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IS and T International Symposium on Electronic Imaging: Media Watermarking, Security, and Forensics, MWSF 2023

作者： Yadav, Amit Kumar Singh Bartusiak, Emily R. Bhagtani, Kratika Delp, Edward J. Video & Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

The ability to synthesize convincing human speech has become easier due to the availability of speech generation tools. This necessitates the development of forensics methods that can authenticate and attribute speech signals. In this paper, we examine a speech attribution task, which identifies the origin of a speech signal. Our proposed method known as Synthetic Speech Attribution Transformer (SSAT) converts speech signals into mel spectrograms and uses a self-supervised pretrained transformer for attribution. This transformer is pretrained on two large publicly available audio datasets: Audio Set and LibriSpeech. We finetune the pretrained transformer on three speech attribution datasets: the DARPA SemaFor Audio Attribution dataset, the ASVspoof2019 dataset, and the 2022 IEEE SP Cup dataset. SSAT achieves high closed-set accuracy on all datasets (99.8% on ASVspoof2019 dataset, 96.3% on SP Cup dataset, and 93.4% on DARPA SemaFor Audio Attribution dataset). We also investigate the method's ability to generalize to unknown speech generation methods (open-set scenario). SSAT has high performance, achieving an open-set accuracy of 90.2% on the ASVspoof2019 dataset and 88.45% on DARPA SemaFor Audio Attribution dataset. Finally, we show that our approach is robust to typical compression rates used by YouTube for speech signals. © 2023, Society for Imaging Science and Technology.

关键词： Large dataset

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BADF:Bounding Volume Hierarchies Centric Adaptive Distance Field Computation for Deformable Objects on GPUs

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Journal of Computer Science & Technology 2022年第3期37卷 731-740页

作者： Xiao-Rui Chen Min Tang Cheng Li Dinesh Manocha Ruo-Feng Tong Laboratory of Geometry Image and Video Processing Enterprise Intelligent ComputingCollege of Computer Science and TechnologyZhejiang UniversityHangzhou 310007China Geometric Algorithms for Modeling Motionand Animation LaboratoryUniversity of MarylandMaryland 20742U.S.A.

We present a novel algorithm BADF(Bounding Volume Hierarchy Based Adaptive Distance Fields)for accelerating the construction of ADFs(adaptive distance fields)of rigid and deformable models on graphics processing *** approach is based on constructing a bounding volume hierarchy(BVH)and we use that hierarchy to generate an octree-based *** exploit the coherence between successive frames and sort the grid points of the octree to accelerate the *** approach is applicable to rigid and deformable *** GPU-based(graphics processing unit based)algorithm is about 20x--50x faster than current mainstream central processing unit based *** BADF algorithm can construct the distance fields for deformable models with 60k triangles at interactive rates on an NVIDIA GTX GeForce ***,we observe 3x speedup over prior GPU-based ADF algorithms.

关键词： distance field deformable object graphics processing unit(GPU) octree bounding volume hierarchy

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Learning to Reconnect Interrupted Trajectories for Weakly Supervised Multi-Object Tracking 48

Learning to Reconnect Interrupted Trajectories for Weakly Su...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Yu-Lei Lu, Yang Li, Jie Wang, Hanzi Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics China Xidian University Video and Image Processing System Laboratory School of Electronic Engineering China

ISBN: (纸本)9781728163277

Recently, some weakly supervised multi-object tracking (MOT) methods learn identity embedding features with pseudo identity labels rather than the high-cost manual ones. However, these pseudo identity labels may contain many false or missing identities, which adversely affect the optimization of tracking networks, resulting in interrupted trajectories of occluded targets. To effectively reconnect the interrupted trajectories caused by noisy pseudo labels, we propose a novel weakly supervised MOT method based on a Trajectory-Reconnecting Transformer (TRTMOT). TRT-MOT performs feature decoupling to extract discriminative embedding features for reconnecting trajectories of occluded targets. Experimental results show that TRTMOT outperforms previous weakly supervised MOT methods by at least +3.6 and +5.6 on MOTA for the MOT17 and MOT20 datasets, respectively. © 2023 IEEE.

