxman
Date: 2024-06-05 00:00:00

We introduce Canonical Consolidation Fields (CanFields). This novel method interpolates arbitrary-length sequences of independently sampled 3D point clouds into a unified, continuous, and coherent deforming shape. Unlike prior methods that oversmooth geometry or produce topological and geometric artifacts, CanFields optimizes fine-detailed geometry and deformation jointly in an unsupervised fitting with two novel bespoke modules. First, we introduce a dynamic consolidator module that adjusts the input and assigns confidence scores, balancing the optimization of the canonical shape and its motion. Second, we represent the motion as a diffeomorphic flow parameterized by a smooth velocity field. We have validated our robustness and accuracy on more than 50 diverse sequences, demonstrating its superior performance even with missing regions, noisy raw scans, and sparse data. Our project page is at: https://wangmiaowei.github.io/CanFields.github.io/.
Dream-in-Style: Text-to-3D Generation Using Stylized Score Distillation
Authors: Hubert Kompanowski, Binh-Son Hua
Date: 2024-06-05 00:00:00
Tasks: 3D Generation, Style Transfer, Text to 3D

We present a method to generate 3D objects in styles. Our method takes a text prompt and a style reference image as input and reconstructs a neural radiance field to synthesize a 3D model with the content aligning with the text prompt and the style following the reference image. To simultaneously generate the 3D object and perform style transfer in one go, we propose a stylized score distillation loss to guide a text-to-3D optimization process to output visually plausible geometry and appearance. Our stylized score distillation is based on a combination of an original pretrained text-to-image model and its modified sibling with the key and value features of self-attention layers manipulated to inject styles from the reference image. Comparisons with state-of-the-art methods demonstrated the strong visual performance of our method, further supported by the quantitative results from our user study.
Generating grid maps via the snake model
Authors: Zhiwei Wei, Nai Yang, Wenjia Xu, Su Ding
Date: 2024-06-04 00:00:00
Tasks: model

The grid map, often referred to as the tile map, stands as a vital tool in geospatial visualization, possessing unique attributes that differentiate it from more commonly known techniques such as choropleths and cartograms. It transforms geographic regions into grids, which requires the displacement of both region centroids and boundary nodes to establish a coherent grid arrangement. However, existing approaches typically displace region centroids and boundary nodes separately, potentially resulting in self-intersected boundaries and compromised relative orientation relations between regions. In this paper, we introduce a novel approach that leverages the Snake displacement algorithm from cartographic generalization to concurrently displace region centroids and boundary nodes. The revised Constrained Delaunay triangulation (CDT) is employed to represent the relations between regions and serves as a structural foundation for the Snake algorithm. Forces for displacing the region centroids into a grid-like pattern are then computed. These forces are iteratively applied within the Snake model until a satisfactory new boundary is achieved. Subsequently, the grid map is created by aligning the grids with the newly generated boundary, utilizing a one-to-one match algorithm to assign each region to a specific grid. Experimental results demonstrate that the proposed approach excels in maintaining the relative orientation and global shape of regions, albeit with a potential increase in local location deviations. We also present two strategies aligned with existing approaches to generate diverse grid maps for user preferences. Further details and resources are available on our project website: https://github.com/TrentonWei/DorlingMap.git.
GFFE: G-buffer Free Frame Extrapolation for Low-latency Real-time Rendering
Authors: Songyin Wu, Deepak Vembar, Anton Sochenov, Selvakumar Panneer, Sungye Kim, Anton Kaplanyan, Ling-Qi Yan
Date: 2024-05-23 00:00:00
Tasks: Efficient Neural Network

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future frames based on previous frames. However, it is a more challenging task because of the lack of information in the disocclusion regions, and recent methods also have a high engine integration cost due to requiring G-buffers as input. We propose a \emph{G-buffer free} frame extrapolation, GFFE, with a novel heuristic framework and an efficient neural network, to plausibly generate new frames in real-time without introducing additional latency. We analyze the motion of dynamic fragments and different types of disocclusions, and design the corresponding modules of the extrapolation block to handle them. After filling disocclusions, a light-weight shading correction network is used to correct shading and improve overall quality. GFFE achieves comparable or better results compared to previous interpolation as well as G-buffer-dependent extrapolation methods, with more efficient performance and easier game integration.
GS-ROR$^2$: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction
Authors: Zuo-Liang Zhu, Beibei Wang, Jian Yang
Date: 2024-05-22 00:00:00
Tasks: 3DGS, Inverse Rendering, NeRF, Novel View Synthesis

3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient rendering speed. Unfortunately, creating relightable 3D assets and reconstructing faithful geometry with 3DGS is still problematic, particularly for reflective objects, as its discontinuous representation raises difficulties in constraining geometries. Volumetric signed distance field (SDF) methods provide robust geometry reconstruction, while the expensive ray marching hinders its real-time application and slows the training. Besides, these methods struggle to capture sharp geometric details. To this end, we propose to guide 3DGS and SDF bidirectionally in a complementary manner, including an SDF-aided Gaussian splatting for efficient optimization of the relighting model and a GS-guided SDF enhancement for high-quality geometry reconstruction. At the core of our SDF-aided Gaussian splatting is the mutual supervision of the depth and normal between blended Gaussians and SDF, which avoids the expensive volume rendering of SDF. Thanks to this mutual supervision, the learned blended Gaussians are well-constrained with a minimal time cost. As the Gaussians are rendered in a deferred shading mode, the alpha-blended Gaussians are smooth, while individual Gaussians may still be outliers, yielding floater artifacts. Therefore, we introduce an SDF-aware pruning strategy to remove Gaussian outliers located distant from the surface defined by SDF, avoiding floater issue. This way, our GS framework provides reasonable normal and achieves realistic relighting, while the mesh from depth is still problematic. Therefore, we design a GS-guided SDF refinement, which utilizes the blended normal from Gaussians to finetune SDF. With this enhancement, our method can further provide high-quality meshes for reflective objects at the cost of 17% extra training time.
Refining 3D Point Cloud Normal Estimation via Sample Selection
Authors: Jun Zhou, Yaoshun Li, Hongchen Tan, Mingjie Wang, Nannan Li, Xiuping Liu
Date: 2024-05-20 00:00:00

In recent years, point cloud normal estimation, as a classical and foundational algorithm, has garnered extensive attention in the field of 3D geometric processing. Despite the remarkable performance achieved by current Neural Network-based methods, their robustness is still influenced by the quality of training data and the models' performance. In this study, we designed a fundamental framework for normal estimation, enhancing existing model through the incorporation of global information and various constraint mechanisms. Additionally, we employed a confidence-based strategy to select the reasonable samples for fair and robust network training. The introduced sample confidence can be integrated into the loss function to balance the influence of different samples on model training. Finally, we utilized existing orientation methods to correct estimated non-oriented normals, achieving state-of-the-art performance in both oriented and non-oriented tasks. Extensive experimental results demonstrate that our method works well on the widely used benchmarks.