关键词： Feature Decoupling Pseudo Identity Labels Trajectory-Reconnecting Transformer Weakly Supervised Multi-Object Tracking

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Illumination Correction for Unsupervised Person Re-Identification

Illumination Correction for Unsupervised Person Re-Identific...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Jiaqi Guo Amy R. Reibman Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Unsupervised person re-identification (re-ID) aims to learn identity information from a source domain (e.g. one surveillance system) and apply it to a target domain (e.g. a different surveillance system). This is challenging due to occlusion, viewpoint, and illumination variations between the different domains (i.e. systems). In this paper, we propose a neural network architecture, known as Synthetic Model Bank (SMB), to address illumination variation in unsupervised person re-ID. The basic idea of SMB is to use synthetic data for training different re-ID models for different illumination conditions. From our experiments, the proposed SMB outperforms other synthetic augmentation methods on several re-ID benchmarks.

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Uli-Ri: A Benchmark for Person Re-Identification With Quantitative Annotations

Uli-Ri: A Benchmark for Person Re-Identification With Quanti...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Jiaqi Guo Amy R. Reibman Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Person re-identification (re-ID) has wide applications in surveillance and security. It is also challenging due to viewpoint, occlusion and illumination variations across different cameras. One solution to unsupervised person re-ID problems is synthetic data augmentation. Generative neural networks have been used to translate images from the source domain into the target domain. In this paper, we introduce a new virtual-human image dataset that can be used as the source domain for person re-ID. This new dataset has images labeled by person identity, background, viewpoint and illumination intensity. We also explore GAN-based and Diffusion-based generative methods for unpaired image-to-image translation and provide qualitative and quantitative evaluation for the synthetic results.

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Distributed and networked analysis of volumetric image data for remote collaboration of microscopy image analysis

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Journal of Medical Imaging 2025年第2期12卷 024001页

作者： Chen, Alain Han, Shuo Lee, Soonam Fu, Chichen Yang, Changye Wu, Liming Winfree, Seth Dunn, Kenneth W. Salama, Paul Delp, Edward J. Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory West LafayetteIN United States National Institute of Allergy and Infectious Diseases Rocky Mountain Laboratories HamiltonMT United States Indiana University School of Medicine IndianapolisIN United States Purdue University at Indianapolis School of Electrical and Computer Engineering IndianapolisIN United States

Purpose: The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. Approach: We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. Results: DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. Conclusions: The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Photointerpretation

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Rotation Adaptive Plot Extraction from UAV RGB images

Rotation Adaptive Plot Extraction from UAV RGB Images

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiaqi Guo Changye Yang Enyu Cai Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana

Unmanned Aerial Vehicles (UAVs) have the ability to acquire high resolution RGB images from plant fields. A field used for plant research is usually divided into smaller groups of plants known as "plots" to evaluate varieties or management practices. In this paper, we propose an optimization-based, rotation-adaptive approach for extracting plots in a UAV RGB orthomosaic image. From the experiments, our proposed method is robust against range/plot rotation and achieves higher segmentation accuracy compared with existing plot extraction approaches.

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Semi-Supervised Object Detection for Sorghum Panicles in UAV imagery

Semi-Supervised Object Detection for Sorghum Panicles in UAV...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Enyu Cai Jiaqi Guo Changye Yang Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10% of original training data.

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Learning to Reconnect Interrupted Trajectories for Weakly Supervised Multi-Object Tracking

Learning to Reconnect Interrupted Trajectories for Weakly Su...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu-Lei Li Yang Lu Jie Li Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University China

关键词： Technological innovation Target tracking Manuals Signal processing Feature extraction Transformers Trajectory

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Development of a method for manufacturing a high-quality diffractive optical slit for hyperspectral equipment 7

Development of a method for manufacturing a high-quality dif...

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7th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2021

作者： Fomchenkov, Sergey Institute of Image Processing Systems RAS Branch of the Federal Research Center 'Crystallography and Photonics' RAS Sergey Fomchenkov Video Data Intelligence Analysis Laboratory Samara Russia

ISBN: (纸本)9781665432177

In this work, a method was proposed that makes it possible to quickly and cheaply produce diffraction slits with the required parameters. The production parameters were experimentally investigated and optimized. The advantage of the proposed method is the use of a small number of technological manufacturing steps and, as a consequence, a cheaper process and a decrease in production time. This ensures high quality of the finished optical element due to the use of direct laser recording by focused laser radiation and the absence of direct mechanical action on the substrate surface. © 2021 IEEE.

关键词： Diffraction

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